The document discusses a proposed approach for monitoring wind turbine states using a random forest algorithm (RFA) applied to SCADA data. It involves 3 stages: 1) predicting any fault, 2) predicting specific fault types, and 3) identifying previously unseen faults. RFA provided the most accurate results among algorithms tested, with prediction accuracies of 78-98% for horizons up to 300 seconds. The model also identified various faults at turbines not in the training data, though it struggled with some gearbox-related faults due to lacking relevant sensor data.
This paper aims to design the pitch angle control based on proportional–integral–derivative (PID) controller combined with fuzzy logic for small-scale wind turbine systems. In this control system, the pitch angle is controlled by the PID controller with their parameter is tuned by the fuzzy logic controller. This control system can compensate for the nonlinear characteristic of the pitch angle and wind speed. A comparison between the fuzzy-PID-controller with the conventional PID controller is carried out. The effectiveness of the method is determined by the simulation results of a small wind turbine using a permanent magnet generator (PMSG).
A Brief Research On Turbojet Engine By Using MPM 20IJERA Editor
Small turbojet engines represent a special class of turbine driven engines. They are suitable for scientific
purposes and research of certain thermodynamic processes ongoing in turbojet engines. Moreover such engines
can be used for research in the area of alternative fuels and new methods of digital control and measurement.
Our research, which is also presented in this article, is headed toward these aims. We evaluate and propose a
system of digital measurement of a particular small turbojet engine – MPM 20. Such engine can be considered
as highly non-linear large scale system. According to obtained data and experiments we propose different model
models of the engine and design of situational control algorithms for the engine with use of certain methods of
artificial intelligence as new methods of control and modeling of large scale systems.
Parametric study of a low cost pneumatic system controlled by onoff solenoid ...eSAT Journals
Abstract Expensive proportional valves are dominantly used in pneumatic positioning systems even with low demanding accuracy
positioning tasks, which deprive pneumatic systems from its economical advantages. Thereby, using low cost on/off solenoid
valves instead of proportional valves has been a topic of research in the last decades. In this paper, a parametric study is
conducted to investigate the effect of using low-cost 3/2 internally pilot on/off solenoid valves to control a double acting cylinder
and study the system nonlinear response to on/off and PWM input signal. Matlab ® Simscape library is used to model and
simulate the system. The model is validated though experimental measurements of the system behavior. The model is used to study
and decrease the nonlinear pressure response associated with the cylinder chambers in addition to the evaluation of the dead
zone and operating range of the on/off solenoid valve when operated with PWM signal. The results show that using a meter-in
flow control and having a near constant cylinder back pressure can reduce the nonlinearity. An orifice of 1e-6 m2 can reduce the
pressure variation by 80% but increase the transient time. Connecting an accumulator with 1 liter volume can result in 50%
reduction in rod side pressure variation. The model has been used to predict the PWM parameters as well. It has been found that
the most suitable parameters for this valve are 20 Hz and duty cycle from 12 to 65%. These results encourage going further with
controlling a pneumatic position system using low-cost control valves and a simple controller.
Keywords: Pneumatic Control, PWM, On/Off Valves, Simscape, Matlab
Research and Development the Adaptive Control Model Using the Spectrometer De...theijes
In this paper there are consider the automatic adaptive control system, selected adaptive control system with the standard model.The mathematical model of adaptive control system with the etalon-model is developed. Constructed and researched mathematical model of adaptive system with a reference model using MathLab Simulink software.There are researched adaptive control system responds to various external influences.
Induction motors are work-horse of the industry and major element in energy conversion. The replacement of the existing non-adjustable speed drives with the modern variable frequency drives would save considerable amount of electricity. A proper control scheme for variable frequency drives can enhance the efficiency and performance of the drive. This paper attempt to provide a rigorous review of various control schemes for the induction motor control and provides critical analysis and guidelines for the future research work. A detailed study of sensor based control schemes and sensor-less control schemes has been investigated. The operation, advantages, and limitations of the various control schemes are highlighted and different types of optimization techniques have been suggested to overcome the limitations of control techniques.
This paper aims to design the pitch angle control based on proportional–integral–derivative (PID) controller combined with fuzzy logic for small-scale wind turbine systems. In this control system, the pitch angle is controlled by the PID controller with their parameter is tuned by the fuzzy logic controller. This control system can compensate for the nonlinear characteristic of the pitch angle and wind speed. A comparison between the fuzzy-PID-controller with the conventional PID controller is carried out. The effectiveness of the method is determined by the simulation results of a small wind turbine using a permanent magnet generator (PMSG).
