In this paper, the artificial neural network (ANN) has been utilized for rotating machinery faults detection and classification. First, experiments were performed to measure the lateral vibration signals of laboratory test rigs for rotor-disk-blade when the blades are defective. A rotor-disk-blade system with 6 regular blades and 5 blades with various defects was constructed. Second, the ANN was applied to classify the different x- and y-axis lateral vibrations due to different blade faults. The results based on training and testing with different data samples of the fault types indicate that the ANN is robust and can effectively identify and distinguish different blade faults caused by lateral vibrations in a rotor. As compared to the literature, the present paper presents a novel work of identifying and classifying various rotating blade faults commonly encountered in rotating machines using ANN. Experimental data of lateral vibrations of the rotor-disk-blade system in both x- and y-directions are used for the training and testing of the network.
This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.
Among the most widespread renewable energy sources is solar energy; Solar panels offer a green, clean, and environmentally friendly source of energy. In the presence of several advantages of the use of photovoltaic systems, the random operation of the photovoltaic generator presents a great challenge, in the presence of a critical load. Among the most used solutions to overcome this problem is the combination of solar panels with generators or with the public grid or both. In this paper, an energy management strategy is proposed with a safety aspect by using artificial neural networks (ANNs), in order to ensure a continuous supply of electricity to consumers with a maximum solicitation of renewable energy.
The significance of the solar energy is to intensify the effectiveness of the Solar Panel with the use of a primordial solar tracking system. Here we propounded a solar positioning system with the use of the global positioning system (GPS) , artificial neural network (ANN) and image processing (IP) . The azimuth angle of the sun is evaluated using GPS which provide latitude, date, longitude and time. The image processing used to find sun image through which centroid of sun is calculated and finally by comparing the centroid of sun with GPS quadrate to achieve optimum tracking point. Weather conditions and situation observed through AI decision making with the help of IP algorithms. The presented advance adaptation is analyzed and established via experimental effects which might be made available on the memory of the cloud carrier for systematization. The proposed system improve power gain by 59.21% and 10.32% compare to stable system (SS) and two-axis solar following system (TASF) respectively. The reduced tracking error of IoT based Two-axis solar following system (IoT-TASF) reduces their azimuth angle error by 0.20 degree.
The aim of this research is the speed tracking of the permanent magnet synchronous motor (PMSM) using an intelligent Neural-Network based adapative backstepping control. First, the model of PMSM in the Park synchronous frame is derived. Then, the PMSM speed regulation is investigated using the classical method utilizing the field oriented control theory. Thereafter, a robust nonlinear controller employing an adaptive backstepping strategy is investigated in order to achieve a good performance tracking objective under motor parameters changing and external load torque application. In the final step, a neural network estimator is integrated with the adaptive controller to estimate the motor parameters values and the load disturbance value for enhancing the effectiveness of the adaptive backstepping controller. The robsutness of the presented control algorithm is demonstrated using simulation tests. The obtained results clearly demonstrate that the presented NN-adaptive control algorithm can provide good trackingperformances for the speed trackingin the presence of motor parameter variation and load application.
Impact analysis of actuator torque degradation on the IRB 120 robot performan...IJECEIAES
Actuators in a robot system may become faulty during their life cycle. Locked joints, free-moving joints, and the loss of actuator torque are common faulty types of robot joints where the actuators fail. Locked and free-moving joint issues are addressed by many published articles, whereas the actuator torque loss still opens attractive investigation challenges. The objectives of this study are to classify the loss of robot actuator torque, named actuator torque degradation, into three different cases: Boundary degradation of torque, boundary degradation of torque rate, and proportional degradation of torque, and to analyze their impact on the performance of a typical 6-DOF robot (i.e., the IRB 120 robot). Typically, controllers of robots are not pre-designed specifically for anticipating these faults. To isolate and focus on the impact of only actuator torque degradation faults, all robot parameters are assumed to be known precisely, and a popular closed-loop controller is used to investigate the robot’s responses under these faults. By exploiting MATLAB-the reliable simulation environment, a simscape-based quasi-physical model of the robot is built and utilized instead of an actual expensive prototype. The simulation results indicate that the robot responses cannot follow the desired path properly in most fault cases.
The development of modeling wind speed plays a very important in helping to obtain the actual wind speed data for the benefit of the power plant planning in the future. The wind speed in this paper is obtained from a PCE-FWS 20 type measuring instrument with a duration of 30 minutes which is accumulated into monthly data for one year (2019). Despite the many wind speed modeling that has been done by researchers. Modeling wind speeds proposed in this study were obtained from the modified Rayleigh distribution. In this study, the Rayleigh scale factor (Cr) and modified Rayleigh scale factor (Cm) were calculated. The observed wind speed is compared with the predicted wind characteristics. The data fit test used correlation coefficient (R2), root means square error (RMSE), and mean absolute percentage error (MAPE). The results of the proposed modified Rayleigh model provide very good results for users.
This paper presents simulation and experimental results of anti-windup PI controller to improve induction machine speed control based on direct torque control (DTC) strategy. Problems like rollover can arise in conventional PI controller due to saturation effect. In order to avoid such problems anti-windup PI controller is presented. This controller is simple for implementation in practice. The proposed anti-windup PI controller demonstrates better dynamic step changes response in speed in terms of overshoots. All simulation work was done using Simulink in the MATLAB software. The experimental results were obtained by practical implementation on a dSPACE 1104 board for a 1.5 KW induction machine. Simulation and experimental results have proven a good performance and verified the validity of the presented control strategy.
This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.
Among the most widespread renewable energy sources is solar energy; Solar panels offer a green, clean, and environmentally friendly source of energy. In the presence of several advantages of the use of photovoltaic systems, the random operation of the photovoltaic generator presents a great challenge, in the presence of a critical load. Among the most used solutions to overcome this problem is the combination of solar panels with generators or with the public grid or both. In this paper, an energy management strategy is proposed with a safety aspect by using artificial neural networks (ANNs), in order to ensure a continuous supply of electricity to consumers with a maximum solicitation of renewable energy.
The significance of the solar energy is to intensify the effectiveness of the Solar Panel with the use of a primordial solar tracking system. Here we propounded a solar positioning system with the use of the global positioning system (GPS) , artificial neural network (ANN) and image processing (IP) . The azimuth angle of the sun is evaluated using GPS which provide latitude, date, longitude and time. The image processing used to find sun image through which centroid of sun is calculated and finally by comparing the centroid of sun with GPS quadrate to achieve optimum tracking point. Weather conditions and situation observed through AI decision making with the help of IP algorithms. The presented advance adaptation is analyzed and established via experimental effects which might be made available on the memory of the cloud carrier for systematization. The proposed system improve power gain by 59.21% and 10.32% compare to stable system (SS) and two-axis solar following system (TASF) respectively. The reduced tracking error of IoT based Two-axis solar following system (IoT-TASF) reduces their azimuth angle error by 0.20 degree.
The aim of this research is the speed tracking of the permanent magnet synchronous motor (PMSM) using an intelligent Neural-Network based adapative backstepping control. First, the model of PMSM in the Park synchronous frame is derived. Then, the PMSM speed regulation is investigated using the classical method utilizing the field oriented control theory. Thereafter, a robust nonlinear controller employing an adaptive backstepping strategy is investigated in order to achieve a good performance tracking objective under motor parameters changing and external load torque application. In the final step, a neural network estimator is integrated with the adaptive controller to estimate the motor parameters values and the load disturbance value for enhancing the effectiveness of the adaptive backstepping controller. The robsutness of the presented control algorithm is demonstrated using simulation tests. The obtained results clearly demonstrate that the presented NN-adaptive control algorithm can provide good trackingperformances for the speed trackingin the presence of motor parameter variation and load application.
Impact analysis of actuator torque degradation on the IRB 120 robot performan...IJECEIAES
Actuators in a robot system may become faulty during their life cycle. Locked joints, free-moving joints, and the loss of actuator torque are common faulty types of robot joints where the actuators fail. Locked and free-moving joint issues are addressed by many published articles, whereas the actuator torque loss still opens attractive investigation challenges. The objectives of this study are to classify the loss of robot actuator torque, named actuator torque degradation, into three different cases: Boundary degradation of torque, boundary degradation of torque rate, and proportional degradation of torque, and to analyze their impact on the performance of a typical 6-DOF robot (i.e., the IRB 120 robot). Typically, controllers of robots are not pre-designed specifically for anticipating these faults. To isolate and focus on the impact of only actuator torque degradation faults, all robot parameters are assumed to be known precisely, and a popular closed-loop controller is used to investigate the robot’s responses under these faults. By exploiting MATLAB-the reliable simulation environment, a simscape-based quasi-physical model of the robot is built and utilized instead of an actual expensive prototype. The simulation results indicate that the robot responses cannot follow the desired path properly in most fault cases.
The development of modeling wind speed plays a very important in helping to obtain the actual wind speed data for the benefit of the power plant planning in the future. The wind speed in this paper is obtained from a PCE-FWS 20 type measuring instrument with a duration of 30 minutes which is accumulated into monthly data for one year (2019). Despite the many wind speed modeling that has been done by researchers. Modeling wind speeds proposed in this study were obtained from the modified Rayleigh distribution. In this study, the Rayleigh scale factor (Cr) and modified Rayleigh scale factor (Cm) were calculated. The observed wind speed is compared with the predicted wind characteristics. The data fit test used correlation coefficient (R2), root means square error (RMSE), and mean absolute percentage error (MAPE). The results of the proposed modified Rayleigh model provide very good results for users.
This paper presents simulation and experimental results of anti-windup PI controller to improve induction machine speed control based on direct torque control (DTC) strategy. Problems like rollover can arise in conventional PI controller due to saturation effect. In order to avoid such problems anti-windup PI controller is presented. This controller is simple for implementation in practice. The proposed anti-windup PI controller demonstrates better dynamic step changes response in speed in terms of overshoots. All simulation work was done using Simulink in the MATLAB software. The experimental results were obtained by practical implementation on a dSPACE 1104 board for a 1.5 KW induction machine. Simulation and experimental results have proven a good performance and verified the validity of the presented control strategy.
Disturbance observer-based controller for inverted pendulum with uncertaintie...IJECEIAES
A new approach based on linear matrix inequality (LMI) technique for stabilizing the inverted pendulum is developed in this article. The unknown states are estimated as well as the system is stabilized simultaneously by employing the observer-based controller. In addition, the impacts of the uncertainties are taken into consideration in this paper. Unlike the previous studies, the uncertainties in this study are unnecessary to satisfy the bounded constraints. These uncertainties will be converted into the unknown input disturbances, and then a disturbance observer-based controller will be synthesized to estimate the information of the unknown states, eliminate completely the effects of the uncertainties, and stabilize inverted pendulum system. With the support of lyapunov methodology, the conditions for constructing the observer and controller under the framework of linear matrix inequalities (LMIs) are derived in main theorems. Finally, the simulations for system with and without uncertainties are exhibited to show the merit and effectiveness of the proposed methods.
Adaptive maximum power point tracking using neural networks for a photovoltai...Mellah Hacene
Adaptive Maximum Power Point Tracking Using Neural Networks for a Photovoltaic Systems According Grid
Electrical Engineering & Electromechanics, (5), 57–66, 2021. https://doi.org/10.20998/2074-272X.2021.5.08
Frequency regulation service of multiple-areas vehicle to grid application in...IJECEIAES
Regarding a potential of electric vehicles, it has been widely discussed that the electric vehicle can be participated in electricity ancillary services. Among the ancillary service products, the system frequency regulation is often considered. However, the participation in this service has to be conformed to the hierarchical frequency control architecture. Therefore, the vehicle to grid (V2G) application in this article is proposed in the term of multiple-areas of operation. The multiple-areas in this article are concerned as parking areas, which the parking areas can be implied as a V2G operator. From that, V2G operator can obtain the control signal from hierarchical control architecture for power sharing purpose. A power sharing concept between areas is fulfilled by a proposed adaptive droop factor based on battery state of charge and available capacity of parking area. A nonlinear multiplier factor is used for the droop adaptation. An available capacity is also applied as a limitation for the V2G operation. The available capacity is analyzed through a stochastic character. As the V2G application has to be cooperated with the hierarchical control functions, i.e. primary control and secondary control, then the effect of V2G on hierarchical control functions is investigated and discussed.
