This document summarizes a research paper that proposes a robust speed estimator for sensorless vector controlled induction motor drives. The authors develop a single neuron cascaded neural network model trained on input/output data to estimate rotor flux. This neural network model replaces the conventional voltage model in rotor flux-based model reference adaptive system (RF-MRAS) speed estimation. By using a neural network reference model, the proposed RF-MRAS speed estimator is robust to variations in stator resistance and does not require a separate online resistance estimator. Simulation results demonstrate the performance of the proposed robust RF-MRAS speed estimator works over a wide speed range including zero speed, and is more robust than the conventional RF-MRAS approach.
Induction Motors Faults Detection Based on Instantaneous Power Spectrum Analy...IDES Editor
A method of induction motor diagnostics based on
the analysis of three-phase instantaneous power spectra has
been offered. Its implementation requires recalculation of
induction motor voltages, aiming at exclusion from induction
motor instantaneous three-phase power signal the component
caused by supply mains dissymmetry and unsinusoidality. The
recalculation is made according to the motor known
electromagnetic parameters, taking into account the
electromotive force induced in stator winding by rotor currents.
The results of instantaneous power parameters computation
proved efficiency of this method in case of supply mains voltage
dissymmetry up to 20%. The offered method has been tested
by experiments. Its applicability for detection of several stator
and rotor winding defects appeared in motor simultaneously
has been proved. This method also makes it possible to
estimate the extent of defects development according to the
size of amplitudes of corresponding harmonics in the spectrum
of total three phase power signal.
FAULT DETECTION AND DIAGNOSIS OF INDUCTION MACHINE WITH ON-LINE PARAMETER PR...Sheikh R Manihar Ahmed
Today all instrumentation system pertaining to industrial process controls as well as domestic application involve automatic fault finding facility. This facility detects the faulty condition of the system and draws operator’s attention towards it enabling him to take suitable remedial action to ensure proper operation of the system. The main purpose of all FDI method is to monitor the system operations and in case of faults accommodate the source of faults so that timely corrective actions are taken. Fault detection simply involves a decision based on the monitored data as to whether there is a fault or the system is running normally. Fault isolation is then executed to identify the type and location of a fault after the fault detection has triggered an alarm so that corrective actions can be made. These two steps are known as Fault Detection and Isolation. Fault diagnosis is referred to as the combination of fault detection, identification and isolation. One such method of annunciation in which activation of visual or mechanical variable takes place when a removed switch or device has been activated as a result of fault in certain system, an audio alarm may also be associated with annunciations. This FDI system is defined and the existing technique to detect & isolate the fault with on-line parameter programming facility. The main advantage of the proposed approach of Control System based fault detection and isolation is its low cost. Low cost in terms of components used makes affordable in terms of easy handling and maintenance and various sensors can be used to give different types of input signals to circuit. An additional advantage is that the real time system still works when the host crashes, the matter that increases the reliability of the system & Data-logging facility can also be provided. A data-logger captures any measurement values which can be represented by a voltage. Nowadays, sensors and transducers are available for, practically, any physical quantity. The function of data-logger is to capture and store a specified number of specified number of sensor measurement values at predefined intervals and transfer the data including date and time to a PC in the form of file.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Induction Motors Faults Detection Based on Instantaneous Power Spectrum Analy...IDES Editor
A method of induction motor diagnostics based on
the analysis of three-phase instantaneous power spectra has
been offered. Its implementation requires recalculation of
induction motor voltages, aiming at exclusion from induction
motor instantaneous three-phase power signal the component
caused by supply mains dissymmetry and unsinusoidality. The
recalculation is made according to the motor known
electromagnetic parameters, taking into account the
electromotive force induced in stator winding by rotor currents.
The results of instantaneous power parameters computation
proved efficiency of this method in case of supply mains voltage
dissymmetry up to 20%. The offered method has been tested
by experiments. Its applicability for detection of several stator
and rotor winding defects appeared in motor simultaneously
has been proved. This method also makes it possible to
estimate the extent of defects development according to the
size of amplitudes of corresponding harmonics in the spectrum
of total three phase power signal.
FAULT DETECTION AND DIAGNOSIS OF INDUCTION MACHINE WITH ON-LINE PARAMETER PR...Sheikh R Manihar Ahmed
Today all instrumentation system pertaining to industrial process controls as well as domestic application involve automatic fault finding facility. This facility detects the faulty condition of the system and draws operator’s attention towards it enabling him to take suitable remedial action to ensure proper operation of the system. The main purpose of all FDI method is to monitor the system operations and in case of faults accommodate the source of faults so that timely corrective actions are taken. Fault detection simply involves a decision based on the monitored data as to whether there is a fault or the system is running normally. Fault isolation is then executed to identify the type and location of a fault after the fault detection has triggered an alarm so that corrective actions can be made. These two steps are known as Fault Detection and Isolation. Fault diagnosis is referred to as the combination of fault detection, identification and isolation. One such method of annunciation in which activation of visual or mechanical variable takes place when a removed switch or device has been activated as a result of fault in certain system, an audio alarm may also be associated with annunciations. This FDI system is defined and the existing technique to detect & isolate the fault with on-line parameter programming facility. The main advantage of the proposed approach of Control System based fault detection and isolation is its low cost. Low cost in terms of components used makes affordable in terms of easy handling and maintenance and various sensors can be used to give different types of input signals to circuit. An additional advantage is that the real time system still works when the host crashes, the matter that increases the reliability of the system & Data-logging facility can also be provided. A data-logger captures any measurement values which can be represented by a voltage. Nowadays, sensors and transducers are available for, practically, any physical quantity. The function of data-logger is to capture and store a specified number of specified number of sensor measurement values at predefined intervals and transfer the data including date and time to a PC in the form of file.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Low Speed Estimation of Sensorless DTC Induction Motor Drive Using MRAS with ...IJECEIAES
This paper presents a closed loop Model Reference Adaptive system (MRAS) observer with artificial intelligent Nuero fuzzy controller (NFC) as the adaptation technique to mitigate the low speed estimation issues and to improvise the performance of the Sensorless Direct Torque Controlled (DTC) Induction Motor Drives (IMD). Rotor flux MRAS and reactive power MRAS with NFC is explored and detailed analysis is carried out for low speed estimation. Comparative analysis between rotor flux MRAS and reactive power MRAS with PI as well as NFC as adaptive controller is performed and results are presented in this paper. The comparative analysis among these four speed estimation methods shows that reactive power MRAS with NFC as adaptation mechanism shows reduced speed estimation error and actual speed error at steady state operating conditions when the drive is subjected to low speed operation. Simulation carried out using MATLAB-Simulink software to validate the performance of the drive especially at low speeds with rated and variable load conditions.
Distance relay is mainly used for fault detection in the power system. But it cannot be used below 11kV. Hence an electronic smart energy meter is developed for the detection of the fault in the distribution system. It consists of a fault detection circuit and an IoT module. The fault detector senses the presence of a fault and give a command signal to the circuit breaker and also passes these fault signal to the operator. This proposed system will be a perfect solution for three major challenges faced in the distribution sector such as automatic fault isolation, information about the fault to the operator and exact location of the fault. The energy meter is capable of displaying the cost of the unit consumed according to their tariff. The variation in tariff can be controlled by an operator using IoT. The initial cost of the proposed system is comparatively higher than the traditional system. As a long term consideration, the cost of installation can be compensated by reducing the wastage of energy by making the consumer aware of the consumption details.