A Brief Research On Turbojet Engine By Using MPM 20IJERA Editor
Small turbojet engines represent a special class of turbine driven engines. They are suitable for scientific
purposes and research of certain thermodynamic processes ongoing in turbojet engines. Moreover such engines
can be used for research in the area of alternative fuels and new methods of digital control and measurement.
Our research, which is also presented in this article, is headed toward these aims. We evaluate and propose a
system of digital measurement of a particular small turbojet engine – MPM 20. Such engine can be considered
as highly non-linear large scale system. According to obtained data and experiments we propose different model
models of the engine and design of situational control algorithms for the engine with use of certain methods of
artificial intelligence as new methods of control and modeling of large scale systems.
Parametric study of a low cost pneumatic system controlled by onoff solenoid ...eSAT Journals
Abstract Expensive proportional valves are dominantly used in pneumatic positioning systems even with low demanding accuracy
positioning tasks, which deprive pneumatic systems from its economical advantages. Thereby, using low cost on/off solenoid
valves instead of proportional valves has been a topic of research in the last decades. In this paper, a parametric study is
conducted to investigate the effect of using low-cost 3/2 internally pilot on/off solenoid valves to control a double acting cylinder
and study the system nonlinear response to on/off and PWM input signal. Matlab ® Simscape library is used to model and
simulate the system. The model is validated though experimental measurements of the system behavior. The model is used to study
and decrease the nonlinear pressure response associated with the cylinder chambers in addition to the evaluation of the dead
zone and operating range of the on/off solenoid valve when operated with PWM signal. The results show that using a meter-in
flow control and having a near constant cylinder back pressure can reduce the nonlinearity. An orifice of 1e-6 m2 can reduce the
pressure variation by 80% but increase the transient time. Connecting an accumulator with 1 liter volume can result in 50%
reduction in rod side pressure variation. The model has been used to predict the PWM parameters as well. It has been found that
the most suitable parameters for this valve are 20 Hz and duty cycle from 12 to 65%. These results encourage going further with
controlling a pneumatic position system using low-cost control valves and a simple controller.
Keywords: Pneumatic Control, PWM, On/Off Valves, Simscape, Matlab
Research and Development the Adaptive Control Model Using the Spectrometer De...theijes
In this paper there are consider the automatic adaptive control system, selected adaptive control system with the standard model.The mathematical model of adaptive control system with the etalon-model is developed. Constructed and researched mathematical model of adaptive system with a reference model using MathLab Simulink software.There are researched adaptive control system responds to various external influences.
Induction motors are work-horse of the industry and major element in energy conversion. The replacement of the existing non-adjustable speed drives with the modern variable frequency drives would save considerable amount of electricity. A proper control scheme for variable frequency drives can enhance the efficiency and performance of the drive. This paper attempt to provide a rigorous review of various control schemes for the induction motor control and provides critical analysis and guidelines for the future research work. A detailed study of sensor based control schemes and sensor-less control schemes has been investigated. The operation, advantages, and limitations of the various control schemes are highlighted and different types of optimization techniques have been suggested to overcome the limitations of control techniques.
The maintenance cost of wind farms is one of the major factors influencing the prof- itability of wind projects. During preventive maintenance, the shutdown of wind turbines results in downtime wind energy losses. Appropriate determination of when to perform maintenance and which turbine(s) to maintain can reduce the overall downtime losses sig- nificantly. This paper uses a wind farm power generation model to evaluate downtime energy losses during preventive maintenance for a given group of wind turbines in the en- tire array. Wakes effects are taken into account to accurately estimate energy production over a specified time period. In addition to wind condition, the influence of wake effects is a critical factor in determining the selection of turbine(s) under maintenance. To min- imize the overall downtime loss of an offshore wind farm due to preventive maintenance, an optimal scheduling problem is formulated that selects the maintenance time of each turbine. Weather conditions are imposed as constraints to ensure the safety of mainte- nance personnel, transportation, and tooling infrastructure. A genetic algorithm is used to solve the optimal scheduling problem. The maintenance scheduling is optimized for a utility-scale offshore wind farm with 25 turbines. The optimized schedule not only reduces the overall downtime loss by selecting the maintenance dates when wind speed is low, but also considers the wake effects among turbines. Under given wind direction, the turbines under maintenance are usually the ones that can generate strong wake effects on others during certain wind conditions, or the ones that generate relatively less power being under excessive wake effects.