This paper presents an online efficiency optimization method for the interior permanent magnet synchronous motor (IPMSM) drive system in an electric vehicle (EV). The proposed method considers accurately the total system losses including fundamental copper and iron losses, harmonic copper and iron losses, magnet loss, and inverter losses. Therefore, it has the capability to always guarantee maximum efficiency control. A highly trusted machine model is built using finite element analysis (FEA). This model considers accurately the magnetic saturation, spatial harmonics, and iron loss effect. The overall system efficiency is estimated online based on the accurate determination of system loss, and then the optimum current angle is defined online for the maximum efficiency per ampere (MEPA) control. A series of results is conducted to show the effectiveness and fidelity of proposed method. The results show the superior performance of proposed method over the conventional offline efficiency optimization methods.
A New Under-Frequency Load Shedding Method Using the Voltage Electrical Dista...IJAEMSJORNAL
This paper proposes a method for determining location to shed the load in order to recover the frequency back to the allowable range. Prioritize distribution of the load shedding at load bus positions based on the voltage electrical distance between the outage generator and the loads. The nearer the load bus from the outage generator is, the sooner the load bus will shed and vice versa. Finally, by selecting the rate of change of generation active power, rate of change of active power of load, rate of change of frequency, rate of change of branches active power and rate of change of voltage in the system as the input to an Artificial Neural Network, the generators outage, the load shedding bus are determined in a short period of time to maintain the stability of the system. With this technique, a large amount of load shedding could be avoided, hence, saved from economic losses. The effectiveness of the proposed method tested on the IEEE 39 Bus New England has demonstrated the effectiveness of this method.
Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural NetworksIJECEIAES
In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.
AN EFFICIENT ALGORITHM FOR WRAPPER AND TAM CO-OPTIMIZATION TO REDUCE TEST APP...IAEME Publication
System-on-Chip (SOC) designs composed of many embedded cores are ubiquitous in today’s integrated circuits. Each of these cores requires to be tested separately after manufacturing of the SoC. That’s why, modular testing is adopted for core-based SoCs, as it promotes test reuse and permits the cores to be tested without comprehensive knowledge about their internal structural details. Such modular testing triggers the need of a special test access mechanism (TAM) to build communication between core I/Os and TAM and promises to minimize overall test time. In this paper, various issues are analyzed to optimize the Wrapper and TAM, which comprises the optimal partitioning of TAM width, assignment of cores to partitioned TAM width etc.
Recently, LCL has become amongst the most attractive filter used for grid-connected flyback inverters. Nonetheless, the switching of power devices in the inverter configuration creates harmonics that affect the end application behavior and might shorten its lifetime. Furthermore, the resonance frequencies produced by the LCL network contribute to the system instability. This paper proposes a step-by-step guide to designing an LCL filter by considering several key aspects such as the resonance frequency and maximum current ripple. A single-phase grid-connected flyback microinverter with an LCL filter was designed then constructed in the MATLAB/Simulink environment. Several different parameter variations and damping solutions were used to analyze the performance of the circuit. The simulation result shows a promising total harmonic distortion (THD) value below 5% and harmonic suppression up to 14%.
FACTS device installation in transmission system using whale optimization alg...journalBEEI
As the world is progressing forward, the load demand in the power system has been continuously increasing day by day. This situation has forced the power system to operate under stress condition due to its limitation. Therefore, due to the stressed condition, the transmission losses faced higher increment with a lower minimum voltage. Theoretically, the installation of the Flexible AC Transmission System (FACTS) device can solve the problem experienced by the power system. This paper presents the whale optimization algorithm for loss minimization using FACTS devices in the transmission system. Thyristor controlled series compensator (TCSC) is chosen for this study. In this study, WOA is developed to identify the optimal sizing of FACTS device for loss minimization in the power system. IEEE 30- bus RTS was used as the test system to validate the effectiveness of the proposed algorithm
Decentralised PI controller design based on dynamic interaction decoupling in...IJECEIAES
An enhanced method for design of decenralised proportional integral (PI) controllers to control various variables of flotation columns is proposed. These columns are multivariable processes characterised by multiple interacting manipulated and controlled variables. The control of more than one variable is not an easy problem to solve as a change in a specific manipulated variable affects more than one controlled variable. Paper proposes an improved method for design of decentralized PI controllers through the introduction of decoupling of the interconnected model of the process. Decoupling the system model has proven to be an effective strategy to reduce the influence of the interactions in the closed-loop control and consistently to keep the system stable. The mathematical derivations and the algorithm of the design procedure are described in detail. The behaviour and performance of the closed-loop systems without and with the application of the decoupling method was investigated and compared through simulations in MATLAB/Simulink. The results show that the decouplers - based closedloop system has better performance than the closed-loop system without decouplers. The highest improvement (2 to 50 times) is in the steady-state error and 1.2 to 7 times in the settling and rising time. Controllers can easily be implemented.
The inverter is the principal part of the photovoltaic (PV) systems that assures the direct current/alternating current (DC/AC) conversion (PV array is connected directly to an inverter that converts the DC energy produced by the PV array into AC energy that is directly connected to the electric utility). In this paper, we present a simple method for detecting faults that occurred during the operation of the inverter. These types of faults or faults affect the efficiency and cost-effectiveness of the photovoltaic system, especially the inverter, which is the main component responsible for the conversion. Hence, we have shown first the faults obtained in the case of the short circuit. Second, the open circuit failure is studied. The results demonstrate the efficacy of the proposed method. Good monitoring and detection of faults in the inverter can increase the system's reliability and decrease the undesirable faults that appeared in the PV system. The system behavior is tested under variable parameters and conditions using MATLAB/Simulink.
Navigation of Mobile Inverted Pendulum via Wireless control using LQR TechniqueIJMTST Journal
Mobile Inverted Pendulum (MIP) is a non-linear robotic system. Basically it is a Self-balancing robot
working on the principle of Inverted pendulum, which is a two wheel vehicle, balances itself up in the vertical
position with reference to the ground. It has four configuration variables (Cart position, Cart Velocity,
Pendulum angle, Pendulum angular velocity) to be controlled using only two control inputs. Hence it is an
Under-actuated system. This paper focuses on control of translational acceleration and deceleration of the
MIP in a dynamically reasonable manner using LQR technique. The body angle and MIP displacement are
controlled to maintain reference states where the MIP is statically unstable but dynamically stable which
leads to a constant translational acceleration due to instability of the vehicle. In this proposal, the
implementation of self balancing robot with LQR control strategy and the implementation of navigation
control of the bot using a wireless module is done. The simulation results were compared between PID control
and LQR control strategies.
With the dominating utility of the internet, it becomes critical to manage the efficiency and reliability of telecom and datacenter, as the power consumption of the involved equipment also increases. Much power being wasted through the power conversion stages by converting AC voltage to DC voltage and then stepping down to lower voltages to connect to information and communication technology (ICT) equipment. 48/12 VDC is the standard DC bus architecture to serve the end utility equipment. This voltage level is further processed to multiple lower voltages to power up the internal auxiliary circuits. Power losses are involved when it is converted from higher voltage to lower voltages. Therefore, the efficiency of power conversion is lower. There is a need to increase the efficiency by minimizing the power losses which occur due to the conversion stages. Different methods are available to increase the efficiency of a system by optimizing the converter topologies, semiconductor materials and control methods. There is another possibility of increasing the efficiency by changing the architecture of a system by increasing the DC bus voltage to higher voltages to optimize the losses. This paper presents a review of available high voltage options for telecom power distribution and developments, implementations and challenges across the world.
Various Types of Faults and Their Detection Techniquesin Three Phase Inductio...IJERA Editor
Artificial neural networks are extensively used for speed, torque estimation, and solid state drive control in both DC and AC machines. These Artificial intelligent techniques are increasingly used for condition monitoring and fault detection of machines. this paper present an overview of researches onThree phase Induction Motors Faults Detection Using Artificial Neural Network(ANN) , a general classification and brief description of the focus area for research and development in this direction are given under title of various types of faults and their detections techniques an improvement in three-phase squirrel-cage induction machine bearing, stator, eccentricity ,inter-turn, end-ring, broken-bar faults detection and diagnosis based on a neural network approach is presented .Future research directions are also highlighted.
Vibration Analysis of Industrial Drive for Broken Bearing Detection Using Pro...IAES-IJPEDS
A reliable monitoring of industrial drives plays a vital role to prevent from the performance degradation of machinery. Today’s fault detection system mechanism uses wavelet transform for proper detection of faults, however it required more attention on detecting higher fault rates with lower execution time. Existence of faults on industrial drives leads to higher current flow rate and the broken bearing detected system determined the number of unhealthy bearings but need to develop a faster system with constant frequency domain. Vibration data acquisition was used in our proposed work to detect broken bearing faults in induction machine. To generate an effective fault detection of industrial drives, Biorthogonal Posterior Vibration Signal-Data Probabilistic Wavelet Neural Network (BPPVS-WNN) system was proposed in this paper. This system was focused to reducing the current flow and to identify faults with lesser execution time with harmonic values obtained through fifth derivative. Initially, the construction of Biorthogonal vibration signal-data based wavelet transform in BPPVS-WNN system localizes the time and frequency domain. The Biorthogonal wavelet approximates the broken bearing using double scaling and factor, identifies the transient disturbance due to fault on induction motor through approximate coefficients and detailed coefficient. Posterior Probabilistic Neural Network detects the final level of faults using the detailed coefficient till fifth derivative and the results obtained through it at a faster rate at constant frequency signal on the industrial drive. Experiment through the Simulink tool detects the healthy and unhealthy motor on measuring parametric factors such as fault detection rate based on time, current flow rate and execution time.
Fault Diagnostics of Rolling Bearing based on Improve Time and Frequency Doma...ijsrd.com
The neural network based approaches a feed forward neural network trained with Back Propagation technique was used for automatic diagnosis of defects in bearings. Vibration time domain signals were collected from a normal bearing and defective bearings under various speed conditions. The signals were processed to obtain various statistical parameters, which are good indicators of bearing condition, then best features are selected from graphical method and these inputs were used to train the neural network and the output represented the bearing states. The trained neural networks were used for the recognition of bearing states. The results showed that the trained neural networks were able to distinguish a normal bearing from defective bearings with 83.33 % reliability. Moreover, the network was able to classify the bearings into different states with success rates better than those achieved with the best among the state-of-the-art techniques.
Disturbance observer-based controller for inverted pendulum with uncertaintie...IJECEIAES
A new approach based on linear matrix inequality (LMI) technique for stabilizing the inverted pendulum is developed in this article. The unknown states are estimated as well as the system is stabilized simultaneously by employing the observer-based controller. In addition, the impacts of the uncertainties are taken into consideration in this paper. Unlike the previous studies, the uncertainties in this study are unnecessary to satisfy the bounded constraints. These uncertainties will be converted into the unknown input disturbances, and then a disturbance observer-based controller will be synthesized to estimate the information of the unknown states, eliminate completely the effects of the uncertainties, and stabilize inverted pendulum system. With the support of lyapunov methodology, the conditions for constructing the observer and controller under the framework of linear matrix inequalities (LMIs) are derived in main theorems. Finally, the simulations for system with and without uncertainties are exhibited to show the merit and effectiveness of the proposed methods.
Adaptive maximum power point tracking using neural networks for a photovoltai...Mellah Hacene
Adaptive Maximum Power Point Tracking Using Neural Networks for a Photovoltaic Systems According Grid
Electrical Engineering & Electromechanics, (5), 57–66, 2021. https://doi.org/10.20998/2074-272X.2021.5.08
Frequency regulation service of multiple-areas vehicle to grid application in...IJECEIAES
Regarding a potential of electric vehicles, it has been widely discussed that the electric vehicle can be participated in electricity ancillary services. Among the ancillary service products, the system frequency regulation is often considered. However, the participation in this service has to be conformed to the hierarchical frequency control architecture. Therefore, the vehicle to grid (V2G) application in this article is proposed in the term of multiple-areas of operation. The multiple-areas in this article are concerned as parking areas, which the parking areas can be implied as a V2G operator. From that, V2G operator can obtain the control signal from hierarchical control architecture for power sharing purpose. A power sharing concept between areas is fulfilled by a proposed adaptive droop factor based on battery state of charge and available capacity of parking area. A nonlinear multiplier factor is used for the droop adaptation. An available capacity is also applied as a limitation for the V2G operation. The available capacity is analyzed through a stochastic character. As the V2G application has to be cooperated with the hierarchical control functions, i.e. primary control and secondary control, then the effect of V2G on hierarchical control functions is investigated and discussed.