PERFORMANCE ANALYSIS OF ENERGY EFFICIENT SCALABLE HEIRARCHIAL PROTOCOL FOR HO...IAEME Publication
Wireless Sensor nodes connect the physical world to the digital world using smart,
tiny and self configured stand alone devices. These small devices offer pack of
brilliant opportunities to the digital world by capturing and revealing real time events
which later used as data cloud in numerous applications. With impressive
improvements in protocols, node level programming, simulation platforms and
middleware developments sensor nodes have become promising options in the
development of smart cities, gas and chemical industry, precision agriculture etc.
However, these industrial application demands more lifetime and faster-secure data
transmissions. In many applications it is recorded that with increase in network size
LEACH routing protocol functioning degenerate. Further, designing of a promising
routing protocol that can maintain less energy consumption during data gathering
and propagation leads to use of variety of approaches. This work is based on the
abstraction of equal distribution of energy among nodes with scalability.
Experimental results show commendable improvement in network lifespan with
residual energy of nodes to last for longer period. Throughput is also monitored
considering scalability.
FUZZY LOGIC APPROACH FOR FAULT DIAGNOSIS OF THREE PHASE TRANSMISSION LINEJournal For Research
Transmission line among the other electrical power system component suffers from unexpected failure due to various random causes. Because transmission line is quite large as it is open in environment. A fault occurs on transmission line when two or more conductors come in contact with each other or ground. This paper presents a proposed model based on MATLAB software to detect the fault on transmission line. Fault detection has been achieved by using Fuzzy Logic based intelligent control technique. The proposed method aims in presenting a fast and accurate fault diagnosis method to classify and identify the type of fault which occurs on a power transmission system. In this paper, some of the unconventional approaches for condition monitoring of power systems comprising of relay Breaker, along with the application of soft computing technique like fuzzy logic. Results show that the proposed methodology is efficient in identifying fault in transmission system.
Backpropagation Neural Network Modeling for Fault Location in Transmission Li...ijeei-iaes
In this topic research was provided about the backpropagation neural network to detect fault location in transmission line 150 kV between substation to substation. The distance relay is one of the good protective device and safety devices that often used on transmission line 150 kV. The disturbances in power system are used distance relay protection equipment in the transmission line. However, it needs more increasing large load and network systems are increasing complex. The protection system use the digital control, in order to avoid the error calculation of the distance relay impedance settings and spent time will be more efficient. Then backpropagation neural network is a computational model that uses the training process that can be used to solve the problem of work limitations of distance protection relays. The backpropagation neural network does not have limitations cause of the impedance range setting. If the output gives the wrong result, so the correct of the weights can be minimized and also the response of galat, the backpropagation neural network is expected to be closer to the correct value. In the end, backpropagation neural network modeling is expected to detect the fault location and identify operational output current circuit breaker was tripped it. The tests are performance with interconnected system 150 kV of Riau Region.
This presentation discuss about the possible signal processing applications for the future smart grid. Later I will discuss about the basics of digital signal processing techniques widely applied in smart grid applications.
speed control of three phase induction motor using IOTswaroop009
The main aim is to control and monitor the speed of the three-phase induction motor by using the Arduino, node MCU controller and 3-phase inverter circuits.
WAVELET- FUZZY BASED MULTI TERMINAL TRANSMISSION SYSTEM PROTECTION SCHEME IN ...ijfls
In This Paper, A New Protection Scheme In The Areas Of Accurate Fault Detection, Classification And
Location Estimation For Multi Terminal Transmission System Compensated With Statcom Is Proposed.
The Fault Indices Of All The Phases At All The Terminals Are Obtained By Analyzing The Detail
Coefficients Of Current Signals Through Bior 1.5 Mother Wavelet. The Complete Digital Simulation Of A
Transmission System With Statcom Is Performed Using Matlab /Simulink For Fault Detection,
Classification, And Faulty Terminal Identification With Variations In Fault Distance And Fault Inception
Angle For All Types Of Faults And Fuzzy Inference System Is Used To Estimate The Fault Location. The
Protection Scheme Yielded Accurate Results Within Half Cycle And Show That The Above Scheme Is
Suitable For Multi Terminal Transmission System With And Without Statcom Compensation.
Fuzzy-Logic-Controller-Based Fault Isolation in PWM VSI for Vector Controlled...iosrjce
IOSR Journal of Electrical and Electronics Engineering(IOSR-JEEE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electrical and electronics engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electrical and electronics engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The modern-day power grid aims at providing reliable and quality power, which requires careful monitoring of the power grid against catastrophic faults.
Therefore one promising way is to provide the system a wide protection and control named as “Wide Area Measurement and Control System” /PMU is required.
A Wavelet Based fault Detection of Induction Motor: A Reviewijsrd.com
This paper presents a review of the researches done on fault detection and tolerant control , main aim of the fault tolerant control and fault detection of induction motor is used the wavelet transform. Wavelet transform is much better tool for the fault diagnosis point of view and a overview of the wavelet types (continuous and discrete), machine faults detection methods and their validation. The software, generality of codes, one dimensional and two dimensional DWT and frequency characteristics components of healthy as well as faulty induction motor has explained. So Finally, stator short winding , shaft fault, bearing fault ,rotor broken bar and open winding are taken as a case study to show the better diagnosis of fault by using wavelet techniques.
Diagnosis of broken bars fault in induction machines using higher order spect...ISA Interchange
Detection and identification of induction machine faults through the stator current signal using higher order spectra analysis is presented. This technique is known as motor current signature analysis (MCSA). This paper proposes two higher order spectra techniques, namely the power spectrum and the slices of bi-spectrum used for the analysis of induction machine stator current leading to the detection of electrical failures within the rotor cage. The method has been tested by using both healthy and broken rotor bars cases for an 18.5 kW-220 V/380 V-50 Hz-2 pair of poles induction motor under different load conditions. Experimental signals have been analyzed highlighting that bi-spectrum results show their superiority in the accurate detection of rotor broken bars. Even when the induction machine is rotating at a low level of shaft load (no-load condition), the rotor fault detection is efficient. We will also demonstrate through the analysis and experimental verification, that our proposed proposed-method has better detection performance in terms of receiver operation characteristics (ROC) curves and precision-recall graph.
Emission characteristics of a diesel engine using soyabean oil and diesel blendseSAT Journals
Abstract Diesel engines have been playing a vital role in the transportation and power generation sectors since from its invention. Despite of having better efficiency with diesel engine, the main concern is on emission of pollutants. There are various methods to reduce pollutant emission from a diesel engine. The prominent way to reduce pollutants is the usages of bio fuels with some modifications in the diesel engine. Diesel engine simulation models can be used to understand the combustion performance, prediction of emission concentration. These models can reduce the number of experiments. In this study, the performance and emissions characteristics of single cylinder, four stroke, and direct injection diesel engine operating on diesel and soybean blends have been investigated theoretically. The variations of various species concentration like CO2,CO and NOx with equivalence ratio have been analysed using diesel engine simulation models. These models can reduce the number of experiments. Computer simulation has contributed enormously towards new evaluation in the field of internal combustion engines. Mathematical tools have become very popular in recent years owing to the continuously increasing improvement in computational power. Index terms: Emissions, Bio fuels, Simulation Models
Low Speed Estimation of Sensorless DTC Induction Motor Drive Using MRAS with ...IJECEIAES
This paper presents a closed loop Model Reference Adaptive system (MRAS) observer with artificial intelligent Nuero fuzzy controller (NFC) as the adaptation technique to mitigate the low speed estimation issues and to improvise the performance of the Sensorless Direct Torque Controlled (DTC) Induction Motor Drives (IMD). Rotor flux MRAS and reactive power MRAS with NFC is explored and detailed analysis is carried out for low speed estimation. Comparative analysis between rotor flux MRAS and reactive power MRAS with PI as well as NFC as adaptive controller is performed and results are presented in this paper. The comparative analysis among these four speed estimation methods shows that reactive power MRAS with NFC as adaptation mechanism shows reduced speed estimation error and actual speed error at steady state operating conditions when the drive is subjected to low speed operation. Simulation carried out using MATLAB-Simulink software to validate the performance of the drive especially at low speeds with rated and variable load conditions.