A coordinated mimo control design for a power plant using improved sliding mo...ISA Interchange
For the participation of the steam power plants in regulating the network frequency, boilers and turbines should be co-ordinately controlled in addition to the base load productions. Lack of coordinated control over boiler–turbine may lead to instability; oscillation in producing power and boiler parameters; reduction in the reliability of the unit; and inflicting thermodynamic tension on devices. This paper proposes a boiler–turbine coordinated multivariable control system based on improved sliding mode controller (ISMC). The system controls two main boiler–turbine parameters i.e., the turbine revolution and superheated steam pressure of the boiler output. For this purpose, a comprehensive model of the system including complete and exact description of the subsystems is extracted. The parameters of this model are determined according to our case study that is the 320 MW unit of Islam-Abad power plant in Isfahan/Iran. The ISMC method is simulated on the power plant and its performance is compared with the related real PI (proportional-integral) controllers which have been used in this unit. The simulation results show the capability of the proposed controller system in controlling local network frequency and superheated steam pressure in the presence of load variations and disturbances of boiler.
Automatic power generation control structure for smart electrical power gridseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Controlling a DC Motor through Lypaunov-like Functions and SAB TechniqueIJECEIAES
In this paper, state adaptive backstepping and Lyapunov-like function methods are used to design a robust adaptive controller for a DC motor. The output to be controlled is the motor speed. It is assumed that the load torque and inertia moment exhibit unknown but bounded time-varying behavior, and that the measurement of the motor speed and motor current are corrupted by noise. The controller is implemented in a Rapid Control Prototyping system based on Digital Signal Processing for dSPACE platform and experimental results agree with theory.
Optimization of Modified Sliding Mode Controller for an Electro-hydraulic Act...IJECEIAES
This paper presents the design of the modified sliding mode controller (MSMC) for the purpose of tracking the nonlinear system with mismatched disturbance. Provided that the performance of the designed controller depends on the value of control parameters, gravitational search algorithm (GSA), and particle swarm optimization (PSO) techniques are used to optimize these parameters in order to achieve a predefined system’s performance. In respect of system’s performance, it is evaluated based on the tracking error present between reference inputs transferred to the system and the system output. This is followed by verification of the efficiency of the designed controller in simulation environment under various values, with and without the inclusion of external disturbance. It can be seen from the simulation results that the MSMC with PSO exhibits a better performance in comparison to the performance of the similar controller with GSA in terms of output response and tracking error.
In this paper, Reduced-Order Observer For Real-Time Implementation Speed Sensorless Control of Induction Using RT-LAB Softwareis presented. Speed estimation is performed through a reduced-order observer. The stability of the proposed observer is proved based on Lyapunov’s theorem. The model is initially built offline using Matlab/Simulink and implemented in real-time environment using RT-LAB package and an OP5600 digital simulator. RT-LAB configuration has two main subsystems master and console subsystems. These two subsystems were coordinated to achieve the real-time simulation. In order to verify the feasibility and effectiveness of proposed method, experimental results are presented over a wide speed range, including zero speed.
In this study, a control strategy is presented to control the position and the feed rate of a table of a milling
machine powered by three-phase induction motor, when machining pieces constituted by different types of
materials: steel, brass and nylon. For development of the control strategy, the vector control technique was
applied to drive the three-phase induction machines. The estimation of the electromagnetic torque of the
motor was used to determine the machining feed rate for each type of material. The speed control was
developed using fuzzy logic Takagi-Sugeno (TS) model and the estimation of the electromagnetic torque
using the artificial neural network (ANN) of the least mean square (LMS) algorithm type. The induction
motor was fed by a three-phase voltage inverter hardware driven by a digital signal processor (DSP).
Experimental results are presented.
This research proposes the control system structure for a small-scale wind turbine. Significantly, the maximum power point tracking algorithm (MPPT) and the pitch angle controller are deeply analyzed; this is the base for proposing the strategy of the MPPT algorithm combined with pitch-angle control in a wide speed range of wind. This article also researches the converters, then analyses the advantages of each converter to choose the suitable converter for the small-scale wind turbine. In the MPPT algorithm design, the expert experience takes advantage through the fuzzy controller. The pitch angle controller is built based on the PID controller with its parameters adjusted by Fuzzy logic. The results showed that the effectiveness of the proposed control strategy is much better than that of the traditional control strategy. Moreover, in high and low wind speeds, the proposed control system operates reliably and stably.
Power System Contingency Ranking Using Fast Decoupled Load Flow Methodpaperpublications3
Abstract: Voltage instability is the phenomena associated with heavily loaded power systems. It is normally aggravated due to large disturbance. The Power system security is one of the significant aspects, where the proper action needs to be taken for the unseen contingency. In the event of contingency, the most serious threat to operation and control of power system is insecurity. Therefore, the contingency analysis is a key for the power system security. The contingency ranking using the performance index is a method for the line outages in a power system, which ranks the highest performance index line first and proceeds in a descending manner based on the calculated PI for all the line outages. This helps to take the prior action to keep the system secure. In this paper Fast Decoupled power flow method is used for the power system contingency ranking for the line outage based on the Active power and Voltage performance index. The ranking is given by considering the overall performance index, which is the summation of Active power and voltage performance index. The proposed method is implemented on a IEEE-14 bus system.