This paper presents an online efficiency optimization method for the interior permanent magnet synchronous motor (IPMSM) drive system in an electric vehicle (EV). The proposed method considers accurately the total system losses including fundamental copper and iron losses, harmonic copper and iron losses, magnet loss, and inverter losses. Therefore, it has the capability to always guarantee maximum efficiency control. A highly trusted machine model is built using finite element analysis (FEA). This model considers accurately the magnetic saturation, spatial harmonics, and iron loss effect. The overall system efficiency is estimated online based on the accurate determination of system loss, and then the optimum current angle is defined online for the maximum efficiency per ampere (MEPA) control. A series of results is conducted to show the effectiveness and fidelity of proposed method. The results show the superior performance of proposed method over the conventional offline efficiency optimization methods.
A New Under-Frequency Load Shedding Method Using the Voltage Electrical Dista...IJAEMSJORNAL
This paper proposes a method for determining location to shed the load in order to recover the frequency back to the allowable range. Prioritize distribution of the load shedding at load bus positions based on the voltage electrical distance between the outage generator and the loads. The nearer the load bus from the outage generator is, the sooner the load bus will shed and vice versa. Finally, by selecting the rate of change of generation active power, rate of change of active power of load, rate of change of frequency, rate of change of branches active power and rate of change of voltage in the system as the input to an Artificial Neural Network, the generators outage, the load shedding bus are determined in a short period of time to maintain the stability of the system. With this technique, a large amount of load shedding could be avoided, hence, saved from economic losses. The effectiveness of the proposed method tested on the IEEE 39 Bus New England has demonstrated the effectiveness of this method.
Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural NetworksIJECEIAES
In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.
AN EFFICIENT ALGORITHM FOR WRAPPER AND TAM CO-OPTIMIZATION TO REDUCE TEST APP...IAEME Publication
System-on-Chip (SOC) designs composed of many embedded cores are ubiquitous in today’s integrated circuits. Each of these cores requires to be tested separately after manufacturing of the SoC. That’s why, modular testing is adopted for core-based SoCs, as it promotes test reuse and permits the cores to be tested without comprehensive knowledge about their internal structural details. Such modular testing triggers the need of a special test access mechanism (TAM) to build communication between core I/Os and TAM and promises to minimize overall test time. In this paper, various issues are analyzed to optimize the Wrapper and TAM, which comprises the optimal partitioning of TAM width, assignment of cores to partitioned TAM width etc.
Recently, LCL has become amongst the most attractive filter used for grid-connected flyback inverters. Nonetheless, the switching of power devices in the inverter configuration creates harmonics that affect the end application behavior and might shorten its lifetime. Furthermore, the resonance frequencies produced by the LCL network contribute to the system instability. This paper proposes a step-by-step guide to designing an LCL filter by considering several key aspects such as the resonance frequency and maximum current ripple. A single-phase grid-connected flyback microinverter with an LCL filter was designed then constructed in the MATLAB/Simulink environment. Several different parameter variations and damping solutions were used to analyze the performance of the circuit. The simulation result shows a promising total harmonic distortion (THD) value below 5% and harmonic suppression up to 14%.
FACTS device installation in transmission system using whale optimization alg...journalBEEI
As the world is progressing forward, the load demand in the power system has been continuously increasing day by day. This situation has forced the power system to operate under stress condition due to its limitation. Therefore, due to the stressed condition, the transmission losses faced higher increment with a lower minimum voltage. Theoretically, the installation of the Flexible AC Transmission System (FACTS) device can solve the problem experienced by the power system. This paper presents the whale optimization algorithm for loss minimization using FACTS devices in the transmission system. Thyristor controlled series compensator (TCSC) is chosen for this study. In this study, WOA is developed to identify the optimal sizing of FACTS device for loss minimization in the power system. IEEE 30- bus RTS was used as the test system to validate the effectiveness of the proposed algorithm
Decentralised PI controller design based on dynamic interaction decoupling in...IJECEIAES
An enhanced method for design of decenralised proportional integral (PI) controllers to control various variables of flotation columns is proposed. These columns are multivariable processes characterised by multiple interacting manipulated and controlled variables. The control of more than one variable is not an easy problem to solve as a change in a specific manipulated variable affects more than one controlled variable. Paper proposes an improved method for design of decentralized PI controllers through the introduction of decoupling of the interconnected model of the process. Decoupling the system model has proven to be an effective strategy to reduce the influence of the interactions in the closed-loop control and consistently to keep the system stable. The mathematical derivations and the algorithm of the design procedure are described in detail. The behaviour and performance of the closed-loop systems without and with the application of the decoupling method was investigated and compared through simulations in MATLAB/Simulink. The results show that the decouplers - based closedloop system has better performance than the closed-loop system without decouplers. The highest improvement (2 to 50 times) is in the steady-state error and 1.2 to 7 times in the settling and rising time. Controllers can easily be implemented.
The inverter is the principal part of the photovoltaic (PV) systems that assures the direct current/alternating current (DC/AC) conversion (PV array is connected directly to an inverter that converts the DC energy produced by the PV array into AC energy that is directly connected to the electric utility). In this paper, we present a simple method for detecting faults that occurred during the operation of the inverter. These types of faults or faults affect the efficiency and cost-effectiveness of the photovoltaic system, especially the inverter, which is the main component responsible for the conversion. Hence, we have shown first the faults obtained in the case of the short circuit. Second, the open circuit failure is studied. The results demonstrate the efficacy of the proposed method. Good monitoring and detection of faults in the inverter can increase the system's reliability and decrease the undesirable faults that appeared in the PV system. The system behavior is tested under variable parameters and conditions using MATLAB/Simulink.
Navigation of Mobile Inverted Pendulum via Wireless control using LQR TechniqueIJMTST Journal
Mobile Inverted Pendulum (MIP) is a non-linear robotic system. Basically it is a Self-balancing robot
working on the principle of Inverted pendulum, which is a two wheel vehicle, balances itself up in the vertical
position with reference to the ground. It has four configuration variables (Cart position, Cart Velocity,
Pendulum angle, Pendulum angular velocity) to be controlled using only two control inputs. Hence it is an
Under-actuated system. This paper focuses on control of translational acceleration and deceleration of the
MIP in a dynamically reasonable manner using LQR technique. The body angle and MIP displacement are
controlled to maintain reference states where the MIP is statically unstable but dynamically stable which
leads to a constant translational acceleration due to instability of the vehicle. In this proposal, the
implementation of self balancing robot with LQR control strategy and the implementation of navigation
control of the bot using a wireless module is done. The simulation results were compared between PID control
and LQR control strategies.
With the dominating utility of the internet, it becomes critical to manage the efficiency and reliability of telecom and datacenter, as the power consumption of the involved equipment also increases. Much power being wasted through the power conversion stages by converting AC voltage to DC voltage and then stepping down to lower voltages to connect to information and communication technology (ICT) equipment. 48/12 VDC is the standard DC bus architecture to serve the end utility equipment. This voltage level is further processed to multiple lower voltages to power up the internal auxiliary circuits. Power losses are involved when it is converted from higher voltage to lower voltages. Therefore, the efficiency of power conversion is lower. There is a need to increase the efficiency by minimizing the power losses which occur due to the conversion stages. Different methods are available to increase the efficiency of a system by optimizing the converter topologies, semiconductor materials and control methods. There is another possibility of increasing the efficiency by changing the architecture of a system by increasing the DC bus voltage to higher voltages to optimize the losses. This paper presents a review of available high voltage options for telecom power distribution and developments, implementations and challenges across the world.
Various Types of Faults and Their Detection Techniquesin Three Phase Inductio...IJERA Editor
Artificial neural networks are extensively used for speed, torque estimation, and solid state drive control in both DC and AC machines. These Artificial intelligent techniques are increasingly used for condition monitoring and fault detection of machines. this paper present an overview of researches onThree phase Induction Motors Faults Detection Using Artificial Neural Network(ANN) , a general classification and brief description of the focus area for research and development in this direction are given under title of various types of faults and their detections techniques an improvement in three-phase squirrel-cage induction machine bearing, stator, eccentricity ,inter-turn, end-ring, broken-bar faults detection and diagnosis based on a neural network approach is presented .Future research directions are also highlighted.
Vibration Analysis of Industrial Drive for Broken Bearing Detection Using Pro...IAES-IJPEDS
A reliable monitoring of industrial drives plays a vital role to prevent from the performance degradation of machinery. Today’s fault detection system mechanism uses wavelet transform for proper detection of faults, however it required more attention on detecting higher fault rates with lower execution time. Existence of faults on industrial drives leads to higher current flow rate and the broken bearing detected system determined the number of unhealthy bearings but need to develop a faster system with constant frequency domain. Vibration data acquisition was used in our proposed work to detect broken bearing faults in induction machine. To generate an effective fault detection of industrial drives, Biorthogonal Posterior Vibration Signal-Data Probabilistic Wavelet Neural Network (BPPVS-WNN) system was proposed in this paper. This system was focused to reducing the current flow and to identify faults with lesser execution time with harmonic values obtained through fifth derivative. Initially, the construction of Biorthogonal vibration signal-data based wavelet transform in BPPVS-WNN system localizes the time and frequency domain. The Biorthogonal wavelet approximates the broken bearing using double scaling and factor, identifies the transient disturbance due to fault on induction motor through approximate coefficients and detailed coefficient. Posterior Probabilistic Neural Network detects the final level of faults using the detailed coefficient till fifth derivative and the results obtained through it at a faster rate at constant frequency signal on the industrial drive. Experiment through the Simulink tool detects the healthy and unhealthy motor on measuring parametric factors such as fault detection rate based on time, current flow rate and execution time.
Fault Diagnostics of Rolling Bearing based on Improve Time and Frequency Doma...ijsrd.com
The neural network based approaches a feed forward neural network trained with Back Propagation technique was used for automatic diagnosis of defects in bearings. Vibration time domain signals were collected from a normal bearing and defective bearings under various speed conditions. The signals were processed to obtain various statistical parameters, which are good indicators of bearing condition, then best features are selected from graphical method and these inputs were used to train the neural network and the output represented the bearing states. The trained neural networks were used for the recognition of bearing states. The results showed that the trained neural networks were able to distinguish a normal bearing from defective bearings with 83.33 % reliability. Moreover, the network was able to classify the bearings into different states with success rates better than those achieved with the best among the state-of-the-art techniques.
Predictive maintenance of rotational machinery using deep learningIJECEIAES
This paper describes an implementation of a deep learning-based predictive maintenance (PdM) system for industrial rotational machinery, built upon the foundation of a long short-term memory (LSTM) autoencoder and regression analysis. The autoencoder identifies anomalous patterns, while the latter, based on the autoencoder’s output, estimates the machine’s remaining useful life (RUL). Unlike prior PdM systems dependent on labelled historical data, the developed system doesn’t require it as it’s based on an unsupervised deep learning model, enhancing its adaptability. The paper also explores a robust condition monitoring system that collects machine operational data, including vibration and current parameters, and transmits them to a database via a Bluetooth low energy (BLE) network. Additionally, the study demonstrates the integration of this PdM system within a web-based framework, promoting its adoption across various industrial settings. Tests confirm the system's ability to accurately identify faults, highlighting its potential to reduce unexpected downtime and enhance machinery reliability.
Fault diagnosis of rolling element bearings using artificial neural network IJECEIAES
Bearings are essential components in the most electrical equipment. Procedures for monitoring the condition of bearings must be developed to prevent unexpected failure of these components during operation to avoid costly consequences. In this paper, the design of a monitoring system for the detection of rolling element-bearings failure is proposed. The method for detecting and locating this type of fault is carried out using advanced intelligent techniques based on a perceptron multilayer artificial neural network (MLP-ANN); its database uses statistical indicators characterizing vibration signals. The effectiveness of the proposed method is illustrated using experimentally obtained bearing vibration data, and the results have shown good accuracy in detecting and locating defects.
Gearbox Fault Diagnosis using Independent Angular Re-Sampling Technique, Wave...RSIS International
The vibration signal of a gearbox carries abundant
information about its condition and is widely utilized to diagnose
the condition of the gearbox. Most of the research efforts in
diagnosing gearbox faults, however, assume the acquired
vibration signal to be stationary. This paper attempts to
highlight the non-stationary nature of gearbox vibration signals
owing to uncertainties of the drive and load mechanisms and to
synchronize such signals from the revolution point of view. This
synchronization is achieved by a simple process referred to as the
independent angular re-sampling (IAR) technique. The basis of
the IAR technique is the assumption of constant angular
acceleration over each independent revolution of the gearbox
drive shaft. Through the IAR process, non-stationary signals
sampled at constant time increments are converted into quasistationary
signals sampled at constant angular increments. The
angular domain signals obtained from the IAR technique are
merged to generate the synchronized angular domain vibration
signal for the complete rotational period. The angular domain
signal for each gear health condition is partitioned into a number
of data samples which are then analyzed using wavelet packet
decomposition. Standard deviation values of wavelet packet
coefficients are then computed and the resulting vectors utilized
as inputs to a multilayer perceptron neural network for
identifying the condition of the gearbox.