Distance relay is mainly used for fault detection in the power system. But it cannot be used below 11kV. Hence an electronic smart energy meter is developed for the detection of the fault in the distribution system. It consists of a fault detection circuit and an IoT module. The fault detector senses the presence of a fault and give a command signal to the circuit breaker and also passes these fault signal to the operator. This proposed system will be a perfect solution for three major challenges faced in the distribution sector such as automatic fault isolation, information about the fault to the operator and exact location of the fault. The energy meter is capable of displaying the cost of the unit consumed according to their tariff. The variation in tariff can be controlled by an operator using IoT. The initial cost of the proposed system is comparatively higher than the traditional system. As a long term consideration, the cost of installation can be compensated by reducing the wastage of energy by making the consumer aware of the consumption details.
PERFORMANCE ANALYSIS OF ENERGY EFFICIENT SCALABLE HEIRARCHIAL PROTOCOL FOR HO...IAEME Publication
Wireless Sensor nodes connect the physical world to the digital world using smart,
tiny and self configured stand alone devices. These small devices offer pack of
brilliant opportunities to the digital world by capturing and revealing real time events
which later used as data cloud in numerous applications. With impressive
improvements in protocols, node level programming, simulation platforms and
middleware developments sensor nodes have become promising options in the
development of smart cities, gas and chemical industry, precision agriculture etc.
However, these industrial application demands more lifetime and faster-secure data
transmissions. In many applications it is recorded that with increase in network size
LEACH routing protocol functioning degenerate. Further, designing of a promising
routing protocol that can maintain less energy consumption during data gathering
and propagation leads to use of variety of approaches. This work is based on the
abstraction of equal distribution of energy among nodes with scalability.
Experimental results show commendable improvement in network lifespan with
residual energy of nodes to last for longer period. Throughput is also monitored
considering scalability.
FUZZY LOGIC APPROACH FOR FAULT DIAGNOSIS OF THREE PHASE TRANSMISSION LINEJournal For Research
Transmission line among the other electrical power system component suffers from unexpected failure due to various random causes. Because transmission line is quite large as it is open in environment. A fault occurs on transmission line when two or more conductors come in contact with each other or ground. This paper presents a proposed model based on MATLAB software to detect the fault on transmission line. Fault detection has been achieved by using Fuzzy Logic based intelligent control technique. The proposed method aims in presenting a fast and accurate fault diagnosis method to classify and identify the type of fault which occurs on a power transmission system. In this paper, some of the unconventional approaches for condition monitoring of power systems comprising of relay Breaker, along with the application of soft computing technique like fuzzy logic. Results show that the proposed methodology is efficient in identifying fault in transmission system.
Backpropagation Neural Network Modeling for Fault Location in Transmission Li...ijeei-iaes
In this topic research was provided about the backpropagation neural network to detect fault location in transmission line 150 kV between substation to substation. The distance relay is one of the good protective device and safety devices that often used on transmission line 150 kV. The disturbances in power system are used distance relay protection equipment in the transmission line. However, it needs more increasing large load and network systems are increasing complex. The protection system use the digital control, in order to avoid the error calculation of the distance relay impedance settings and spent time will be more efficient. Then backpropagation neural network is a computational model that uses the training process that can be used to solve the problem of work limitations of distance protection relays. The backpropagation neural network does not have limitations cause of the impedance range setting. If the output gives the wrong result, so the correct of the weights can be minimized and also the response of galat, the backpropagation neural network is expected to be closer to the correct value. In the end, backpropagation neural network modeling is expected to detect the fault location and identify operational output current circuit breaker was tripped it. The tests are performance with interconnected system 150 kV of Riau Region.
This presentation discuss about the possible signal processing applications for the future smart grid. Later I will discuss about the basics of digital signal processing techniques widely applied in smart grid applications.
speed control of three phase induction motor using IOTswaroop009
The main aim is to control and monitor the speed of the three-phase induction motor by using the Arduino, node MCU controller and 3-phase inverter circuits.
WAVELET- FUZZY BASED MULTI TERMINAL TRANSMISSION SYSTEM PROTECTION SCHEME IN ...ijfls
In This Paper, A New Protection Scheme In The Areas Of Accurate Fault Detection, Classification And
Location Estimation For Multi Terminal Transmission System Compensated With Statcom Is Proposed.
The Fault Indices Of All The Phases At All The Terminals Are Obtained By Analyzing The Detail
Coefficients Of Current Signals Through Bior 1.5 Mother Wavelet. The Complete Digital Simulation Of A
Transmission System With Statcom Is Performed Using Matlab /Simulink For Fault Detection,
Classification, And Faulty Terminal Identification With Variations In Fault Distance And Fault Inception
Angle For All Types Of Faults And Fuzzy Inference System Is Used To Estimate The Fault Location. The
Protection Scheme Yielded Accurate Results Within Half Cycle And Show That The Above Scheme Is
Suitable For Multi Terminal Transmission System With And Without Statcom Compensation.
Fuzzy-Logic-Controller-Based Fault Isolation in PWM VSI for Vector Controlled...iosrjce
IOSR Journal of Electrical and Electronics Engineering(IOSR-JEEE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electrical and electronics engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electrical and electronics engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The modern-day power grid aims at providing reliable and quality power, which requires careful monitoring of the power grid against catastrophic faults.
Therefore one promising way is to provide the system a wide protection and control named as “Wide Area Measurement and Control System” /PMU is required.
A Wavelet Based fault Detection of Induction Motor: A Reviewijsrd.com
This paper presents a review of the researches done on fault detection and tolerant control , main aim of the fault tolerant control and fault detection of induction motor is used the wavelet transform. Wavelet transform is much better tool for the fault diagnosis point of view and a overview of the wavelet types (continuous and discrete), machine faults detection methods and their validation. The software, generality of codes, one dimensional and two dimensional DWT and frequency characteristics components of healthy as well as faulty induction motor has explained. So Finally, stator short winding , shaft fault, bearing fault ,rotor broken bar and open winding are taken as a case study to show the better diagnosis of fault by using wavelet techniques.
Diagnosis of broken bars fault in induction machines using higher order spect...ISA Interchange
Detection and identification of induction machine faults through the stator current signal using higher order spectra analysis is presented. This technique is known as motor current signature analysis (MCSA). This paper proposes two higher order spectra techniques, namely the power spectrum and the slices of bi-spectrum used for the analysis of induction machine stator current leading to the detection of electrical failures within the rotor cage. The method has been tested by using both healthy and broken rotor bars cases for an 18.5 kW-220 V/380 V-50 Hz-2 pair of poles induction motor under different load conditions. Experimental signals have been analyzed highlighting that bi-spectrum results show their superiority in the accurate detection of rotor broken bars. Even when the induction machine is rotating at a low level of shaft load (no-load condition), the rotor fault detection is efficient. We will also demonstrate through the analysis and experimental verification, that our proposed proposed-method has better detection performance in terms of receiver operation characteristics (ROC) curves and precision-recall graph.