Adaptive Control Machining systems,Adaptive Control,Where to use adaptive control? Application:Sources of variability in machining,Types of Adaptive controls,Operation of ACC system,Relationship of AC software to APT program,Benefits of AC
The maintenance cost of wind farms is one of the major factors influencing the prof- itability of wind projects. During preventive maintenance, the shutdown of wind turbines results in downtime wind energy losses. Appropriate determination of when to perform maintenance and which turbine(s) to maintain can reduce the overall downtime losses sig- nificantly. This paper uses a wind farm power generation model to evaluate downtime energy losses during preventive maintenance for a given group of wind turbines in the en- tire array. Wakes effects are taken into account to accurately estimate energy production over a specified time period. In addition to wind condition, the influence of wake effects is a critical factor in determining the selection of turbine(s) under maintenance. To min- imize the overall downtime loss of an offshore wind farm due to preventive maintenance, an optimal scheduling problem is formulated that selects the maintenance time of each turbine. Weather conditions are imposed as constraints to ensure the safety of mainte- nance personnel, transportation, and tooling infrastructure. A genetic algorithm is used to solve the optimal scheduling problem. The maintenance scheduling is optimized for a utility-scale offshore wind farm with 25 turbines. The optimized schedule not only reduces the overall downtime loss by selecting the maintenance dates when wind speed is low, but also considers the wake effects among turbines. Under given wind direction, the turbines under maintenance are usually the ones that can generate strong wake effects on others during certain wind conditions, or the ones that generate relatively less power being under excessive wake effects.
A coordinated mimo control design for a power plant using improved sliding mo...ISA Interchange
For the participation of the steam power plants in regulating the network frequency, boilers and turbines should be co-ordinately controlled in addition to the base load productions. Lack of coordinated control over boiler–turbine may lead to instability; oscillation in producing power and boiler parameters; reduction in the reliability of the unit; and inflicting thermodynamic tension on devices. This paper proposes a boiler–turbine coordinated multivariable control system based on improved sliding mode controller (ISMC). The system controls two main boiler–turbine parameters i.e., the turbine revolution and superheated steam pressure of the boiler output. For this purpose, a comprehensive model of the system including complete and exact description of the subsystems is extracted. The parameters of this model are determined according to our case study that is the 320 MW unit of Islam-Abad power plant in Isfahan/Iran. The ISMC method is simulated on the power plant and its performance is compared with the related real PI (proportional-integral) controllers which have been used in this unit. The simulation results show the capability of the proposed controller system in controlling local network frequency and superheated steam pressure in the presence of load variations and disturbances of boiler.
Automatic power generation control structure for smart electrical power gridseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Controlling a DC Motor through Lypaunov-like Functions and SAB TechniqueIJECEIAES
In this paper, state adaptive backstepping and Lyapunov-like function methods are used to design a robust adaptive controller for a DC motor. The output to be controlled is the motor speed. It is assumed that the load torque and inertia moment exhibit unknown but bounded time-varying behavior, and that the measurement of the motor speed and motor current are corrupted by noise. The controller is implemented in a Rapid Control Prototyping system based on Digital Signal Processing for dSPACE platform and experimental results agree with theory.
Optimization of Modified Sliding Mode Controller for an Electro-hydraulic Act...IJECEIAES
This paper presents the design of the modified sliding mode controller (MSMC) for the purpose of tracking the nonlinear system with mismatched disturbance. Provided that the performance of the designed controller depends on the value of control parameters, gravitational search algorithm (GSA), and particle swarm optimization (PSO) techniques are used to optimize these parameters in order to achieve a predefined system’s performance. In respect of system’s performance, it is evaluated based on the tracking error present between reference inputs transferred to the system and the system output. This is followed by verification of the efficiency of the designed controller in simulation environment under various values, with and without the inclusion of external disturbance. It can be seen from the simulation results that the MSMC with PSO exhibits a better performance in comparison to the performance of the similar controller with GSA in terms of output response and tracking error.
In this paper, Reduced-Order Observer For Real-Time Implementation Speed Sensorless Control of Induction Using RT-LAB Softwareis presented. Speed estimation is performed through a reduced-order observer. The stability of the proposed observer is proved based on Lyapunov’s theorem. The model is initially built offline using Matlab/Simulink and implemented in real-time environment using RT-LAB package and an OP5600 digital simulator. RT-LAB configuration has two main subsystems master and console subsystems. These two subsystems were coordinated to achieve the real-time simulation. In order to verify the feasibility and effectiveness of proposed method, experimental results are presented over a wide speed range, including zero speed.