Effective fault diagnosis of rolling bearings are of great importance in guaranteeing the
normal operation of rotating machinery. However, the measured rolling bearing vibration signals
are highly nonlinear and interrupted by background noise, making it hard to obtain the representative
fault features. Based on this, an optimal fault diagnosis method is proposed to accurately and
steadily diagnose the rolling bearing faults in this paper. The proposed method mainly contains the
following stages. Firstly, gated recurrent unit and sparse autoencoder are constructed as a novel
hybrid deep learning model to directly and effectively mine the fault information of rolling bearing
vibration signals. Secondly, the key parameters of the constructed model are optimized by grey wolf
optimizer algorithm to achieve better diagnosis performance. Finally, the features obtained by the
constructed model are input into the classifier to get the final diagnosis results. The proposed method
is validated using the experimental and practical engineering bearing data and the results confirm
the diagnosis performance of the developed method is more effective and robust than other methods.
Detection of Static Air-Gap Eccentricity in Three Phase induction Motor by Us...IJERA Editor
This paper presents the effect of the static air-gap eccentricity on the performance of a three phase induction motor .The Artificial Neural Network (ANN) approach has been used to detect this fault .This technique depends upon the amplitude of the positive and negative harmonics of the frequency. Two motors of (2.2 Kw) have been used to achieve the actual fault and desirable data at no-load, half-load and full-load conditions. Motor Current Signature analysis (MCSA) based on stator current has been used to detect eccentricity fault. Feed forward neural network and error back propagation training algorithms are used to perform the motor fault detection. The inputs of artificial neural network are the amplitudes of the positive and negative harmonics and the speed, and the output is the type of fault. The training of neural network is achieved by data through the experiments test on healthy and faulty motor and the diagnostic system can discriminate between “healthy” and “faulty” machine.
Induction Motor Bearing Health Condition Classification Using Machine Learnin...Niloy Sikder
A survey on a few existing methods for motor bearing fault classification using various machine learning algorithms. Presented as a part of a course "Seminar (CSE 5001)"
Slantlet transform used for faults diagnosis in robot armIJEECSIAES
The robot arm systems are the most target systems in the fields of faults detection and diagnosis which are electrical and the mechanical systems in many fields. Fault detection and diagnosis study is presented for two robot arms. The disturbance due to the faults at robot's joints causes oscillations at the tip of the robot arm. The acceleration in multi-direction is analysed to extract the features of the faults. Simulations for planar and space robots are presented. Two types of feature (faults) detection methods are used in this paper. The first one is the discrete wavelet transform, which is applied in many research's works before. The second type, is the Slantlet transform, which represents an improved model of the discrete wavelet transform. The multi-layer perceptron artificial neural network is used for the purpose of faults allocation and classification. According to the obtained results, the Slantlet transform with the multi-layer perceptron artificial neural network appear to possess best performance (4.7088e-05), lower consuming time (71.017308 sec) and higher accuracy (100%) than the results obtained when applying discrete wavelet transform and artificial neural network for the same purpose.
Slantlet transform used for faults diagnosis in robot armnooriasukmaningtyas
The robot arm systems are the most target systems in the fields of faults detection and diagnosis which are electrical and the mechanical systems in many fields. Fault detection and diagnosis study is presented for two robot arms. The disturbance due to the faults at robot's joints causes oscillations at the tip of the robot arm. The acceleration in multi-direction is analysed to extract the features of the faults. Simulations for planar and space robots are presented. Two types of feature (faults) detection methods are used in this paper. The first one is the discrete wavelet transform, which is applied in many research's works before. The second type, is the Slantlet transform, which represents an improved model of the discrete wavelet transform. The multi-layer perceptron artificial neural network is used for the purpose of faults allocation and classification. According to the obtained results, the Slantlet transform with the multi-layer perceptron artificial neural network appear to possess best performance (4.7088e-05), lower consuming time
(71.017308 sec) and higher accuracy (100%) than the results obtained when applying discrete wavelet transform and artificial neural network for the same purpose.
Applications of Artificial Neural Network and Wavelet Transform For Conditio...IJMER
The vibration analysis of rotating machinery indicates of the condition of potential faults such
as unbalance, bent shaft, shaft crack, bearing clearance, rotor rub, misalignment, looseness, oil whirl
and whip and other malfunctions. More than one fault can occur in a rotor. This paper describes the
application of Artificial Neural Network (ANN) and Wavelet Transform (WT) for the prediction of the
effect of the combined faults of unbalance and bearing clearance on the frequency components of
vibration signature of the rotating machinery. The experimental data of frequency components and
corresponding Root Mean Square (RMS) velocity (amplitude) data are used as inputs to train the ANN,
which consists of a three-layered network. The ANN is trained using an improved multilayer feed forward
back propagation Levenberg-Marquardt algorithm. In particular, an overall success rates achieved were
99.78% for unbalance, 99.81% bearing clearance, and 99.45% for the combined faults of unbalance and
bearing clearance. The wavelet transform approach enables instant to instant observation of different
frequency components over the full spectrum. A new technique combining the WT with ANN performs
three general tasks data acquisition, feature extraction and fault identification. This method is tested
successfully for individual and combined faults of unbalance and bearing clearance at a success rate of
99.99%.
Condition Monitoring of Rotating Equipment Considering the Cause and Effects ...IJMERJOURNAL
ABSTRACT: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing Condition Monitoring with emphasis on models, algorithms and technologies for data processing and maintenance decision-making. Realising the increasing trend of using multiple sensors in condition monitoring, the authors also discuss different techniques for multiple sensor data fusion. The paper concludes with a brief discussion on current practices, possible future trends of Condition Monitoring with a brief outline on the novelty of the current research work.
This paper focuses on the artificial bee colony (ABC) algorithm, which is a nonlinear optimization problem. is proposed to find the optimal power flow (OPF). To solve this problem, we will apply the ABC algorithm to a power system incorporating wind power. The proposed approach is applied on a standard IEEE-30 system with wind farms located on different buses and with different penetration levels to show the impact of wind farms on the system in order to obtain the optimal settings of control variables of the OPF problem. Based on technical results obtained, the ABC algorithm is shown to achieve a lower cost and losses than the other methods applied, while incorporating wind power into the system, high performance would be gained.
Kosovo has limited renewable energy resources and its power generation sector is based on fossil fuels. Such a situation emphasizes the importance of active research and efficient use of renewable energy potential. According to the analysis of meteorological data for Kosovo, it can be concluded that among the most attractive potential wind power sites are the locations known as Kitka (42° 29' 41" N and 21° 36' 45" E) and Koznica (42° 39′ 32″ N, 21° 22′30″E). The two terrains in which the analysis was carried out are mountain areas, with altitudes of 1142 m (Kitka) and 1230 m (Koznica). the same measuring height, about 84 m above the ground, is obtained for these average wind speeds: Kitka 6,667 m/s and Koznica 6,16 m/s. Since the difference in wind speed is quite large versus a difference in altitude that is not being very large, analyses are made regarding the terrain characteristics including the terrain relief features. In this paper it will be studied how much the roughness of the terrain influences the output energy. Also, that the assumption to be taken the same as to how much they will affect the annual energy produced.
Large-scale grid-tied photovoltaic (PV) station are increasing rapidly. However, this large penetration of PV system creates frequency fluctuation in the grid due to the intermittency of solar irradiance. Therefore, in this paper, a robust droop control mechanism of the battery energy storage system (BESS) is developed in order to damp the frequency fluctuation of the multi-machine grid system due to variable active power injected from the PV panel. The proposed droop control strategy incorporates frequency error signal and dead-band for effective minimization of frequency fluctuation. The BESS system is used to consume/inject an effective amount of active power based upon the frequency oscillation of the grid system. The simulation analysis is carried out using PSCAD/EMTDC software to prove the effectiveness of the proposed droop control-based BESS system. The simulation result implies that the proposed scheme can efficiently curtail the frequency oscillation.
This study investigates experimentally the performance of two-dimensional solar tracking systems with reflector using commercial silicon based photovoltaic module, with open and closed loop control systems. Different reflector materials were also investigated. The experiments were performed at the Hashemite University campus in Zarqa at a latitude of 32⁰, in February and March. Photovoltaic output power and performance were analyzed. It was found that the modified photovoltaic module with mirror reflector generated the highest value of power, while the temperature reached a maximum value of 53 ̊ C. The modified module suggested in this study produced 5% more PV power than the two-dimensional solar tracking systems without reflector and produced 12.5% more PV power than the fixed PV module with 26⁰ tilt angle.
This paper focuses on the modeling and control of a wind energy conversion chain using a permanent magnet synchronous machine. This system behaves a turbine, a generator, DC/DC and DC/AC power converters. These are connected on both sides to the DC bus, where the inverter is followed by a filter which is connected to the grid. In this paper, we have been used two types of controllers. For the stator side converter, we consider the Takagi-Sugeno approach where the parameters of controller have been computed by the theory of linear matrix inequalities. The stability synthesis has been checked using the Lyapunov theory. According to the grid side converter, the proportional integral controller is exploited to keep a constant voltage on the DC bus and control both types of powers. The simulation results demonstrate the robustness of the approach used.
This paper deals with an advanced design for a pump powered by solar energyto supply agricultural lands with water and also the maximum power point is used to extract the maximum value of the energy available inside the solar panels and comparing between techniques MPPT such as Incremental conductance, perturb & observe, fractional short current circuit, and fractional open voltage circuit to find the best technique among these. The solar system is designed with main parts: photovoltaic (PV) panel, direct current/direct current (DC/DC) converter, inverter, filter, and in addition, the battery is used to save energy in the event that there is an increased demand for energy and not to provide solar radiation, as well as saving energy in the case of generation more than demand. This work was done using the matrix laboratory (MATLAB) simulink program.
The objective of this paper is to provide an overview of the current state of renewable energy resources in Bangladesh, as well as to examine various forms of renewable energies in order to gain a comprehensive understanding of how to address Bangladesh's power crisis issues in a sustainable manner. Electricity is currently the most useful kind of energy in Bangladesh. It has a substantial influence on a country's socioeconomic standing and living standards. Maintaining a stable source of energy at a cost that is affordable to everyone has been a constant battle for decades. Bangladesh is blessed with a wealth of natural resources. Bangladesh has a huge opportunity to accelerate its economic development while increasing energy access, livelihoods, and health for millions of people in a sustainable way due to the renewable energy system.
When the irradiance distribution over the photovoltaic panels is uniform, the pursuit of the maximum power point is not reached, which has allowed several researchers to use traditional MPPT techniques to solve this problem Among these techniques a PSO algorithm is used to have the maximum global power point (GMPPT) under partial shading. On the other hand, this one is not reliable vis-à-vis the pursuit of the MPPT. Therefore, in this paper we have treated another technique based on a new modified PSO algorithm so that the power can reach its maximum point. The PSO algorithm is based on the heuristic method which guarantees not only the obtaining of MPPT but also the simplicity of control and less expensive of the system. The results are obtained using MATLAB show that the proposed modified PSO algorithm performs better than conventional PSO and is robust to different partial shading models.
A stable operation of wind turbines connected to the grid is an essential requirement to ensure the reliability and stability of the power system. To achieve such operational objective, installing static synchronous compensator static synchronous compensator (STATCOM) as a main compensation device guarantees the voltage stability enhancement of the wind farm connected to distribution network at different operating scenarios. STATCOM either supplies or absorbs reactive power in order to ensure the voltage profile within the standard-margins and to avoid turbine tripping, accordingly. This paper present new study that investigates the most suitable-location to install STATCOM in a distribution system connected wind farm to maintain the voltage-levels within the stability margins. For a large-scale squirrel cage induction generator squirrel-cage induction generator (SCIG-based) wind turbine system, the impact of STATCOM installation was tested in different places and voltage-levels in the distribution system. The proposed method effectiveness in enhancing the voltage profile and balancing the reactive power is validated, the results were repeated for different scenarios of expected contingencies. The voltage profile, power flow, and reactive power balance of the distribution system are observed using MATLAB/Simulink software.