Emission characteristics of a diesel engine using soyabean oil and diesel blendseSAT Journals
Abstract Diesel engines have been playing a vital role in the transportation and power generation sectors since from its invention. Despite of having better efficiency with diesel engine, the main concern is on emission of pollutants. There are various methods to reduce pollutant emission from a diesel engine. The prominent way to reduce pollutants is the usages of bio fuels with some modifications in the diesel engine. Diesel engine simulation models can be used to understand the combustion performance, prediction of emission concentration. These models can reduce the number of experiments. In this study, the performance and emissions characteristics of single cylinder, four stroke, and direct injection diesel engine operating on diesel and soybean blends have been investigated theoretically. The variations of various species concentration like CO2,CO and NOx with equivalence ratio have been analysed using diesel engine simulation models. These models can reduce the number of experiments. Computer simulation has contributed enormously towards new evaluation in the field of internal combustion engines. Mathematical tools have become very popular in recent years owing to the continuously increasing improvement in computational power. Index terms: Emissions, Bio fuels, Simulation Models
Direct Torque Control (DTC) of Induction Motor drive has quick torque response without complex orientation transformation and inner loop current control. DTC has some drawbacks, such as the torque and flux ripple. The control scheme performance relies on the accurate selection of the switching voltage vector. This proposed simple structured neural network based new identification method for flux position estimation, sector selection and stator voltage vector selection for induction motors using direct torque control (DTC) method. The ANN based speed controller has been introduced to achieve good dynamic performance of induction motor drive. The Levenberg-Marquardt back-propagation technique has been used to train the neural network. Proposed simple structured network facilitates a short training and processing times. The stator flux is estimated by using the modified integration with amplitude limiter algorithms to overcome drawbacks of pure integrator. The conventional flux position estimator, sector selector and stator voltage vector selector based modified direct torque control (MDTC) scheme compared with the proposed scheme and the results are validated through both by simulation and experimentation.
This presentation looks at the OBD technology, what is designed for, how it works and how it can be used.
The slides are a very dense summary of the benefits and issues related to OBD dongles in the UBI sector.
The author is Matt Noel, senior consultant at PTOLEMUS
Soft Computing Based Speed Control Technique of Induction Motor Drive in Sens...IJMTST Journal
Induction motor drives have certain advantages like less cost, ruggedness and required low maintenance. Field oriented control provides good solution for industrial applications. Normally in order to implement a vector control operation we generally require number of position sensors like speed, voltage, current sensors. But if we use the position sensors then the cost and size will be increased. So, to overcome this we need to use limited number of sensors. Reducing the number of sensors will increase the reliability of the system. So, if we eliminate the number of sensors we need to estimate the required quantity. The estimation can be done by using different strategies like model based and signal based. Out of this, model based estimation is the best method to estimate the speed by using Model Reference Adaptive System (MRAS).
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Novel Rotor Resistance Estimation Technique for Vector Controlled Induction...IAES-IJPEDS
Induction motor with indirect field oriented control is well suited for high
performance applications due to its excellent dynamic behavior. However it
is sensitive to variations in rotor time constant, especially variation in rotor
resistance. In this study a scheme based on the Rotor flux Model Reference
Adaptive Controller is used for on line identification of the rotor resistance
and thus improving the steady state performance of the drive. The overriding
feature of this estimation technique is the accurate identification of rotor
resistance during transient and steady state conditions for drive operation at
full load and at zero speed condition. Moroever, the effectiveness of the TS
fuzzy controller utilizing rotor flux for online estimation of rotor resistance
for four quadrant operation of motor drive is investigated and compared with
the conventional PI and Mamdani fuzzy controller.Simulation results in
MATLAB/Simulink environment have been presented to confirm the
effectiviness of the proposed technique.
Induction motor modelling and applications reportUmesh Dadde
A three-phase induction motor is one of the most popular and versatile motor in electrical
power system and industries. It can perform the best when operated using a balanced three-phase
supply of the correct frequency. In spite of their robustness they do occasionally fail and their
resulting unplanned downtime can prove very costly. Therefore, condition monitoring of
electrical machines has received considerable attention in recent years.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The speed estimation technique of induction machines has become a non-trivial task. For estimating the speed of an induction motor precisely and accurately an optimum state estimator is necessary. This paper deals with the performance analysis of induction motor drives using a recursive, optimum state estimator. This technique uses a full order state space Extended Kalman Filter (EKF) model where the rotor flux, rotor speed and stator currents are estimated. A major challenge with induction motor occurs at very low and at near zero speed. In such cases, information about the rotor parameters with respect to stator side become unobservable while using the synchronously rotating reference frame. To overcome this lost coupling effect, EKF observer linearizes the non-linear parameter in every sampling period and estimates the states and machine parameters simultaneously. The proposed algorithm is tuned to obtain least error in estimated speed. Any error found is further optimized using a non-linear fuzzy controller to obtain improved performance of the drive.
Brushless DC Motor Drive during Speed Regulation with Artificial Neural Netwo...IJERA Editor
Brushless DC motor, at this moment is extensively used being many industrial functions due to the different
features like high efficiency and dynamic response and high speed range. This paper is proposing a technology
named as Artificial Neural Network controller to control the speed of the brushless DC motor. Here the paper
contributes an analysis of performance Artificial Neural Network controller. Because it is difficult to handle by
the use of conventional PID controller as BLDC drive is a nonlinear. Through PID controller, the speed
regulation of BLDC is not efficient and reliable as PID controller cannot operate the large data, results it gives
different variation in BLDC motor control. The ANN easily trains the data of large amount by NN toolbox. As
ANN controller has the strength to indulge characteristics of control and it is accessible to operate the huge
amount of data as like human can store in a mind. The empirical results prove that an ANN controller can better
control the act than the PID controller. The modelling, control and the simulation of the BLDCM get done by
applying MATLAB/SIMULINK software kit.
Particle swarm optimization-based stator resistance observer for speed sensor...IJECEIAES
This paper presents a different technique for the online stator resistance estimation using a particle swarm optimization (PSO) based algorithm for rotor flux oriented control schemes of induction motor drives without a rotor speed sensor. First, a conventional proportional-integral controller-based stator resistance estimation technique is used for a speed sensorless control scheme with two different model reference adaptive system (MRAS) concepts. Finally, a novel method for the stator resistance estimation based on the PSO algorithm is presented for the two MRAS-type observers. Simulation results in the Matlab/Simulink environment show good adaptability of the proposed estimation model while the stator resistance is varied to 200% of the nominal value. The results also confirm more accurate stator resistance and rotor speed estimation in comparison with the conventional technique.
This paper deals with the problem of estimation of rotor angular position for the indirect pulse-vector control of wound rotor induction motor drive. The paper considers issues of thematic justification and expanding of the field of using sensorless motor drives. With a view to improve energy consumption readings during design and modernization of motor drives of massive mechanisms with moderate standards for accuracy of velocity control, requiring long-term velocity decrease during load reduction (according to technological process conditions), using the system of the pulse-vector control of wound rotor induction motor is suggested. The paper provides the solution for the problem of developing math models of this motor drive system both for the motor-mounted sensor, and for indirect angular position sensing. The models were developed in ANSYS Electromagnetics Suite using the finite element method for studying electromagnetic processes. Based on the models, the investigation of transition and steady states of a motor drive was carried out, process quality parameters were obtained, namely: max and root-mean-square currents, torques; velocity control errors caused by pulse operation mode. From that simulation, the result illustrates the effectiveness of the proposed approach.
Similar to A robust rf mras based speed estimator using neural network as a reference model for sensor-less vector controlled im drives (20)
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
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
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.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
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
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
A robust rf mras based speed estimator using neural network as a reference model for sensor-less vector controlled im drives
1. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.3, 2012
A Robust RF-MRAS based Speed Estimator using Neural Network
as a Reference Model for Sensor-less Vector Controlled IM Drives
A. Venkadesan, Research Scholar
Department of Electrical and Electronics Engineering
Pondicherry Engineering College, Pondicherry, India
E-mail: a_venkyeee@pec.edu
S. Himavathi, Professor
Department of Electrical and Electronics Engineering
Pondicherry Engineering College, Pondicherry, India
A. Muthuramalingam, Professor and Head
Department of Electrical and Electronics Engineering
Pondicherry Engineering College, Pondicherry, India
The authors acknowledge the financial support of the Department of Science and Technology, Delhi for the grant of
Junior Research Fellowship (JRF)-Professional to the first author for pursuing this research work. The research
project titled “AI techniques for Electrical Drives” is supported by the grants from the All India Council for
Technical Education (AICTE), a statutory body of Government of India. File Number: No 8023/BOR/RID/RPS-
79/2007-08 and 8020/RID/TAPTEC-32/2001-02.