In this study, a control strategy is presented to control the position and the feed rate of a table of a milling
machine powered by three-phase induction motor, when machining pieces constituted by different types of
materials: steel, brass and nylon. For development of the control strategy, the vector control technique was
applied to drive the three-phase induction machines. The estimation of the electromagnetic torque of the
motor was used to determine the machining feed rate for each type of material. The speed control was
developed using fuzzy logic Takagi-Sugeno (TS) model and the estimation of the electromagnetic torque
using the artificial neural network (ANN) of the least mean square (LMS) algorithm type. The induction
motor was fed by a three-phase voltage inverter hardware driven by a digital signal processor (DSP).
Experimental results are presented.
This research proposes the control system structure for a small-scale wind turbine. Significantly, the maximum power point tracking algorithm (MPPT) and the pitch angle controller are deeply analyzed; this is the base for proposing the strategy of the MPPT algorithm combined with pitch-angle control in a wide speed range of wind. This article also researches the converters, then analyses the advantages of each converter to choose the suitable converter for the small-scale wind turbine. In the MPPT algorithm design, the expert experience takes advantage through the fuzzy controller. The pitch angle controller is built based on the PID controller with its parameters adjusted by Fuzzy logic. The results showed that the effectiveness of the proposed control strategy is much better than that of the traditional control strategy. Moreover, in high and low wind speeds, the proposed control system operates reliably and stably.
Power System Contingency Ranking Using Fast Decoupled Load Flow Methodpaperpublications3
Abstract: Voltage instability is the phenomena associated with heavily loaded power systems. It is normally aggravated due to large disturbance. The Power system security is one of the significant aspects, where the proper action needs to be taken for the unseen contingency. In the event of contingency, the most serious threat to operation and control of power system is insecurity. Therefore, the contingency analysis is a key for the power system security. The contingency ranking using the performance index is a method for the line outages in a power system, which ranks the highest performance index line first and proceeds in a descending manner based on the calculated PI for all the line outages. This helps to take the prior action to keep the system secure. In this paper Fast Decoupled power flow method is used for the power system contingency ranking for the line outage based on the Active power and Voltage performance index. The ranking is given by considering the overall performance index, which is the summation of Active power and voltage performance index. The proposed method is implemented on a IEEE-14 bus system.
Adaptive Control Machining systems,Adaptive Control,Where to use adaptive control? Application:Sources of variability in machining,Types of Adaptive controls,Operation of ACC system,Relationship of AC software to APT program,Benefits of AC
Monitoring wind turbine using wi fi network for reliable communicationeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Cuckoo search algorithm based for tunning both PI and FOPID controllers for ...IJECEIAES
Wind Energy has received great attention in this century. It influences the new power systems, adding new challenges to the power system expansion problem. Nowadays, double feed induction generator (DFIG) wind turbines are used majorly in wind farms, due to their advantages over other types. Therefore, the analysis of the system using this type has become very important. In this paper, a wind turbine modelling was introduced with suggested controllers, in order to enhance the system response, with respect to both pitch control and maximum output power. Cuckoo search algorithm (CSA), a meta-heuristic optimization technique, was implemented to determine the gains of a proportional-integral (PI) controller and fractional order proportional-integral-derivative (FOPID) controller to optimize the system, which considered three control loops: pitch, rotor-side converter, and grid-side converter control loop. Simulation results were determined using MATLAB/Simulink. The comparative analysis of the results showed that the PI Controller gave the simplest and the best response in case of the pitch and rotor-side control loops while the FOPID was the best when applied to the grid-side control loop. Based on the results and discussion, a suggestion of using a compination of each controller was introduced.
A transition from manual to Intelligent Automated power system operation -A I...IJECEIAES
This paper reviews the transition of the power system operation from the traditional manual mode of power system operations to the level where automation using Internet of Things (IOT) and intelligence using Artificial Intelligence (AI) is implemented. To make the review paper brief only indicative papers are chosen to cover multiple power system operation based implementation. Care is taken there is lesser repeatation of similar technology or application be reviewed. The indicative review is to take only a representative literature to bypass scrutinizing multiple literatures with similar objectives and methods. A brief review of the slow transition from the traditional to the intelligent automated way of carrying out power system operations like the energy audit, load forecasting, fault detection, power quality control, smart grid technology, islanding detection, energy management etc is discussed .The Mechanical Engineering Perspective on the basis of applications would be noticed in the paper although the energy management and power delivery concepts are electrical.