The electrical and environmental parameters of polymer solar cells (PSC) provide important information on their performance. In the present article we study the influence of temperature on the voltage-current (I-V) characteristic at different temperatures from 10 °C to 90 °C, and important parameters like bandgap energy Eg, and the energy conversion efficiency η. The one-diode electrical model, normally used for semiconductor cells, has been tested and validated for the polemeral junction. The PSC used in our study are formed by the poly(3-hexylthiophene) (P3HT) and [6,6]-phenyl C61-butyric acid methyl ester (PCBM). Our technique is based on the combination of two steps; the first use the Least Mean Squares (LMS) method while the second use the Newton-Raphson algorithm. The found results are compared to other recently published works, they show that the developed approach is very accurate. This precision is proved by the minimal values of statistical errors (RMSE) and the good agreement between both the experimental data and the I-V simulated curves. The obtained results show a clear and a monotonic dependence of the cell efficiency on the studied parameters.
The electrical distribution network is undergoing tremendous modifications with the introduction of distributed generation technologies which have led to an increase in fault current levels in the distribution network. Fault current limiters have been developed as a promising technology to limit fault current levels in power systems. Though, quite a number of fault current limiters have been developed; the most common are the superconducting fault current limiters, solid-state fault current limiters, and saturated core fault current limiters. These fault current limiters present potential fault current limiting solutions in power systems. Nevertheless, they encounter various challenges hindering their deployment and commercialization. This research aimed at designing a bridge-type nonsuperconducting fault current limiter with a novel topology for distribution network applications. The proposed bridge-type nonsuperconducting fault current limiter was designed and simulated using PSCAD/EMTDC. Simulation results showed the effectiveness of the proposed design in fault current limiting, voltage sag compensation during fault conditions, and its ability not to affect the load voltage and current during normal conditions as well as in suppressing the source powers during fault conditions. Simulation results also showed very minimal power loss by the fault current limiter during normal conditions.
This paper provides a new approach to reducing high-order harmonics in 400 Hz inverter using a three-level neutral-point clamped (NPC) converter. A voltage control loop using the harmonic compensation combined with NPC clamping diode control technology. The capacitor voltage imbalance also causes harmonics in the output voltage. For 400 Hz inverter, maintain a balanced voltage between the two input (direct current) (DC) capacitors is difficult because the pulse width modulation (PWM) modulation frequency ratio is low compared to the frequency of the output voltage. A method of determining the current flowing into the capacitor to control the voltage on the two balanced capacitors to ensure fast response reversal is also given in this paper. The combination of a high-harmonic resonator controller and a neutral-point voltage controller working together on the 400 Hz NPC inverter structure is given in this paper.
Direct current (DC) electronic load is a useful equipment for testing the electrical system. It can emulate various load at a high rating. The electronic load requires a power converter to operate and a linear regulator is a common option. Nonetheless, it is hard to control due to the temperature variation. This paper proposed a DC electronic load using the boost converter. The proposed electronic load operates in the continuous current mode and control using the integral controller. The electronic load using the boost converter is compared with the electronic load using the linear regulator. The results show that the boost converter able to operate as an electronic load with an error lower than 0.5% and response time lower than 13 ms.
This paper presents a new simplified cascade multiphase DC-DC buck power converter suitable for low voltage and large current applications. Cascade connection enables very low voltage ratio without using very small duty cycles nor transformers. Large current with very low ripple content is achieved by using the multiphase technique. The proposed converter needs smaller number of components compared to conventional cascade multiphase DC-DC buck power converters. This paper also presents useful analysis of the proposed DC-DC buck power converter with a method to optimize the phase and cascade number. Simulation and experimental results are included to verify the basic performance of the proposed DC-DC buck power converter.
A new bidirectional multilevel inverter topology with a high number of voltage levels with a very reduced number of power components is proposed in this paper. Only TEN power switches and four asymmetric DC voltage sources are used to generate 25 voltage levels in this new topology. The proposed multilevel converter is more suitable for e-mobility and photovoltaic applications where the overall energy source can be composed of a few units/associations of several basic source modules. Several benefits are provided by this new topology: Highly sinusoidal current and voltage waveforms, low Total Harmonic Distortion, very low switching losses, and minimum cost and size of the device. For optimum control of this 25-level voltage inverter, a special Modified Hybrid Modulation technique is performed. The proposed 25-level inverter is compared to various topologies published recently in terms of cost, the number of active power switches, clamped diodes, flying capacitors, DC floating capacitors, and the number of DC voltage sources. This comparison clearly shows that the proposed topology is cost-effective, compact, and very efficient. The effectiveness and the good performance of the proposed multilevel power converter (with and without PWM control) are verified and checked by computational simulations.
This paper presents a novel shunt active power filter (SAPF). The power converter that is used in this SAPF is constructed from a four-leg asymmetric multi-level cascaded H-bridge (CHB) inverter that is fed from a photovoltaic source. A three-dimensional space vector modulation (3D-SVPWM) technique is adopted in this work. The multi-level inverter can generate 27-level output with harmonic content is almost zero. In addition to the capability to inject reactive power and mitigating the harmonics, the proposed SAPF has also, the ability to inject real power as it is fed from a PV source. Moreover, it has a fault-tolerant capability that makes the SAPF maintaining its operation under a loss of one leg of the multi-level inverter due to an open-circuit fault without any degradation in the performance. The proposed SAPF is designed and simulated in MATLAB SIMULINK using a single nonlinear load and the results have shown a significant reduction in total harmonics distortion (THD) of the source current under the normal operating condition and post a failure in one phase of the SAPF. Also, similar results are obtained when IEEE 15 bus network is used.
Transmission lines react to an unexpected increase in power, and if these power changes are not controlled, some lines will become overloaded on certain routes. Flexible alternating current transmission system (FACTS) devices can change the voltage range and phase angle and thus control the power flow. This paper presents suitable mathematical modeling of FACTS
devices including static var compensator (SVC) as a parallel compensator and high voltage direct current (HVDC) bonding. A comprehensive modeling of SVC and HVDC bonding in the form of simultaneous applications for power flow is also performed, and the effects of compensations are compared. The comprehensive model obtained was implemented on the 5-bus test system in MATLAB software using the Newton-Raphson method, revealed that generators have to produce more power. Also, the addition of these devices stabilizes the voltage and controls active and reactive power in the network.
Environmental factors such as air pollution and increase in global warming by using polluting fuels are the most important reasons of using renewable and clean energy that runs in global community. Wind energy is one of the most suitable and widely used kind of renewable energy which had been in consideration so well. This paper introduces an electric power generation
system of wind based on Y-source and improved Y-source inverter to deliver optimal electrical power to the network. This new converter is from impedance source converters family. This presented converter has more degrees of freedom to adjust voltage gain and modulation. Also, by limiting the range of simultaneous control (shooting through) while it maintains the
highest power of maximizer, it can operate in higher modulation range. This causes the reduce of stress in switching and thus it will improve the quality of output. Recommended system had been simulated in MATLAB/Simulink and shown results indicate accurate functionality.
A hybrid DC/DC/AC converter connected to the grid without a three-phase transformer is controlled. The decentralized control method is applied to the hybrid DC-DC converter such that the maximum power of PV flows to the grid side. This controller must charge and discharge the battery at the proper time. It must also regulate DC-link voltage. An additional advantage of the proposed control is that the three-phase inverter does not need a separate controller such as PWM and SPWM. A simple technique is used for creating the desired phase shift in the three-phase inverter, which makes the active and reactive power of the inverter controllable. A new configuration is also proposed to transmit and manage the generation power of PV. In this scheme, the battery and fuel cell are employed as an auxiliary source to manage the generation power of PV. Finally, a real-time simulation is performed to verify the effectiveness of the proposed controller and system by considering the real characteristics of PV and FC.
This paper presents an analytical comparison between two-level inverter and three-level neutral point diode clamped inverters for electric vehicle traction purposes. The main objective of the research is to declare the main differences in the performance of the two inverter schemes in terms of the switching and conduction losses over an entire domain of the modulation index and the phase angle distribution, steady-state operation, transient operation at a wide range of speed variation, and the total harmonic distortion THD% of the line voltage output waveform. It also declares the analysis of the three-level neutral point diode clamped inverter (NPCI) obstacle and the unbalance of the DC-link capacitor voltages. The introduced scheme presents an Induction Motor (IM) drive for electric vehicle (EV) applications. Considering the dynamic operation of the EV, the speed of the three-phase induction motor is controlled using a scalar V/Hz control for the full range of the IM power factor (PF). A comprehensive MATLAB/Simulink model for the proposed scheme is established.
More from International Journal of Power Electronics and Drive Systems (20)
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
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Rotating blade faults classification of a rotor-disk-blade system using artificial neural network
1. International Journal of Power Electronics and Drive Systems (IJPEDS)
Vol. 12, No. 3, September 2021, pp. 1900~1911
ISSN: 2088-8694, DOI: 10.11591/ijpeds.v12.i3.pp1900-1911 1900
Journal homepage: http://ijpeds.iaescore.com
Rotating blade faults classification of a rotor-disk-blade system
using artificial neural network
Abdullahi Abubakar Mas’ud1
, Ahmad Jamal2
, Surajuddeen Adewusi3
, Arunachalam Sundaram4
1,4
Department of Electrical and Electronic Engineering, Jubail Industrial College, Kingdom of Saudi Arabia
1,2
Prince Saud bin Thunayan Research Centre, Royal Commission for Jubail, Kingdom of Saudi Arabia
2,3
Department of Mechanical Engineering, Jubail University College, Kingdom of Saudi Arabia
Article Info ABSTRACT
Article history:
Received May 12, 2021
Revised Jul 9, 2021
Accepted Jul 17, 2021
In this paper, the artificial neural network (ANN) has been utilized for
rotating machinery faults detection and classification. First, experiments
were performed to measure the lateral vibration signals of laboratory test rigs
for rotor-disk-blade when the blades are defective. A rotor-disk-blade system
with 6 regular blades and 5 blades with various defects was constructed.
Second, the ANN was applied to classify the different x- and y-axis lateral
vibrations due to different blade faults. The results based on training and
testing with different data samples of the fault types indicate that the ANN is
robust and can effectively identify and distinguish different blade faults
caused by lateral vibrations in a rotor. As compared to the literature, the
present paper presents a novel work of identifying and classifying various
rotating blade faults commonly encountered in rotating machines using
ANN. Experimental data of lateral vibrations of the rotor-disk-blade system
in both x- and y-directions are used for the training and testing of the
network.
Keywords:
Artificial neural networks
Blade faults
Blade system
Python
Rotating machinery
This is an open access article under the CC BY-SA license.
Corresponding Author:
Abdullahi Abubakar Mas‟ud
Department of Electrical and Electronic Engineering
Jubail Industrial College and Prince Saud bin Thunayan Research Centre, Jubail
P.O. Box, 10099, Kingdom of Saudi Arabia
Email: masud_a@jic.edu.sa
1. INTRODUCTION
For competitiveness and high quality operating requirements in industries, the need for effective
rotating machines has been significant. This can be achieved in part through continuous monitoring of
operating conditions to detect any fault before the rotating machines break down. Faults in rotating blades are
very difficult to detect by direct blade vibration measurements and evaluation. This is because blades often
operate in very harsh environments. In addition, no significant results have been obtained in the quest for a
more efficient artificial intelligence system for real time monitoring of these faults yet.
The performance and reliability of spinning machines are maintained by constantly tracking their
working conditions in order to detect any defects before causing a significant problem or failure of the
system that may impact output targets. There are research articles on the rotor vibration response [1]-[3], that
are usually monitored in the industry and analysed for identification of faults. These efforts have resulted in
the development of knowledge base and standard procedures to identify machine faults such as shaft
misalignment and unbalance, rotor eccentricity, blade crack, twisting, and rubbing. Many industrial rotating
machines have rotors, disks, blades, and vanes. Hence, it is important to monitor the dynamic response of the
machine parts such as rotor-disk-blade system that potentially can develop machine faults contributed by
2. Int J Pow Elec & Dri Syst ISSN: 2088-8694
Rotating blade faults classification of a rotor-disk-blade system using … (Abdullahi Abubakar Mas’ud)
1901
blade defects. Despite the fact that vibration response of rotor can be easily measured with the help of
proximity probe and accelerometer, direct measurement of vibration response of the blades sometimes
becomes very difficult and expensive since blades sometimes operate at very extreme conditions of
temperature and pressure encountered in gas and steam turbines. Also, strain gauges mounted on the blades
to measure blade deflection have short operating life due to these extreme conditions, blade aerodynamics
interference. As a result, non-contacting and non-intrusive techniques to measure the blade vibrations due to
blade defects have been developed. One such technique is used by Lawson and Ivey [4], in which blade tip
timing is measured using tip clearance capacitance probes.