Abstract
This paper proposes a robust MRAS based speed estimator for sensorless vector controlled IM drives. Rotor Flux
based MRAS Model Reference Adaptive System (RF-MRAS) for rotor speed estimation is gaining popularity for its
simplicity in sensorless vector controlled IM drives. In this scheme, the voltage model equations are used as the
reference model. The voltage model equations in turn depend on stator resistance which varies with temperature
during motor operation and more predominant at low frequencies/speed. Hence separate on-line estimator is required
to track the stator resistance variation. The newly developed MRAS technique uses a robust Single Neuron Cascaded
Neural Network (SNC-NN) based rotor flux estimator trained from input/output data as reference model in the place
of the conventional voltage model in RF-MRAS to form a robust RF-MRAS based speed estimator. This makes the
reference model robust to stator resistance variation without the need for separate Rs estimator. The performance of
the proposed speed estimator is investigated extensively for various operating conditions. The performance of
proposed MRAS is shown to work for wide range of operating conditions including zero speed operation. The
robustness of the proposed RF-MRAS based speed estimator is demonstrated through MATLAB simulations and
compared with the conventional RF-MRAS.
Keywords: Robust Rotor Flux-Model Reference Adaptive System, Rotor flux estimator, neural network, SNC-NN
model, Sensor-less operation, vector-controlled IM drives.
1. Introduction
Advances in digital technology have made the vector control realizable by industries for high performance variable
speed control applications. Various vector controlled techniques for induction motor drives have been proposed in
the literature. In particular, sensor-less vector control is an emerging area. The speed sensor which is expensive,
fragile, requires extra attention from failures under hostile environment and needs special enclosures and cabling is
not needed for sensor-less closed loop control of Induction Motor (IM) drives. This leads to cheaper and more
reliable control.
The performance of sensor-less vector controlled IM drive depends to a large extent on the knowledge of motor
speed. Various techniques for speed estimation have been suggested such as Model Reference Adaptive System
(MRAS), Luenberger and Kalman filter Observers, Sliding Mode Observers. MRAS scheme offer simpler
implementation and require less computational effort compared to other methods and therefore the most popular
1
2. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.3, 2012
strategies used for sensor-less control (Shady et al., 2009). Various MRAS schemes have been introduced in the
literature based on rotor flux, back electromotive force, and reactive power (S.Maiti et al., 2008, 2010; P.Vas, 1998).
However, rotor flux MRAS, first introduced by Schauder et al. (1992) is the most popular MRAS strategy. In this
MRAS scheme, the conventional voltage model equations are used as the reference model. Conventional voltage
model suffers from the problems of pure integrator and variation of stator resistance especially at low
frequencies/speed (B.K.Bose, 2005; J.Holtz et al., 2003). Several techniques are proposed in the literature to
overcome the problems of pure integrator (B.K. Bose et al., 1997; J.Hu et al., 1998). Stator resistance varies with
temperature during motor operation and more predominant at low frequencies/speed. Numerous methods for on-line
Rs estimation are proposed in the literature (B. Karanayil et al., 2007, 2005; N. Jaalam et al., 2011). But the
additional Rs estimator would increases the complexity of the drive system.
Neural Network (NN) based estimators provide an alternate solution for flux estimation. It dispenses the direct use
of complex mathematical model of the machine and hence overcomes the problems of integrator. The nonlinear
dynamic system mapping capability of neural network was well proven in the literature (K.S. Narendra et al., 1990).
They can be trained to be adaptive for parameter variations. Several Neural Network methods are reported for flux
estimation. Programmable-cascaded low pass filter was realized as a recurrent NN whose weights are obtained
through a polynomial-NN (L.E.B. da Silva et al., 1999). Single Layer Feed-forward Neural Network (SLFF-NN)
trained using input/output data is proposed for rotor flux estimation (Shady et al., 2009). It is shown to improve the
performance of the drive at very low and near zero speed, provide immunity to motor parameter variations, remove
low-pass filter/ integrator and reduce the error. The Heuristic Design methodology for Multilayer Feed-Forward NN
based flux estimator is proposed (A.Venkadesan et al. 2010). A compact NN model with desired accuracy assumes
importance in real implementation of on-line flux estimator to ensure faster estimation for effective control. Single
Neuron Cascaded (SNC) NN model is identified and shown to provide distinctly compact NN model for on-line flux
estimation (A.Muthuramalingam et al., 2010).
In this paper, SNC-NN based flux estimator trained with data including Rs changes is proposed to eliminate the need
separate for on-line Rs estimator. The designed robust SNC-NN model is proposed to replace the conventional
voltage model in the RF-MRAS to form robust RF-MRAS based speed estimator. The performance of the robust RF-
MRAS is investigated extensively for various operating conditions. The performance of proposed MRAS is shown to
work for wide operating range of operating conditions including zero speed. The robustness of the proposed RF-
MRAS based speed estimator is demonstrated through MATLAB simulations and compared with the conventional
RF-MRAS.
The paper is organized as follows. Section II details the sensor-less IM drives, RF-MRAS and its issues. Section III
describes the SNC-NN based flux estimator. The performance study of the proposed robust RF-MRAS based speed
estimator is carried out and simulation results are presented in section IV. The performance study of the proposed
and conventional MRAS based speed estimation scheme for parameter variation are carried out and simulation
results are presented in section V. Section VI concludes the paper.
2. Speed Sensor-less Vector Controlled IM Drives
The speed sensor-less vector control of induction motor drive presented is indirect rotor flux field oriented control.
Figure 1 shows the overall block diagram of the speed-sensor-less drive system of an induction motor. Generally
through a PI controller, the speed error signal is processed and the torque command is generated. It is combined with
the flux command corresponding to the flux error to generate the common reference to control the motor current. The
reference is used to produce the PWM pulses to trigger the voltage source inverter and control the current and
frequency applied to the IM drive. The performance of sensor-less vector controlled IM drive to a large extent
depends on the accuracy of speed estimation. There are many speed estimation schemes available in the literature.
Out of which, Rotor Flux Model Reference Adaptive System (RF-MRAS) is the most popular MRAS strategy.
The general block diagram of MRAS scheme for speed estimation is shown in Figure 2. The MRAS scheme consists
of a reference model which determines the desired states and adaptive (adjustable) model which generates the
estimated values of the states. The error between these states is fed to an adaptation mechanism to generate an
estimated value of the rotor speed which is used to adjust the adaptive model. This process continues till the error
between two outputs tends to zero.