Permanent magnet direct current motors (PMDCM) are widely used in various applications such as space technologies, personal computers, medical, military, robotics, electrical vehicles, etc. In this paper, the mathematical model of PMDCM is designed and simulated using MATLAB software. The PMDCM speed is controlled using rate feedback controller due to its ability of improving system damping. To improve the controller performance, it’s parameters are tuned using genetic algorithm (GA) and direct search (DS) techniques. The tuning process based on different performance criteria. The most four common performance criteria used in this paper are JIAE (Integral of Absolute Error), JISE (Integral of Square Error), JITAE (Integral of Time-Weighted Absolute Error), and JITSE (Integral of Time-Weighted Square Error). The results obtained from these evolutionary techniques are compared. The results show an obvious improvement in system performance including enhancing the transient and steady state of PMDCM speed responses for all performance criteria.
Sensor fault reconstruction for wind turbine benchmark model using a modified...IJECEIAES
This paper proposes a fault diagnosis scheme applied to a wind turbine system. The technique used is based on a modified sliding mode observer (SMO), which permits the reconstruction of actuator and sensor faults. A wind turbine benchmark with a real sequence of wind speed is exploited to validate the proposed fault detection and diagnosis scheme. Rotor speed, generator speed, blade pitch angle, and generator torque have different orders of magnitude. As a result, the dedicated sensors are susceptible to faults of quite varying magnitudes, and estimating simultaneous sensor faults with accuracy using a classical SMO is difficult. To address this issue, some modifications are made to the classic SMO. In order to test the efficiency of the modified SMO, several sensor fault scenarios have been simulated, first in the case of separate faults and then in the case of simultaneous faults. The simulation results show that the sensor faults are isolated, detected, and reconstructed accurately in the case of separate faults. In the case of simultaneous faults, with the proposed modification of SMO, the faults are precisely isolated, detected, and reconstructed, even though they have quite different amplitudes; thus, the relative gap does not exceed 0.08% for the generator speed sensor fault.
Risk assessment of power system transient instability incorporating renewabl...IJECEIAES
Transient stability affected by renewable energy sources integration due to reductions of system inertia and uncertainties associated with the expected generation. The ability to manage relation between the available big data and transient stability assessment (TSA) enables fast and accurate monitoring of TSA to prepare the required actions for secure operation. This work aims to build a predictive model using Gaussian process regression for online TSA utilizing selected features. The critical fault clearing time (CCT) is used as TSA index. The selected features map the system dynamics to reduce the burden of data collection and the computation time. The required data were collected offline from power flow calculations at different operating conditions. Therefore, CCT was calculated using electromagnetic transient simulation at each operating point by applying self-clearance three phase short circuit at prespecified locations. The features selection was implemented using the neighborhood component analysis, the Minimum Redundancy Maximum Relevance algorithm, and K-means clustering algorithm. The vulnerability of selected features tends to result great variation on the best features from the three methods. Hybrid collection of the best common features was used to enhance the TSA by refining the final selected features. The proposed model was investigated over 66-bus system.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
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Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4
Q034301190123
1. International Journal of Computational Engineering Research||Vol, 03||Issue, 4||
www.ijceronline.com ||April||2013|| Page 119
SCADA System Monitoring wind Turbine Using Random Forest
Algorithm
S.Mahalakshmi
1,
ME[CSE] IFET College of Engineering,Villupuram
I. INTRODUCTION
Wind energy is regarded as a major renewable resource destined to grow in importance in the decades
to come. The expansion of wind farms makes their operations and maintenance (O&M) an important issue. It is
not unusual for the maintenance/repair cost of wind turbine components to exceed their procurement cost [1],
[2]. According to the data presented in [3], maintenance cost alone may account for at least 10% of the total
generation cost. To address O&M issues, traditional maintenance practices such as periodic and corrective
maintenance are being replaced with condition-based monitoring and maintenance. State-of-the-art condition
maintenance applications in the wind industry are discussed in [4]–[7]. Condition-based monitoring approaches
continuously monitor the performance of wind turbine components with installed sensors and equipment.
Vibration analysis [8], optical strain measurements [9], and oil particle.
Framework of the proposed approach
The Analysis is commonly used in condition monitoring. Performance monitoring is another promising
approach that closely resembles condition monitoring. It utilizes historical wind turbine data to predict wind
turbine performance Parameters such as gearbox oil temperature, and tower acceleration. Performance
monitoring is a cost-effective approach to analyze wind turbine performance as the Supervisory Control and
Data Acquisition (SCADA) system records various wind turbine parameters that could be fault informative.
Data mining has been used as a viable approach to performance monitoring of wind turbines. Related data-
mining algorithm applications include fault diagnosis [1], [11], modeling of abnormal behavior [12], [13], and
power curve monitoring [14]. Other related research includes identification of status patterns of wind turbines,
in which the authors employed association rule mining to identify patterns within the individual statuses. Here
the term status represents a potential fault. In reference, the authors employed an adaptive control strategy to
gain maximum power and minimum torque ramp. Considering the role of converters in optimizing wind turbine
performance, a stream of research has focused on reliability assessment of wind turbines. The research reported
in this paper utilized data-mining algorithms to predict wind turbine states.