Many researchers have extracted the blade dynamic response from coupled responses of rotor-disk-
blade systems [5], [6]. For this, they established a relationship between rotor torsional vibration and blade
dynamic response. However, one study shows blade fault detection from rotor torsional vibrations. There are
few studies that exhibit relationship between the rotor-blade system and blade vibration [7]. Few studies
exhibit relationship between the rotor-blade system and blade vibration [6], [8]. One of these studies
measured [8], analytically and experimentally, the blade vibrations through the lateral vibration of the
support of a rotor-blade system. Even though specific blade faults were not investigated in this study, they
provided the basis for blade fault detection from rotor lateral vibration.
A Substantial amount of work on the detection of various rotating machine faults such as blade
distortion, shaft misalignment, rotor unbalance, rotor crack. using machine learning algorithms has been
undertaken. A number of review articles are found on fault diagnosis of rotating machinery using the
artificial intelligence (AI). Liu et al. [9] presented a comprehensive review of previous research on such
faults using naïve Bayes, support vector machine, deep learning algorithms and the k-nearest neighbor
(KNN). The benefits, constraints, and practical implications of such AI algorithms were also discussed.
Another review paper by Lei et al. [10] presented the machine learning algorithms utilized over the years to
detect machine faults. They categorized the algorithms coined as intelligent fault diagnosis (IFD) into (a)
traditional machine learning theories such as the probabilistic graphical method (PGM), artificial neural
network (ANN), support vector machine (SVM), k-nearest neighbor (KNN), (b) convolutional neural
network (CNN), ResNet and deep learning theory such as deep belief network (DBN), and (c) transfer
learning theories such as transfer component analysis (TCA) and the generative adversarial network (GAN).
They indicated that almost all the IFD could be utilized to diagnose rotating machinery faults. Both review
papers focus on different machine learning algorithms and state-of-the-art techniques useful in diagnosing
different rotating machine faults rather than on the faults or any specific machine fault. Sánchez et al. [11]
used the random forest and KNN machine learning algorithms as classifiers of the gearbox and bearing
faults. Through these, a methodological structure for detecting multiple faults in rotating machinery was
discussed. From the vibration signal, they calculated thirty features in the time domain through function
ranking techniques such as relief, information gain and the chi-square.
Considering some specific rotating machine faults, one of the faults is the shaft unbalance. Out of
many papers on this type of fault, a representative paper by Gohari and Eyadi [12] presented a comparative
study of two machine learning algorithms namely the decision tree algorithms and the KNN to identify the
shaft unbalance parameters such as eccentric radius, disc number and the eccentric mass value. They
concluded on the basis of their results that KNN estimates the faults better than the Decision Tree algorithm.
Another fault is the blade rub-impact. Prosvirin et al. [13] identified this fault in turbines using a deep neural
network (DNN) and the Auto encoder-based nonlinear function approximation. They used a two-step process
for the diagnosis. First, they used a deep under-complete de-noising auto encoder for determining the
nonlinear function of the system and then they used the resulting residual signals from the original signals as
input to the DNN to find the current state of the blade-rotor system.
Classical ANN algorithms have been utilized by many researchers in the past to diagnose various
rotating machine faults. Zhong et al. [14] utilized single back-propagation (BP), single ellipsoid
backpropagation, and hierarchical diagnostic artificial neural network (HDANN) algorithms to classify
different rotor system faults. They showed that HDANN required only known fault patterns instead of
training the entire network. The fault data were collected experimentally through a motor and test rig rotor
system and then fed into the three algorithms for fault identification. They concluded that HDANN classified
the faults most accurately. Nahvi and Esfahanian [15], used multi-layer feed forward neural network with
nonlinear neurons (sigmoidal activation function) to identify 40 rotating machine faults. They could train the
neural network from selected data and then utilize it to identify the faults. Adewusi and Albedoor [16] used
multi-layer feed-forward ANN to detect crack in a rotor. Two- and three-layer networks with different
number of neurons in each layer were tested to determine an optimal ANN configuration. Vibration signals
of a normal rotor and that with a propagating crack were used to train and test the ANN using back-
propagation (BP) algorithm. The results showed that the performance of a three-layer ANN was better than
that of two-layer ANN in detecting the severity of the propagating crack in the rotor. Nguyen et al. [17], on
3. ISSN: 2088-8694
Int J Pow Elec & Dri Syst, Vol. 12, No. 3, September 2021 : 1900 – 1911
1902
the other hand, worked on the gearbox system and developed a deep neural network based diagnosis model to
diagnose and classify multi-level tooth cut gear faults operating under variable shaft speeds. They first
implemented the adaptive noise control approach to remove any unwanted noise in the original vibration
signal and then employed the stacked sparse autoencoder based deep neural network to diagnose and classify
the gearbox faults.
In addition to the application of ANN to diagnose specific rotating machine faults, some previous
works are reported here identifying rotor-bearing system faults using multi-layer BP neural network [18],
rolling element bearing faults using time-domain features in ANN [19], minor faults such as scratch and hole
in the bearings of induction motors using five machine learning algorithms and convolutional neural network
[20], and compound bearing defects such as inner race, outer race and roller defect for embedded systems
under varying rotational speeds using MobileNet-v2; a light state-of-the-art convolutional neural network
model [21]. In addition, two articles related to rotating shafts addressed the the detection of ball bearing
faults producing vibrations in Rotating shafts using multi-layer feed forward neural network [22] and
induction motor shaft misalignments using multi-scale entropy (MSE) coupled with back-propagation neural
network [23].
Related to the present study of utilizing ANN for blade faults identification, some previous work can
be found. Rao and Srinivas [24] used single layer neural network to locate the optimal position of rotational
constraint along the length of a pre-twisted blade on a rotor system in order to increase the lowest natural
frequency undergoing torsional vibration. Two papers are found that address the blade faults of variable
rotational speed wind turbines. First is by Chen et al. [25] in which they used the deep learning approach by
embedding attention mechanism into long-short-term memory neural network to identify the wind turbine
blade imbalance fault due to the accumulation of ice on the blades. Second paper by Joshuva et al. [26]
presented a study investigating various wind turbine blade faults using first variational mode decomposition
(VMD) for experimental data signal pre-processing and then multi-layer perceptron (MLP) for blade fault
classification. The experimental data of lateral vibrations of the shaft in only vertical axis, i.e., y-axis was
acquired specific to wind turbines.
2. EXPERIMENT AND ESTABLISHMENT OF THE SIMULATION MODEL
2.1. Experimental set-up and data measurement
Figure 1 shows the experimental test rig, consisting of a variable speed electric motor, a rotor, a 6-
blade disk and 2 ball bearing supports. The disk and the 6 normal blades were designed and fabricated in a
workshop. In addition to the 6 normal blades, more blades were designed and fabricated and the following
defects were introduced on the blades: twisted, bent, bulged, cracked, slotted bulged, cracked and slotted
blades. Some of the blades with defects are shown in Figure 2 (a). Four accelerometers are attached to the
two supports along the x- and y-axes to measure the lateral vibration of the rotor-disk-blade system. The
signals of the accelerometers are connected to a six-channel B&K data acquisition system and then to a
laptop with Pulse software (Figure 2 (b)). The Pulse software is capable of measuring, analysing and storing
vibration signals from the accelerometers and the signals can be exported to excel file for further processing.
Figure 1. Experimental test-rig for rotor-disk-blade responses at a constant running speed
First, the test-rig was operated by running the electric motor and rotor-disk-blade system at a speed
of 1000 rpm and the vibration signals in time domain for the set-up with 6 normal blades were obtained from
the four accelerometers and saved in the laptop. One of the 6 normal blades was then replaced with one blade
with a defect and the set-up was operated again at 100 rpm to obtain the vibration signal of the system with a
defective blade. The defective blade was later removed and subsequently replaced with other defective blades
one after the other to obtain the vibration signals for all the defective blades. All the time domain vibration
4. Int J Pow Elec & Dri Syst ISSN: 2088-8694
Rotating blade faults classification of a rotor-disk-blade system using … (Abdullahi Abubakar Mas’ud)
1903
signals of the rotor-disk-blade system were exported to excel file for analysis. The vibration signals from the
x- and y-axis accelerometers attached to the support that is close to the disk-blade system are considered for
further analysis.
(a) (b)
Figure 2. (a) three defective blades (bent, twisted and broken blades); (b) PC and B&K pulse system and
vibration data measurement and analysis accessories.
2.2. Data collection procedure
Experiments are conducted on the different blades of a wind turbine by varying the frequency (F)
from 0 Hz to 2000 Hz. The x- and y-coordinates for different sets of blades „namely‟ the good blades without
any defects, twisted blades, bent blades, bulged blades, cracked blades, and slotted blades; all of which are
recorded in an excel file. The dataset so prepared has 13 attributes and 2134 observations. The attributes are
the frequency, x- and y-axis vibration measurements of the good blades without any defects, twisted blades,
bent blades, bulged blades, cracked blades, and slotted blades.
2.3. Simulation model
Python is a high-level programming language. Python is a programming language that can be
utilized to create desktop graphical user interface (GUI) programs [28]. Furthermore, since Python is a high-
level programming language, it helps you to concentrate on the application's core features while it handles
the mundane programming tasks. Python was used in this study to develop the ANN for classifying various
rotating blade faults commonly encountered in rotating machines. Compared to other software‟s, python is a
general-purpose programming language. Python gives more control over one's code organization and better
management of namespace. There are three main steps to develop the ANN in python [29], i.e. 1) using the
provided data as input 2) predicting the output 3) comparison of the prediction with the actual output and 4)
changing its internal state in order to correctly predict the next time.
3. ARTIFICIAL NEURAL NETWORK
ANN refers to a computational model mimicking the human brain and its information processing
capabilities [30], [31]. An ANN is an ensemble of highly interconnected processing units known as the
neurons [32]. There are a number of numerical values between the neurons that shows the strength of the
connections known as the weights [23], [33]. The ANN is capable of self-organization and acquisition of
information, i.e., learning [34].
Figure 3 shows a typical three feed forward neural network, comprising of the input layer, hidden
layer and output layer. In this network, the middle layer nodes only receive information from the nodes of the
previous layer and forward their outputs to consecutive layer nodes only. The hidden layers are not connected
with the outside world directly. The computational elements or nodes used in neural network models are
normally nonlinear and generally analog. As shown in Figure 3, the simplest node sums the weighted inputs
and transfers the result through nonlinearity. The major part of the ANN is its learning algorithm. The BP,
which is a supervised learning algorithm, is the most widely used for various applications [35], [36].
5. ISSN: 2088-8694
Int J Pow Elec & Dri Syst, Vol. 12, No. 3, September 2021 : 1900 – 1911
1904
Figure 3. Typical feed-forward ANN
3.1. Back-propagation algorithm
A type of supervised learning is the BP algorithm [37], [38]. It takes place in two stages, „namely‟
forward propagation of information and backward propagation of errors [14]. It learns by presenting the input
and output data patterns repeatedly and propagating the error backward each time and adjusting the weights
until the error minimizes to a reasonable low value. Some of the BP algorithm's fundamental weaknesses
include longer convergence period and susceptibility to failure of training. One of the modifications
introduced to resolve this issue include adding a momentum term for faster training, thereby compromising
additional memory space. The following equation gives the output of the neuron [37]:
( ) ∑ (1)
Where w1j and xj are the associated weights and the inputs of the ANN, respectively. Figure 4 show
how the BP algorithm is implemented together with the processing elements. Inputs (Xp1.............XpNo) are
initially propagated and the response is computed via the network. Then the output error (Tp1……….TpNM) is
propagated backward through the network and the weights are modified according to the algorithm based on
the gradient descent [30]. This method continues until the average mean square error at the output reaches an
acceptable minimum value. For this work, the BP algorithm will be implemented in the ANN to classify the
different blade faults.
Figure 4. Typical implementation of the BP algorithm
4. DATA EXPLORATION AND PRE-PROCESSING
There are several important steps in data exploration and pre-processing. This section gives an
account of the steps involved in data exploration and pre-processing for vibration faults.