A. RF-MRAS
2
3. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.3, 2012
In RF-MRAS, the state variable used is the rotor flux. Conventional voltage model equations for rotor flux
estimation are used as the reference model because it is independent of the rotor speed. The voltage model equations
are given in (1).
dΨs d is
dr v i s ds
dt L ds ds − σ L dt
= r
v −R (1)
s L s s s d s
d Ψ qr m qs i qs
i qs
dt dt
The current model equations for rotor flux estimation are used as the adaptive model because it is dependent on the
rotor speed. The current model equations are given in (2).
d Ψ s −1
dr −ω
T r Ψ s L ids
dt r dr m
= + (2)
s −1 Ψ s T iqs
d Ψ qr ω qr r
dt r
T
r
Where,
v s ( v qs )
ds
s - Stator voltages d axis (q axis)
s s
i ds (i qs ) - Stator currents d axis (q axis)
s s
Ψ ds ( Ψ qs ) - Stator flux d axis (q axis)
s s
Ψ dr ( Ψ qr ) - Rotor flux d axis (q axis)
R s( Rr ) - Stator resistance (rotor)
L s( Lr ) - Stator inductance (rotor)
Lm - Magnetization inductance
L2
m -Leakage Co-Efficient
σ = 1-
Lr Ls
Lr
Tr = -Rotor Time Constant
Rr
With correct speed signal, ideally, the fluxes calculated from the reference model and those calculated from the
adaptive model will match, that is, Ψ dr = Ψ 's and Ψ qr = Ψ 's , where Ψ dr and Ψ qr are reference model outputs and
s
dr
s
qr
s s
Ψ 's
dr and Ψ 's are the adaptive model outputs. An adaptation algorithm with PI controller, as indicated, can be used to
qr
tune the speed (ωr ,est ) so that the error ξ = 0 .
In designing the adaptation algorithm for the MRAS, it is important to take account of the overall stability of the
system and ensure that the estimated speed will converge to the desired value with satisfactory dynamic
characteristics. Using popov’s criteria for a globally asymptotically stable system, the following relation for speed
estimation can be derived.
ωr ,est = ξ K p + K i
(3)
S
' s Ψ s − Ψ s Ψ' s
ξ = Ψ dr qr (4)
dr qr
In steady state, ξ = 0 balancing the fluxes; in other words,
s
Ψ dr = Ψ 'dr and
s s
Ψ qr = Ψ 'qr
s (5)
From the equations (1), it is inferred that the voltage model used as the reference model in RF-MRAS are
dependent on resistance Rs and inductances Ls, Lm, Lr. The variation of these parameters tends to reduce the accuracy
of the flux estimation. Particularly, temperature variation of Rs becomes more dominant especially at low
frequencies/speed. At higher frequencies, the influence of Rs variation on the estimator is negligible. A small
mismatch in Rs between the motor and the estimator would cause the flux estimated from the voltage model based
3
4. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.3, 2012
estimator to get drift from the actual. This leads to large error in the speed estimation which would affect the overall
sensorless operation of IM drives. A separate additional on-line Rs estimator may overcome this problem but it
would increases the complexity of the drive system.
Hence to overcome these problems, a Single Neuron Cascaded Neural Network (SNC-NN) based flux estimator
trained with data including Rs changes is proposed to eliminate the need for separate on-line Rs estimator. The
proposed Single Neuron Cascaded Neural Network based flux estimator is used to replace the conventional voltage
model based flux estimator in RF-MRAS to form robust RF-MRAS.
3. Robust SNC-NN based Flux Estimator used as a Reference Model in RF-MRAS
The data based flux estimator is designed using SNC-NN model. The Single Neuron Cascaded (SNC) architecture
(A.Muthuramalingam et al., 2010) with multiple inputs/single output is shown in Figure 3. SNC-NN architecture
consists of an input layer, hidden layers and an output layer. The first hidden layer receives only external signals as
inputs. Other layers (M) receive external inputs and outputs from all previous (M-1) 1ayers. To create multilayer
structure hidden layers are added one by one and the whole network trained repeatedly using the concept of moving
weights so as to obtain compact network (A.Muthuramalingam et al., 2010). This process continues, till the
performance index is reached. Cascading single neuron in every hidden layer in the “Single Neuron Cascaded” (SNC)
architecture greatly simplifies the design process and can be self-organized which aids design automation similar to
SLFF-NN. Thus SNC-NN combines the advantage of self organizing feature of SLFF-NN and power multilayer
mapping capability of MLFF-NN. Also SNC-NN model is identified and shown to provide distinctly compact NN
model for on-line flux estimation (A.Muthuramalingam et al., 2010). Hence in this paper, SNC-NN model is chosen
to model the on-line Flux Estimator.
The indirect field oriented controlled (IFOC) IM drive system with sinusoidal pulse width modulation is built using
MATLAB with switching frequency of 10 KHz. The present and past samples of the d-q components of the stator
s s s s
voltages { v ds ( k ) , v ds ( k − 1) , v qs ( k ) , v qs ( k − 1) } and stator currents { i ds ( k ) , i ds ( k − 1) , i s ( k ) , i qs ( k − 1) }are used as the inputs to
s s s
qs
s s
the SNC-NN Model. The outputs are the direct and quadrature axis rotor fluxes { Ψ dr ( k ) , Ψ qr (k ) }. The block diagram
of SNC-NN based flux estimator is shown in Figure 4. The vector controlled IM drive is the variable frequency
drive. Hence, equal number of data sets for all operating conditions is used to train the network. Around 11,266 data
sets are obtained from the IFOC System for various operating conditions. In the literature, it is reported that the
change in Rs may go upto 50% (B. Karanayil et al., 2007, 2005). Hence, to make SNC-NN robust to parameter
variation, maximum of 50% change in Rs variation is incorporated in the training data sets. The activation function
for hidden layers and output layer is chosen as tan-sigmoid and pure linear function respectively. The SNC-NN is
trained with input/output data using LM algorithm for the required Mean Square Error (MSE) of 1.88876×10-6.The
obtained SNC-NN model for flux estimation has the structure 8-13(h)-2 (h-hidden layer with one neuron). The
obtained SNC-NN model for flux estimation replaces the conventional voltage model in the RF-MRAS to form
“robust RF-MRAS”.
4. Performance of Proposed Robust RF-MRAS based Speed Estimator
The performance of proposed robust RF-MRAS scheme is tested for speed estimation for various operating
conditions extensively through MATLAB simulations. Sample results for the major test conditions are presented in
the following sections.
1) Test 1- Stair Case Speed Transients from 148 to 0 to −148 rad/sec at No Load:
In this test, the IM drive is subjected to a stair case speed commands from 148 rad/sec to zero speed in a series of
five 40 rad/sec steps continuing to −148 rad/sec, at no load. The performance of robust RF-MRAS is shown in
Figure 5. The rotor fluxes estimated from the proposed flux estimator tracks the actual with negligible error for all
the speed commands including the zero speed. The speed estimated from the proposed MRAS scheme is also found
to closely track with the actual. The results depict Stable operation of the proposed MRAS scheme, particularly
around zero speed.
2) Test 2- Load Torque Change from 0% to 100% at 148rad/sec:
The test 2 examines the load torque disturbance capability of the proposed MRAS based speed estimation scheme.
The drive is operated with reference speed of 148rad/sec. 100% step change in load torque is applied at 1.5sec. The
proposed robust MRAS shows better steady state and dynamic performance with negligible error between the actual
4
5. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.3, 2012
and estimated speed, as shown in Figure 6. The estimated rotor speed undershoots similar to actual speed and settles
faster with the actual as soon as the step change in load torque is applied.
3) Test 3- Load Torque Change from 100% to 50% at 148rad/sec:
The test 3 also examines the load torque change capability of the robust RF-MRAS. The drive is initially operated
with the speed command of 148rad/sec with 100% load condition and suddenly the load torque is reduced to 50% at
1.5sec. In this case also, the speed estimated from the proposed robust RF-MRAS is found to closely match with the
actual with negligible error. The estimated speed overshoots similar to the actual speed and settles faster with the
actual as soon as the load torque is suddenly reduced to 50%, as presented in Figure 7.
4) Test 4- ±148 rad/sec Speed at No Load:
The test 4 examines the speed reversal capability of proposed speed estimation scheme. Initially, the drive is
operated with the speed command of 148rad/sec. The slow speed reversal is taking place during 1.5-2 sec. The
command speed is fixed to -148rad/sec after 2sec. The performance of proposed speed estimation scheme is shown
in Figure 8. The proposed speed estimation scheme shows better performance. The speed estimated is found to
closely match with the actual with negligible error.