ABSTRACT
The rapid expansion of wind farms has generated interest in operations and maintenance. An
operating wind turbine undergoes various state changes, including transformation from a normal to a
fault mode. Condition-based maintenance tools are needed to identify potential faults in the system. The
prediction of turbine fault modes is of particular interest. In this proposed system, The various
algorithms are employed to construct prediction models for wind turbine faults. A three-stage prediction
process is followed: 1) prediction of a fault of any kind; 2) prediction of specific faults of the system; and
3) identification on unseen faults. A comparative analysis of various algorithms is reported based on the
data collected at a large wind farm. Random forest algorithm models provided the best accuracy among
all algorithm tested. The robustness of the predictive model is validated for faults that have occurred at
turbines with previously unseen data.
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II. MODELS FOR MONITORING WIND TURBINE STATES
The framework for building prediction models is provided in Fig. 1. An abstraction of turbine states is
used to categorize the output data into a number of states using expert knowledge. Model building involves
using various data-mining algorithms. The models are then tested. The generated dataset is used to construct
models for Phase-I and Phase-II predictions. The main objective of objective of Phase-I is to predict a fault of
any kind, whereas, predictions in Phase-II target specific faults. In Phase-III predictions, unseen faults from
different wind turbines are identified. Descriptions of various wind turbine states are provided next.
2.1 RELATED WORKS
A. Turbine State Description
The variability of wind speed impacts the performance of wind turbines and is recorded as fault state.
Normal operations, weather-related downtime, maintenance downtime, fault mode, and emergency stop are
some of the many states recorded by the SCADA system of a wind turbine. States changes may vary from
insignificant (e.g., when a turbine is changing its state from idle to normal operations) to a potential fault. Table
I lists the 17 possible states of a wind turbine. State number 17 represents the fault mode of wind turbines and
there can be more than 400 possible ways in which a wind turbine can be faulted. Gearbox oil over-temperature,
blade angle asymmetry, pitch thruster fault, and yaw runaway are some of the common fault modes of a wind
turbine.
B. Abstraction of Turbine States
A typical turbine may undergo a number of different states including turbine normal operations, run-up
idling, maintenance/repair mode, fault mode, weather downtime, etc. The prediction of a turbine’s fault mode is
of particular interest as it represents some potential fault in the system. A turbine in state 17 can be affected by
as many as 400 different fault modes of varying intensity. The histogram of 17 wind turbine based on the
frequency of fault mode, turbine 12 was considered in the analysis. In order to reduce the computational effort
required by data-mining algorithms, the recorded states of wind turbines were further categorized using domain
knowledge. Table II represents the initially recorded and categorized states of turbine 12. The initial 44 turbine
states were categorized into four states: Turbine OK, Fault, Weather downtime, and Maintenance downtime. The
Turbine OK category and comparison of wind turbine using to
Comparison of wind turbines states
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Corresponds to normal functioning operations, including run-up idling, whereas, the Fault category
corresponds to an actual or potential fault in the system. The Weather downtime category corresponds to turbine
downtime due to poor weather conditions, whereas, any other downtime is considered as Maintenance
downtime.
C. Learning Strategy
For both prediction phases, the dataset was divided into two parts, i.e., initial dataset and blind dataset.
The data-mining algorithms used two-thirds of the initial data for training, and the remaining one-third of the
initial data was used for testing. The performance of the data-mining algorithms on the test dataset was used for
algorithm selection. The best performing algorithm was then used to construct prediction models on the blind
dataset. Details regarding the parameter selection are discussed in the next section.
D. Parameter Selection: A Supervisory Control and Data Acquisition (SCADA) system records more
broadly categorized into: 1) wind turbine performance parameters, 2) wind turbine control parameters, and 3)
wind turbine no controllable parameters. Parameters such as power, generator speed, and rotor speed are the
performance parameters, whereas, blade pitch angle and generator torque are controllable parameters. Wind
speed is the only no controllable parameter. In the research reported in this paper, a combination of turbine
performance parameters, control parameters, and no controllable parameters are used to predict the wind turbine
states. To minimize the data dimensionality and to remove irrelevant parameters, parameter selection algorithms
are used. A month of data was used for parameter selection and algorithm learning. A stratified subset of the
original data was used for parameter selection to make the process computationally efficient. The original and
stratified data. Distribution of the output class is preserved in stratified data to avoid bias towards any specific
class. Three different data-mining algorithms, wrapper with genetic search (WGS), wrapper with best first
search (WBFS) , and boosting tree algorithm (BTA) were selected to determine relevant parameters for
prediction of turbine states. Wrapper is a supervised learning approach using different search techniques to
select the relevant parameters by performing ten-fold cross validation. Table II lists the ten best parameters from
each parameter selection algorithm. Parameters for nacelle revolution, blade (1–3) pitch angle, current Phase C,
temperature hub, and generator/gearbox speed were finally selected to build the prediction model.