6. Int J Pow Elec & Dri Syst ISSN: 2088-8694
Rotating blade faults classification of a rotor-disk-blade system using … (Abdullahi Abubakar Mas’ud)
1905
4.1. Exploratory data analysis
The next important step after data collection is the exploratory data analysis. The data is analysed by
summarizing the basic statistical description of the data for finding anomalies, missing values, duplicate
values, and patterns or relationship between the attributes. Statistical description of the data reveals that all
attributes are floating point numbers and there are no missing values in the dataset. The range of the attribute
frequency is from 0 Hz to 2000 Hz. For all other attributes, the minimum value is a very small continuous
floating point value that has a power of 1e-9. Further analysis of the data using duplicated in pandas, a data
analysis software, reveal there are no duplicate rows in the dataset. To further analyse the dataset for outliers,
skewness and kurtosis functions in pandas have been used and the results reveal high positive value for skew
and kurtosis for all attributes except the frequency. High positive value for skew indicates all attributes
except frequency are right skewed which indicates the data distribution is not symmetrical and highly skewed
with longer tails to the right side. High positive values for kurtosis reveal numerous outliers in the dataset for
all attributes except frequency. The results shown in Table 1 indicate significant reduction in skewness and
kurtosis values which indicates the transformed dataset is now closer to normal distribution with reduced
outliers. Exploratory data analysis reveals that the dataset has numerous outliers and is positively highly
skewed. Therefore, the training of the ANN would not be ideal and with the help of a suitable transformation
algorithm, the dataset has to be transformed to a new dataset devoid of outliers and skewness.
Table 1. Analysis of the attributes in the dataset
Before processing After processing
Skewness Kurtosis Skewness Kurtosis
F 0 -1.2 -1.97483 5.53236
Good X 14.94039 254.9368 -0.90639 0.649114
Good Y 45.25426 2074.963 -1.42593 4.291290
Twisted X 14.87656 324.2824 -0.74694 -0.13571
Twisted Y 43.77001 1981.886 -1.12677 2.473182
Bent X 11.33756 162.3006 -0.84875 0.286339
Bent Y 44.99905 2058.742 -1.36612 4.188487
Bulged X 24.84025 759.1271 -0.90449 0.610648
Bulged Y 46.12201 2129.461 -1.55131 5.372109
Cracked X 20.21336 546.9635 -0.88551 0.480553
Cracked Y 45.89052 2115.043 -1.4902 4.656801
Slotted X 18.88787 463.5698 -0.82665 0.072927
Slotted Y 46.16197 2131.942 -1.47583 5.158623
4.2. Data transformation
Even many transformations are available, log transformation is applied to all attributes of this
dataset to reduce the skewness to make the data as normal as possible. For the attribute frequency, the log
transformation is applied to all values except the first value which is zero. For all other values, the application
of the Log transformation results in a negative value and hence, after applying Log transformation, the sign
of the data is changed from negative to positive. The basic statistical details of the transformed dataset are
summarized in Table 2.
Table 2. Statistical description of the data after transformation
Count Mean Standard deviation Minimum 25% 50% 75% Maximum
Frequency 2134 2.86629 0.43507 -0.02803 2.69890 2.99993 3.17602 3.30096
Good X 2134 5.69455 0.78286 2.48596 5.15335 5.89308 6.29755 8.73845
Good Y 2134 5.72442 0.57929 1.59341 5.44511 5.85112 6.10766 8.52216
Twisted X 2134 5.60984 0.86286 2.40997 4.92536 5.90164 6.30942 8.67312
Twisted Y 2134 5.67138 0.59989 1.98165 5.33377 5.80116 6.08955 8.81861
Bent X 2134 5.67871 0.79395 2.75327 5.16924 5.89219 6.29509 8.68163
Bent Y 2134 5.71686 0.60327 1.47977 5.42954 5.84326 6.12088 8.95687
Bulged X 2134 5.72402 0.77280 2.25688 5.20475 5.93149 6.31493 8.66393
Bulged Y 2134 5.76086 0.60550 0.89438 5.47030 5.89358 6.15173 8.96419
Cracked X 2134 5.71243 0.78809 2.34252 5.20453 5.93768 6.31626 8.39567
Cracked Y 2134 5.74478 0.60007 1.29346 5.46098 5.87850 6.13801 8.76337
Slotted X 2134 5.67220 0.81912 2.46975 5.08905 5.92917 6.30308 8.38482
Slotted Y 2134 5.69869 0.60130 0.73089 5.39308 5.83621 6.10179 8.76579
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5. DESIGN OF THE ANN AND THE PREDICTED RESULTS
5.1. Design of the ANN
This section describes the architecture of the ANN designed to predict the 10 faults (5 defects in
each x- and y-directions) in the blades of the rotating machines. The input layer of ANN consists of three
neurons „namely‟ the frequency, good x and good y attributes. The output layer consists of 10 neurons
namely, x- and y-axis vibration measurements of twisted blades, bent blades, bulged blades, cracked blades
and slotted blades. After trial and error with a number of neurons, a single hidden layer with 80 neurons with
linear activation function is considered between the input and output layer as shown in Figure 5. Before
choosing the linear activation, correlation between the attributes were considered and a positive correlation
between the input and output attributes was found as shown in the heat map (see Figure 6).
Figure 5. The architectures of the ANN to find the faults in the rotating machine
Figure 6. Heat map of the correlation between the attributes after data transformation
5.2. Selection of the training and testing dataset
Scikit-learn machine learning library available for machine learning for python programming [21] is
used to split the transformed dataset into training and testing dataset. Ninety percent of the dataset is used for
training and ten percent of the dataset is used for testing. 1920 observations are randomly split as training
data and 214 observations are split as testing data.
5.3. Training of the ANN
TensorFlow is a machine learning platform used for training neural networks and it is an open
source library with plenty of fast numerical computing algorithms available to create ANN models and to
train them. Keras is an API that runs on top of TensorFlow and enables fast experimentation to fine tune and
train ANN models. Training of ANN in this research is carried out using Keras and TensorFlow as it helps to
sequentially build and test neural network very quickly with minimal lines of code.
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Rotating blade faults classification of a rotor-disk-blade system using … (Abdullahi Abubakar Mas’ud)
1907
Adam optimizer (adaptive moment estimator) is used for upgrading the weights of the dense layers
of the ANN during the training. Adam uses adaptive learning rate during training phase and changes the
individual learning rates for each parameter based on squared gradients and momentum to enable faster
convergence. Adam is superior to stochastic gradient descent optimizer which uses a learning rate that does
not vary during the iterations and hence preferred for training the ANN.
During training the mean squared error (MSE) loss function is minimized. The maximum epoch is
specified as 80. Very common issue with training neural network model is the problem of over fitting. To
overcome this problem EarlyStopping feature available in keras to stop training when a monitored metric has
stopped improving has been used. The patience argument available in EarlyStopping function is set to five
which stops the training when the loss function has not significantly improved over 5 iterations. The 20 per
cent of the training data was used for validation to test the model during training. The convergence of the
model is provided in Table 3. In Table 3, MSE, MAE, VAL_MSE and VAL_MAE refer to mean squared
error during training, mean absolute error during training, mean squared error during validation, and mean
absolute error during validation respectively. As seen from Table 3 the model converged in 17 iterations
when there is no significant improvement in mse during training. The early stopping feature used in this work
ensures the training continues until the error significantly decreases and error for training and validation data
is almost similar to avoid over fitting. The mse during training and validation is almost the same at the end of
the 17th
iterations which indicates the model will not over fit for unseen data. The plot the convergence
characteristics are shown in Figure 7 (a) and Figure 7 (b). From Figure 7 (a) and Figure 7 (b), it is evident
that during training of ANN, the MSE and MAE reduces to the lowest possible value.
Table 3. Convergence characteristics of the model
Epoch MSE MAE VAL_MSE VAL_MAE
0 18.711683 2.649993 0.358549 0.439097
1 0.231891 0.369927 0.171996 0.322538
2 0.143537 0.290199 0.129434 0.275002
3 0.129264 0.274902 0.12706 0.270735
- - - - -
- - - - -
13 0.064647 0.189005 0.054906 0.17281
14 0.060739 0.182184 0.048822 0.163773
15 0.066706 0.193218 0.06819 0.208151
16 0.060984 0.182181 0.05758 0.182925
17 0.064329 0.189406 0.061712 0.195263
(a) MSE (b) MAE
Figure 7. Plot of (a) MSE, and (b) MAE during training of ANN
5.4. ANN prediction results
In this section, the plot of the actual value and the predicted value for all 10 faults are shown. In
Figures 8 (a)-(j), the trained models are evaluated on the test data and the plots for the 10 attributes are
shown. The predicted values for each fault are shown in the y-axis, while the actual values are shown in x-
axis. The result shows that ANN can predict the x- and y-axes lateral vibrations of most of the faults very
closely. A 45° line indicates a prediction with minimal errors and the data points away from the 45° line
indicates the error in prediction by the ANN. Generally, the results imply that the actual and predicted values
for most of the faults are closely similar with wider variations in few fault examples.
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(a) (b)
(c) (d)
(e) (f)
(g) (h)
(i) (j)
Figure 8. Plot of actual and predicted values for 10 faults, (a) twistedx, (b) twistedy, (c) bentx, (d) benty, (e)
bulgedx, (f) bulgedy, (g) crackedx, (h) crackedy, (i) slottedx, and (j) slottedy
6. CONCLUSION
In this paper, the ANN has been applied to classify different faults of rotating machines. First, the
lateral vibrations of a rotor-disk-blade device consisting of 6 healthy blades were analyzed experimentally
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and through finite element modal analysis. The system responses in two directions were obtained at 1000
rpm. Five faulty blades (twisted, bent, bulged, cracked, and slotted) were replaced in turn by one of the 6 safe
blades. Comparisons of a twisted blade's response magnitudes with those of healthy blades indicate that the
vibration amplitudes in the acceleration response range of the first two resonant frequencies decreased, while
the amplitude nearer to the running speed increased.
After obtaining the data on the aforementioned rotational machine faults, the ANN was utilized to
be able to classify and discriminate the x- and y-axis lateral vibrations of the rotor. The ANN was coded in
python software. Initially, correlation between the attributes was considered and it was shown that there is a
positive correlation between the input and output attributes. For training the ANN, 90% of the dataset is used
and 10% of the dataset is used for testing. The result shows that ANN can very accurately predict the two-
dimensional (x- and y-axes) lateral vibrations of most of the faults. The results imply that the ANN can
effectively recognize and discriminate different lateral vibration in a rotor depending on training and testing
with different data samples of the faults types. Further research concentrates on the application of the ANN
for practical fault detection and classification.
ACKNOWLEDGEMENTS
Acknowledgements are due to Jubail Industrial College and Jubail University College, Royal
Commission for Jubail.
REFERENCES
[1] S. A. Adewusi and B. O. Al-Bedoor, “Wavelet analysis of vibration signals of an overhang rotor with a
propagating transverse crack,” J. Sound Vib., vol. 246, no. 5, pp. 777-793, 2001, doi: 10.1006/jsvi.2000.3611.
[2] J. Der Jeng, L. Hsu, C. W. Hun, and C. Y. Chou, “Identification for bifurcation and responses of rub-impacting
rotor system,” Procedia Eng., vol. 79, pp. 369-377, 2014, doi: 10.1016/j.proeng.2014.06.357.
[3] S. A. Adewusi and B. O. Al-Bedoor, “Experimental study on the vibration of an overhung rotor with a propagating
transverse crack,” Shock Vib., vol. 9, pp. 91-104, 2002, doi: 10.1155/2002/405928.
[4] C. P. Lawson and P. C. Ivey, “Tubomachinery blade vibration amplitude measurement through tip timing with
capacitance tip clearance probes,” Sensors Actuators, A Phys., vol. 118, no. 1, pp. 14-24, 2005, doi:
10.1016/S0924-4247(04)00482-0.
[5] B. O. Al-Bedoor, L. Ghouti, S. A. Adewusi, Y. Al-Nassar, and M. Abdlsamad, “Experiments on the extraction of
blade vibration signature from the shaft torsional vibration signals,” J. Qual. Maint. Eng., vol. 9, no. 2, pp. 144-
159, 2003, doi: 10.1108/13552510310482398.
[6] B. O. Al-Bedoor, Y. Al-Nassar, L. Ghouti, S. A. Adewusi, and M. Abdlsamad, “Shaft lateral and torsional
vibration responses to blade(s) random vibration excitation,” Arab. J. Sci. Eng., vol. 29, no. 1C, pp. 39-67, 2004.