The proposed MRAS scheme works for wide range of operating conditions from 148 (rad/sec) to -148 (rad/sec)
including zero speed operation. The error between the actual and the estimated speed from the proposed RF-MRAS
for various operating conditions are consolidated and presented in Table I. The error in the speed estimated from the
proposed MRAS scheme is found to be within ±0.4% for normal operating speeds. At very low operating speeds, the
error is found to be within ±1.4%.
4. Performance Comparison of Proposed robust RF-MRAS and Conventional RF-MRAS based speed
Estimator for Stator Resistance Variation
The performance of proposed robust RF-MRAS and conventional RF-MRAS is tested for step change in stator
resistance variation. Of course, in a real drive, the stator resistance never undergoes abrupt variations in response to
temperature change due to the large thermal time constant. The step variation represents an extreme case and is used
to show the robustness of the proposed MRAS. The effect of Rs variation is investigated at very low speed of
1rad/sec with 50% load condition. Two different cases for stator resistance detuning are considered.
(a) Slight Rs detuning:
The actual Rs of the induction motor are slightly detuned with respect to the nominal ones, as follows:
∆Rs
= −5% (6)
Rs
In this case, 5% step change in Rs is effected at 2sec. The locus diagram of rotor fluxes for the robust SNC-NN
model and voltage model are presented in Figure 9(a) and (b) respectively. It is understood that the locus diagram of
rotor fluxes of robust SNC-NN model closely tracks the locus of the actual flux and it is centered on the origin
similar to the actual flux. The radius of the locus of the proposed flux estimator is also found to be similar to the
actual flux. In the case of voltage model based flux estimator, the locus of rotor fluxes is not centered on the origin
and it is shifted away from the origin approximately 0.05433wb. The radius of the locus of the voltage model based
flux estimator is also observed to get increased approximately by 4.777%. Hence, the proposed robust SNC-NN
based flux estimator is found to estimate the flux components with good accuracy even when there is change in the
Rs with d and q-axis rotor flux MSE of 1.124×10-6 and 1.723×10-6 respectively.
The speed estimated from the robust RF-MRAS and conventional RF-MRAS is presented in Figure 10(a) and (b)
respectively. From the results obtained, it is obvious that the speed estimated from the robust RF-MRAS tracks
closely the actual speed even when there is a change in the Rs and the error in the speed estimation is almost
negligible. But, the speed estimated from the RF-MRAS fluctuates between -0.1798 (rad/sec) to 1.0873 (rad/sec).
5
6. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.3, 2012
4.2 Large Rs detuning:
In many real applications, the Rs may vary on ranges which are larger than those considered in previous section. In
order to check the robustness of the proposed speed estimator in the presence of larger detuning, the actual Rs of the
induction motor are largely detuned with respect to the nominal ones, as follows:
∆Rs
= −50% (7)
Rs
50% step change in Rs is effected at 2sec. The locus diagram of rotor fluxes for the proposed robust SNC-NN model
and voltage model are presented in Figure 11 (a) and (b) respectively.
From the results obtained, it is observed that even in the case of large parameter detuning, the locus of the robust
SNC-NN model tracks the actual flux locus very well. But the centre of the locus of flux estimated from the voltage
model is shifted largely away from the origin approximately 0.3741wb which is 588.569% times larger than the
previous one. The radius is also observed to get increased approximately by 56.871% which is much larger than the
previous one. The robust speed estimation is observed from the proposed speed estimator even in the case of large
parameter detuning which is presented in Figure 12(b). The speed estimated using RF-MRAS oscillates between -
29.6098 (rad/sec) to 0.3144 (rad/sec) which is evident from Figure 12 (a). Thus the centre and radius of the locus of
flux estimated from the voltage model keeps on increases with increase in Rs. This leads to increase of oscillation in
the estimated speed using voltage model. The conventional RF-MRAS can also be made robust to Rs variation with
an additional on-line Rs estimator which would increase the complexity of the drive system. Thus the NN based
estimator, trained with parameter variations result in the robust NN based flux estimator. The robust NN based
estimator used as the reference model in RF-MRAS which result in the robust RF-MARS. This in turn results in
robust speed estimation even in the presence of Rs variation.
5. Conclusion
This paper proposes a robust RF-MRAS for speed estimation over wide operating range in sensorless IM drives. The
robust data based flux estimator is designed using SNC-NN model with data including Rs variation to avoid the
requirement of separate need for on-line Rs estimator. The designed robust NN based flux estimator is proposed to
replace the conventional voltage model in RF-MRAS to from a robust RF-MRAS. The performance of proposed
MRAS is extensively investigated for various operating conditions. The proposed MRAS is shown to work for wide
range of operating conditions including zero speed. The error in the speed estimated from the proposed MRAS
scheme is found to be within ±0.4% for normal operating speeds. At very low operating speeds, the error is found to
be within ±1.4%.The robustness of proposed MRAS scheme is illustrated for parameter variation and found to
outperform the conventional RF-MRAS scheme. The advantages of the Proposed MRAS scheme are: independent of
stator resistance, the reference model in proposed robust RF-MRAS is free from the integrator related problems, and
the reference model in the proposed MRAS is computationally less rigorous as compared to the integral equations as
involved in the reference model of conventional RF-MRAS.
Thus the proposed robust RF-MRAS based speed estimation scheme is shown to perform well under all operating
conditions including Rs variation. Hence it is concluded that a robust RF-MRAS based speed estimator is a
promising technique for speed estimation in sensor-less vector controlled IM drives.
APPENDIX
The parameters of the induction machine used for simulation are given in the table shown below.
INDUCTION MOTOR PARAMETERS
Parameters Values Parameters Values
Rated Power 1.1kW Stator Resistance (Rs) 6.03
Rated voltage 415V Rotor Resistance (Rr) 6.085
Rated current 2.77A Magnetizing Inductance (Lm) 0.4893H
Type 3 Ph Stator Inductance (Ls) 0.5192H
Frequency 50Hz Rotor Inductance (Lr) 0.5192H
Number of poles 4 Total Inertia (JT) 0.011787Kgm2
Rated Speed 1415RPM Friction Coefficient (B) 0.0027Kgm2/s
6
7. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.3, 2012
References
Shady M.Gadoue, Damian Giaouris, John W.Finch (2009) Sensorless Control of Induction Motor Drives at
very Low and Zero Speeds Using Neural Network Flux Observers. IEEE Transactions on Industrial Electronics
56(8): 3029-3039.doi: 10.1109/TIE.2009.2024665
S.Maiti, C. Chakraborty, Y.Hori and M.C. Ta (2008) Model reference adaptive controller-based rotor resistance
and speed estimation techniques for vector controlled induction motor drive utilizing reactive power. IEEE
Transactions on Industrial Electronics 55(2): 594-601. doi: 10.1109/TIE.2007.911952
S.Maiti and C. Chakraborty (2010) A new instantaneous reactive power based MRAS for sensorless induction
motor drive. Simulation Modelling Practice and Theory 18: 1314-1326. doi: 10.1016/j.simpat.2010.05.005
P. Vas (1998), Sensorless Vector and Direct Torque Control, NewYork:Oxford Univ. Press
Colin Schauder (1992) Adaptive Speed Identification for Vector Control of Induction Motors without
Rotational Transducers. IEEE Transactions on Industry Applications 28(5).1054-1061
Bimal K. Bose (2005) Modern Power Electronics and AC Drives. Prentice-Hall. India
J.Holtz, J.Quan (2003) Drift- and Parameter-Compensated Flux Estimator for Persistent Zero-Stator-
Frequency Operation of Sensorless-Controlled Induction Motors. IEEE Transactions on Industry Applications
39(4): 1052-1060.doi: 10.1109/TIA.2003.813726
B.K.Bose, N.R.Patel (1997) A Programmable Cascaded Low-Pass Filter-Based Flux Synthesis for a Stator
Flux-Oriented Vector-Controlled Induction Motor Drive. IEEE Transactions on Industrial Electronics
44(1):140-143.