TABLE II
SELECTED PARAMETERS USING DATA-MINING ALGORITHMS
Evaluation Metric of wind turbines states
Parameters for nacelle revolution, blade (1–3) pitch angle, current Phase C, temperature hub, and
generator/gearbox speed were finally selected to build the prediction models.
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E. Evaluation Metric: The evaluation of data-mining algorithms is based on the prediction accuracy of each
output class. Geometric mean (glean) of the output class is used as criteria for selection data mining data mining
algorithms for the prediction task.
F. Algorithm Selection: Five data-mining algorithms: neural network (NN), support vector machine (SVM),
random forest algorithm (RFA), boosting tree algorithm (BTA), and general chi-square automatic interaction
detector (CHAID) algorithm were initially selected for building models at time stamp. NN uses back
propagation to classify instances . Twenty NN models with different kernels and structures were built in this
research, and the most accurate and robust model was selected. SVM constructs a hyper plane or set of hyper
planes in a high dimensional space, which can be used for classification, or regression. In SVM the hyper plane
with the largest distance to the nearest training data points of any class (so-called functional Margin), yields
good accuracy.
RFA is an ensemble learning method where multiple random trees are generated during classification.
It selects random input parameters for each node split . BTA generates multiple models and applies a weighted
combination of the predictions from individual models to derive a single prediction model CHAID is a tree-
based data-mining algorithm that performs multilevel splits for classification. The prediction accuracy for each
class (Phase-I predictions) Essentially, all the algorithms performed well while predicting Turbine OK and Fault
class, however, the output class Weather downtime and Maintenance downtime were predicted with relatively
low accuracy. The geometric mean metric indicates that when all classes are predicted with perfect accuracy its
value is 1. The algorithm with the highest value of was selected to build prediction models at different time
stamps. The Phase-I prediction results both the boosting tree algorithm and the random forest algorithms
outperformed the remaining three data-mining algorithms. However, RFA was selected to build the prediction
models, as it possesses great generalization ability and it is almost insensitive to the size of the dataset. The tree
complexity of the random forest algorithm as a function of the misclassification rate.
III. COMPUTATIONAL RESULTS
In this section, the random forest algorithm (RFA) was used to build eight prediction models at various
time stamps, with a maximum prediction length of 5 min. The maximum tree size for the random forest
algorithm was set to 300. The accuracy was found to be in the range of 81%–99% for all output classes. The
proposed approach involved three key steps: turbine state abstraction, algorithm learning, and state prediction.
The tree complexity of the random forest algorithm as a function of the misclassification rate. performance of
different data-mining algorithms using gmean a criterion(Phase-II prediction).
A. Phase-II Prediction
In this phase, output class Fault was replaced with the actual fault types, these being pitch overrun 0,
and pitch thyristor 2 faults, axle 1 fault pitch controller, and pulse sensor motor defect.
Performance of different data-mining algorithms using gmean a criterion(Phase-II prediction).
B. Phase-III Predictions
While the results on the testing dataset indicated the effectiveness of the random forest algorithm, in
order to validate the robustness of the proposed model, data from other fault-prone turbines were analyzed with
the additional objective of seeing how the model would respond to unseen states types. The accuracy for
correctly identifying unseen fault cases was found to be in the range of 60%–100%, except for faults related to
gearboxes (e.g., gearbox over-temperature, gearbox oil pressure too low) which were always identified Turbine
OK. The reasons for this include a lack of related input parameters (e.g., gearbox temperature, gearbox oil
pressure, etc.).
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Model analysis of Turbine14
Methodology for predicting wind turbine states was presented. The proposed approach involved three
key steps: turbine state abstraction, algorithm learning, and state prediction. In the first step, the initial wind
turbine states were separated into classes using domain knowledge. To reduce the computational effort, data-
mining algorithms were trained using a stratified data set. Turbine parameters such as the blade pitch angle,
generator/gearbox speed, temperature hub, and nacelle Revolution and current Phase C constitute the input to
the prediction model.
IV. CONCLUSION
Among the selected data-mining algorithms, the random forest algorithm provided the most accurate
results. Prediction models with up to 300-s horizons provided results with an accuracy in the range 78%–98%.
The proposed model also identified various faults that occurred at wind turbines not included in the training
data. Future research will involve further analysis once additional data becomes available. New concepts will be
researched to improve model robustness for identifying faults not reflected in the training data sets.
REFERENCES
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