[7] A. A. Gubran and J. K. Sinha, “Shaft instantaneous angular speed for blade vibration in rotating machine,” Mech.
Syst. Signal Process., vol. 44, no. 1-2, pp. 47-59, 2014, doi: 10.1016/j.ymssp.2013.02.005.
[8] I. F. Santos, C. M. Saracho, J. T. Smith, and J. Eiland, “Contribution to experimental validation of linear and non-
linear dynamic models for representing rotor-blade parametric coupled vibrations,” J. Sound Vib., vol. 271, no. 3-5,
pp. 883-904, 2004, doi: 10.1016/S0022-460X(03)00758-2.
[9] R. Liu, B. Yang, E. Zio, and X. Chen, “Artificial intelligence for fault diagnosis of rotating machinery: A review,”
Mech. Syst. Signal Process., vol. 108, pp. 33-47, 2018, doi: 10.1016/j.ymssp.2018.02.016.
[10] Y. Lei, B. Yang, X. Jiang, F. Jia, N. Li, and A. K. Nandi, “Applications of machine learning to machine fault
diagnosis: A review and roadmap,” Mech. Syst. Signal Process., vol. 138, 106587, 2020, doi:
10.1016/j.ymssp.2019.106587.
[11] R. V. Sánchez, P. Lucero, R. E. Vásquez, M. Cerrada, J. C. Macancela, and D. Cabrera, “Feature ranking for multi-
fault diagnosis of rotating machinery by using random forest and KNN,” J. intell. Fuzzy Syst., vol. 34, pp. 3463-
3473, 2018, doi: 10.3233/JIFS-169526.
[12] M. Gohari and A. M. Eydi, “Modelling of shaft unbalance: Modelling a multi discs rotor using K-Nearest Neighbor
and Decision Tree Algorithms,” Measurement, vol. 151, 107253, 2020, doi: 10.1016/j.measurement.2019.107253.
[13] A. E. Prosvirin, F. Piltan, and J. M. Kim, “Blade rub-impact fault identification using autoencoder-based nonlinear
function approximation and a deep neural network,” Sensors, vol. 20, 6265, 2020, doi: 10.3390/s20216265.
[14] B. Zhong, J. MacIntyre, Y. He, and J. Tait, “High order neural networks for simultaneous diagnosis of multiple
faults in rotating machines,” Neural Comput. Appl., vol. 8, pp. 189-195, 1999, doi: 10.1007/s005210050021.
[15] H. Nahvi and M. Esfahanian, “Fault identification in rotating machinery using artificial neural networks,” Proc.
Inst. Mech. Eng. Part C J. Mech. Eng. Sci., vol. 219, no. 2, pp. 141-158, 2005, doi: 10.1243/095440605X8469.
[16] S. A. Adewusi and B. O. Al-Bedoor, “Detection of propagating cracks in rotors using neural networks,” Proc.
ASME Pipe Comp. Anlys. Diag. Conf., vol. 447, no. PVP2002-1518, pp. 71-78, 2002, doi: 10.1115/PVP2002-1518.
[17] C. D. Nguyen, A. E. Prosvirin, C. H. Kim, and J. M. Kim, “Construction of a sensitive and speed invariant gearbox
fault diagnosis model using an incorporated utilizing adaptive noise control and a stacked sparse autoencoder-based
deep neural network,” Sensors, vol. 21, no. 1, 1424-8220, 2021, doi: 10.3390/s21010018.
[18] N. S. Vyas and D. Satishkumar, “Artificial neural network design for fault identification in a rotor-bearing system,”
Mech. Mach. Theory, vol. 36, no. 2, pp. 157-175, 2001, doi: 10.1016/S0094-114X(00)00034-3.
11. ISSN: 2088-8694
Int J Pow Elec & Dri Syst, Vol. 12, No. 3, September 2021 : 1900 – 1911
1910
[19] B. Samanta and K. R. Al-Balushi, “Artificial neural network based fault diagnostics of rolling element bearings
using time-domain features,” Mech. Syst. Signal Process., vol. 17, no. 2, pp. 317-328, 2003, doi:
10.1006/mssp.2001.1462.
[20] S. E. Pandarakone, Y. Mizuno, and H. Nakamura, “A comparative study between machine learning algorithm and
artificial intelligence neural network in detecting minor bearing fault of induction motors,” Energies, vol. 12, no.
11, 2019, doi: 10.3390/en12112105.
[21] M. T. Pham, J. M. Kim, and C. H. Kim, “Deep learning-based bearing fault diagnosis method for embeded
systems,” Sensors, vol. 20, 6886, 2020, doi: 10.3390/s20236886.
[22] H. Taplak, I. Uzmay, and S. Yildirim, “An artificial neural network application to fault detection of a rotor bearing
system,” Ind. Lubr. Tribol., vol. 58, no. 1, pp. 32-44, 2006, doi: 10.1108/00368790610640082.
[23] A. K. Verma, S. Sarangi, and M. Kolekar, “Misalignment faults detection in an induction motor based on multi-
scale entropy and artificial neural network,” Electr. Power Compon. Syst., vol. 44, no. 8, pp. 916-927, 2016, doi:
10.1080/15325008.2016.1139015.
[24] M. A. Rao and J. Srinivas, “Torsional vibrations of pre-twisted blades using artificial neural network technology,”
Eng. Comput., vol. 16, pp. 10-15, 2000, doi: 10.1007/s003660050032.
[25] J. Chen, W. Hu, D. Cao, B. Zhang, Q. Huang, Z. Chen, and F. Blaabjerg, “An imbalance fault detection algorithm
for variable-speed wind turbines: a deep learning approach,” Energies, vol. 12, no. 14, 2764, 2019, doi:
10.3390/en12142764.
[26] A. Joshuva, R. S. Kumar, S. Sivakumar, G. Deenadayalan, and R. Vishnuvardhan, “An insight on VMD for
diagnosing wind turbine blade faults using C4.5 as feature selection and discriminating through multilayer
perceptron,” Alexandria Eng. J., vol. 59, no. 5, pp. 3863-3879, 2020, doi: 10.1016/j.aej.2020.06.041.
[27] S. Adewusi, “Detection of rotating blade faults from lateral vibrations of a rotor-disk-blade system,” Proc. Conf.
Mech. Vib. Noise, no. DETC2015-46567, pp. V008T13A066, 2015, doi: 10.1115/detc2015-46567.
[28] D. Kuhlman, “A python book: beginning python, advanced python, and python exercises,” A Python B., 2009.
[29] F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res., 2011.
[30] S. ichi Amari, “Backpropagation and stochastic gradient descent method,” Neurocomputing, vol. 5, no. 4-5, pp.
185-196, 1993, doi: 10.1016/0925-2312(93)90006-O.
[31] S. L. Souad, B. Azzedine, and S. Meradi, “Fault diagnosis of rolling element bearings using artificial neural
network,” Int. J. Electr. Comput. Eng., vol. 10, no. 5, pp. 5288–5295, 2020, doi: 10.11591/IJECE.V10I5.PP5288-
5295.
[32] D. Barschdorff, L. Monostori, A. F. Ndenge, and G. W. Wöstenkühler, “Multiprocessor systems for connectionist
diagnosis of technical processes,” Comput. Ind., vol. 17, no. 2-3, pp. 131-145, 1991, doi: 10.1016/0166-
3615(91)90026-6.
[33] A. S. Rawat, A. Rana, A. Kumar, and A. Bagwari, “Application of multi layer artificial neural network in the
diagnosis system: A systematic review,” IAES Int. J. Artif. Intell., vol. 7, no. 3, pp. 138–142, 2018, doi:
10.11591/ijai.v7.i3.pp138-142.
[34] P. Bunnoon, “Fault Detection Approaches to power system: state-of-the-art article reviews for searching a new
approach in the future,” Int. J. Electr. Comput. Eng., vol. 3, no. 4, pp. 553–560, 2013, doi:
10.11591/ijece.v3i4.3195.
[35] S. Haykin, Neural Networks and Learning Machines. 2008.
[36] P. Sinha, S. Debath, and S. K. Goswami, “Classification of power quality events using wavelet analysis and
probabilistic neural network,” IAES Int. J. Artif. Intell., vol. 5, no. 1, pp. 1-12, 2016, doi: 10.11591/ijai.v5.i1.pp1-
12.
[37] R. Rojas, “The backpropagation algorithm,” in Neural Networks, pp. 149-182, 1996.
[38] A. Kaur, Y. S. Brar, and G. Leena, “Fault detection in power transformers using random neural networks,” Int. J.
Electr. Comput. Eng., vol. 9, no. 1, pp. 78–84, 2019, doi: 10.11591/ijece.v9i1.pp78-84.
BIOGRAPHIES OF AUTHORS
Abdullahi A. Masud (PhD, CEng) received his BEng. Degree in Electrical Engineering
from Ahmadu Bello University (ABU) in 1999. Subsequently, he pursued his Master‟s
degree in the same discipline at ABU and graduated in 2003. He also obtained a PhD in
Electrical Engineering in 2013 from the Glasgow Caledonian University, Scotland, UK,
under the supervision of Prof. Brian Stewart and Prof. Scott McMeekin. In 2002 he became
an assistant lecturer in the Faculty of Engineering at the Ahmadu Bello University, Zaria,
Nigeria. In 2013 he joined Jubail Industrial College, Saudi Arabia, and is currently an
Associate Professor in the Department of Electrical and Electronic Engineering. He has
several publications in high impact factor journals and conference proceedings in high
voltage partial discharge and renewable energy. He is a Chartered Engineer, a Member of the
IET, and a registered Engineer (COREN) Nigeria.
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Rotating blade faults classification of a rotor-disk-blade system using … (Abdullahi Abubakar Mas’ud)
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Ahmad Jamal obtained his B.S. in Mechanical Engineering degree in 1998 University of
Engineering and Technology, Lahore, Pakistan. His M.S. degree in Mechanical Engineering
was from King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia in 2002
with specialization in Thermo-Fluid. He secured his Ph.D. degree from McGill University,
Montreal, Canada in 2014 in the area of Fluid-Structure Interaction (FSI). He is the author of
more than 20 refreed journals and conferences in the fields of Thermo-Fluid, FSI,
Reliability, and Machine Learning. He undertook many research projects related to Thermo-
Fluid and FSI. He has also supervised many undergraduate design projects related to
Thermo-Fluid and Applied Mechanics. His new research avenue is in the field of Control
Theory.
Dr. Surajudeen Adewusi, P. Eng., MASME is an Assistant Professor and Chairman of
Mechanical Engineering Department at Jubail University College, Saudi Arabia. He
obtained Ph.D. in Mechanical Engineering from Concordia University, Canada in 2009 and
Masters of Science in Mechanical Engineering from King Fahd University of Petroleum &
Minerals, Saudi Arabia in 2000. He was a recipient of the Natural Sciences and Engineering
Research Council of Canada (NSERC) and Quebec Province Research Fund on Nature and
Technology (FQRNT) Postdoctoral Research Fellowships in 2011. Surajudeen has presented
research/technical papers at international conferences and co-authored 30 research papers in
international journals and conference proceedings. His area of expertise include
Experimental and Finite Element Modeling, Analysis and Control of Systems (Dynamic
Characterization of Systems), Rotating Machinery Vibration Condition Monitoring and
Faults Diagnosis (Rotordynamics), Human Biodynamic Responses to Occupational
Vibrations (Biomechanics) and Renewable Energy. He has 25 years of combined academic,
industrial and research work experiences. Dr. Adewusi is a member of the Professional
Engineers of Ontario (PEO), Canada and American Society of Mechanical Engineers
(ASME).
Dr. Arunachalam Sundaram received the B.E. degree in Electrical and Electronics
Engineering from Annamalai University, Chidambaram, India, in 2001, and the M.E. degree
in Power Systems Engineering and the Ph.D. degree in Electrical Engineering from Anna
University, Chennai, India, in 2004 and 2014, respectively. He received the Gold Medal
from Anna University for securing first rank in M.E power systems engineering. He has also
completed Post Graduation Program in Artificial Intelligence and Machine Learning from
University of Texas at Austin offered by Great Learning. He is a senior member in IEEE. He
is currently working as Associate Professor in Jubail Industrial College, Kingdom of Saudi
Arabia, Al-Jubail. He has over 17 years of academic experience where he was involved in
developing short course and long courses for various industries. His research interest
includes artificial intelligence and machine learning, optimization applied to power system
engineering, and smart grids.