J.Hu, B.Wu (1998) New Integration Algorithms for Estimating Motor Flux over a Wide Speed Range. IEEE
Transactions on Power Electronics 13(5):969-977
B. Karanayil, M.F.Rahman and C.Grantham (2007) Online Stator and Rotor Resistance Estimation Scheme
Using Artificial Neural Networks for Vector Controlled Speed Sensorless Induction Motor Drive. IEEE
Transactions on Industrial Electronics 54(1):167-176. doi: 10.1109/TIE.2006.888778
B. Karanayil, M.F.Rahman and C.Grantham (2005) Stator and Rotor Resistance Observers for Induction Motor
Drive Using Fuzzy Logic and Artificial Neural Networks. IEEE Transactions on Energy Conversion 20(4):771-
780. doi: 10.1109/TEC.2005.853761
N. Jaalam, A.M.A.Haidar, N.L.Ramli, N.L.Ismail and A.S.M.Sulaiman (2011) A Neuro-fuzzy Approach for
Stator Resistance Estimation of Induction Motor. International Conference on Electrical, Control and Computer
Engineering 394-398.
K.S.Narendra, K.Parthasarathy (1990) Identification and Control of Dynamical Systems Using Neural
Networks. IEEE Transactions on Neural Networks 1(1):4-27
Luiz.E.B.daSilva,B.K.Bose,Joao.o.p.Pinto (1999) Recurrent-Neural-Network-Based Implementation of a
Programmable Cascaded Low-Pass Filter Used in Stator Flux Synthesis of Vector-Controlled Induction Motor
Drive. IEEE Transactions on Industrial Electronics 46(3):662-665
A.Venkadesan, S.Himavathi, and A.Muthuramalingam (2010) Design of Feed-Forward Neural Network Based
On-line Flux Estimator for Sensor-less Vector Controlled Induction Motor Drives. International Journal of
Recent Trends in Engineering and Technology 4(3):110-114
A.Muthuramalingam, A.Venkadesan and S.Himavathi (2010) On-Line Flux Estimator using Single Neuron
Cascaded Neural Network Model for Sensor-less Vector Controlled Induction Motor Drives. (ICSDC-2010),
Manipal Insititute of Technology, Manipal, India: 96-100
7
8. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.3, 2012
Solid State IM Drive Systems
va ia
Input Supply PWM
C Inverter IM
vc ic
ωr
ψ PWM-a
ref PWM-b
ψs Rotor
dr Flux PWM-c
s
ψ qr Oriented
ω Controller
r,ref
ωr,est
ωr,est
Speed
Estimator
Figure 1. Sensor-less Vector Controlled IM Drives showing the requirement of Speed Estimator
v dq
Reference Model s
Ψ qr
i dq s
Ψ dr
Ψ 'dr
s ξ
Adaptive Model
Ψ 'qr
s
ω r ,est Adaptive Mechanism
(PI-Controller)
Figure 2. RF-MRAS based Speed Estimator
8
9. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.3, 2012
p p p
1 2 R
Layer 1
1
w 1,1 1 1
n a
∑ 1
f 1 1
1 2,1
w 1,R b
1
1 w
1,1
Layer 2 w
m ,1
1,1
2
w 1,1
2 2
n a
∑ 1
f 2 1
2
w 1,R
b
2
1 m ,2
w1,1 Layer m
m
w 1,1 m m
n a
m
∑ 1
f m 1
w 1, R
b
m
1
Figure 3. SNC-NN with multiple inputs/single output
where,
p - Input vector, p = [1, 2, ...R]
m, k Link weight of neuron ‘i’ of layer ‘m’ for input
w
i, j -
from neuron ‘j’ of layer ‘k’.
m Input weight of neuron ‘i’ of layer ‘m’ for
w
i,R -
external input ‘R’.
m
b
i
- bias for neuron ‘i’ of layer ‘m’.
m Activation functions of all neurons in a layer
f -
‘m’.
m
a
i
- Output of neuron ‘i’ of layer ‘m’
s
v ds ( k )
s
v ds ( k − 1)
s
v qs ( k )
s
Ψ dr ( k )
s
v qs ( k − 1) SNC-NN
Based
s Flux Estimator s
i ds ( k ) Ψ qr ( k )
s
i ds ( k − 1)
s
i qs ( k )
s
i qs ( k − 1)
Figure 4. The Inputs and Outputs of SNC-NN based Flux Estimator
9
10. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.3, 2012
(a) (b)
Figure 5. Performance Curves for Test Condition-1 : (a) Rotor Fluxes (b) Rotor Speed
(a) (b)
Figure 6. Performance Curves for Test Condition-2 : (a) Rotor Fluxes (b) Rotor Speed
(a) (b)
Figure 7. Performance Curves for Test Condition-3 : (a) Rotor Fluxes (b) Rotor Speed
(a) (b)
Figure 8. Performance Curves for Test Condition-4 : (a) Rotor Fluxes (b) Rotor Speed
10
11. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.3, 2012
(b) (b)
Figure 9. Locus of Rotor Fluxes with Slight Rs Detuning: (a) Voltage Model (b) Robust SNC-NN Model
(b) (b)
Figure 10. Rotor Speed with Slight Rs Detuning: (a) RF-MRAS (b) Robust RF-MRAS
(b) (b)
Figure 11. Locus of Rotor Fluxes with Large Rs Detuning: (a) Voltage Model (b) Robust SNC-NN Model
(c) (b)
Figure 12. Rotor Speed with Large Rs Detuning: (a) RF-MRAS (b) Robust RF-MRAS
11
12. Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.3, 2012
TABLE I
PERFORMANCE OF THE PROPOSED RF-MRAS BASED SPEED ESTIMATOR FOR VARIOUS SPEED COMMANDS
Command
Actual Speed Estimated Speed using
Speed %Error
(rad/sec) Robust RF-MRAS (rad/sec)
(rad/sec)
148 147.9998 147.9255 0.05022
75 75.0015 74.9623 0.05160
35 34.9976 34.9891 0.02428
25 25.0012 24.9966 0.01839
15 14.9996 14.9971 0.01666
5 4.9884 5.0074 0.38088
12
13. This academic article was published by The International Institute for Science,
Technology and Education (IISTE). The IISTE is a pioneer in the Open Access
Publishing service based in the U.S. and Europe. The aim of the institute is
Accelerating Global Knowledge Sharing.
More information about the publisher can be found in the IISTE’s homepage:
http://www.iiste.org
The IISTE is currently hosting more than 30 peer-reviewed academic journals and
collaborating with academic institutions around the world. Prospective authors of
IISTE journals can find the submission instruction on the following page:
http://www.iiste.org/Journals/
The IISTE editorial team promises to the review and publish all the qualified
submissions in a fast manner. All the journals articles are available online to the
readers all over the world without financial, legal, or technical barriers other than
those inseparable from gaining access to the internet itself. Printed version of the
journals is also available upon request of readers and authors.
IISTE Knowledge Sharing Partners
EBSCO, Index Copernicus, Ulrich's Periodicals Directory, JournalTOCS, PKP Open
Archives Harvester, Bielefeld Academic Search Engine, Elektronische
Zeitschriftenbibliothek EZB, Open J-Gate, OCLC WorldCat, Universe Digtial
Library , NewJour, Google Scholar