Artificial-Intelligence-Based Electrical Machines and Drives

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1 Artificial-Intelligence-Based Electrical Machines and Drives Application of Fuzzy, Neural, Fuzzy-Neural, and Genetic-Algorithm-Based Techniques Peter Vas Professor of Electrical Engineering University of Aberdeen OXFORD UNIVERSITY PRESS

2 Contents Glossary of symbols xxvi 1 Introduction l 2 Past, present, and future of electrical machines and variable-speed drives The past The present D.C. drives: improved drives A.C. drives: vector drives, direct torque-controlled drives, reluctance motor drives, universal drives, 'integrated drives', intelligent control The future General, convergence of technologies Size of the world and European drives market Packaging, integrated drives: integration of motor and inverter Power semiconductors, their management Self-tuning, fuzzy, neural, fuzzy-neuro, and geneticalgorithm-based and genetic-algorithm-assisted control systems, minimum-configuration soft-computing Electromagnetic compatibility, power quality, efficiency Railway traction, maglev systems, electric vehicles Applications of superconductivity Machine design, virtual reality, testing and maintenance, other developments Future of drives as seen by various drive, software, and hardware manufacturers Summary of future trends and developments 30 Bibliography 31 3 General aspects of the application of artificial intelligence in variable-speed drives General, classical and intelligent control, semiconductor, processor, and fuzzy logic markets Artificial neural networks (ANNs) Fuzzy logic systems (FLSs) 39 ( 24

3 xvi 3.4 Fuzzy-neural, neural-fuzzy systems Genetic algorithms (GAs) AI applications in electrical machines and drives including control, estimation, condition monitoring and diagnosis Industrial applications of AI in drives Hitachi drive Yaskawa drive 47 Bibliography 49 4 Conventional, fuzzy, neural, and fuzzy-neural function approximators General Polynomial function approximator Kolgomorov's theorem, neural function approximators Fuzzy function approximators, the three main types of fuzzy approximators Fuzzy-neural (neural-fuzzy) function approximators 72 Bibliography 75 5 Application of artificial neural networks ANN fundamentals Biological neuron model, artificial neuron model, ANN networks Biological neuron Static and dynamic artificial neuron models: adaptive function estimators General Weights Inputs, bias Activation functions ANN networks: single-layer and multilayer feedforward ANNs Single-layer ANN Multi-layer ANN Radial basis function neural network Hardware implementation of ANNs Various ANNs and training strategies for different applications Application of the error backpropagation algorithm Data preparation for the backpropagation algorithm Nodes, layers Backpropagation training and learning, learning curve The generalized delta rule (error backpropagation learning algorithm) 105

4 xvii Application of simultaneous input vectors: batching Numerical acceleration techniques, avoidance of overfitting Momentum method Topology adjustment Dynamic learning Avoidance of overfitting The Levenberg-Marquardt algorithm Unsupervised (self-organized) learning, competitive learning Main features of unsupervised learning and winner-take-all learning technique Three computational stages of clustering Winner-take-all network initialization and weight adjustment algorithm Limitations of the winner-take-all algorithm, other algorithms The Kohonen feature map (KFM) and other selforganizing techniques Lateral connections in a biological neural network Lateral connections in an artificial neural network: the Kohonen feature map The KFM learning algorithm, main characteristics Neurocontrol systems General Classical control systems Classical open-loop control systems Classical non-linear feedback control systems Neurocontrol Identification using neural networks Forward modelling: Series-parallel indentification, parallel indentification Inverse modelling Control using neural networks Direct inverse neurocontrol Neurocontrol using a reference model General Direct adaptive neurocontrol using a reference model Indirect adaptive neurocontrol using a reference model 166 Bibliography Fuzzy logic systems General Proven advantages of various industrial fuzzy logic applications 174

5 XV Japanese research in the field of fuzzy logic applications LIFE research consortium Science and Technology Agency What is a fuzzy logic system (FLS)? When should fuzzy logic be used (electrical machine and drive applications)? Fuzzy logic basics Classical (crisp) set, characteristic function, classical set operations Classical set, characteristic function Classical set operations Fuzzy set, membership function, fuzzy set operations Fuzzy set, membership function Various membership functions, parameter sets Conventional fuzzy set operations Functions of fuzzy sets, fuzzy rules, fuzzy reasoning, fuzzy inference systems (fuzzy logic controllers) Functions of fuzzy sets, the extension principle, fuzzy rules, fuzzy reasoning Various fuzzy inference systems (fuzzy logic controllers) Mamdani fuzzy logic inference system Sugeno fuzzy logic inference system Tsukamoto fuzzy logic inference system Fuzzy system design: automatic generation of fuzzy rules from data General Design steps Adaptive fuzzy logic systems 232 Bibliography Fuzzy-neural systems Mamdani-type fuzzy-neural system General Mamdani-type fuzzy neural controller design Structure of fuzzy-neural controller Determination of the fuzzy-neural network structufe and parameters Fuzzy-neuro system tuning Alternative fuzzy-neural system designs Sugeno-type fuzzy-neural system Network architecture Learning rules, hybrid learning Tsukamoto-type fuzzy-neural system Wang-type fuzzy-neural system 254

6 XIX Network architecture Learning rules Closed-loop control systems using fuzzy-neural networks 259 Bibliography Genetic algorithms General Genetic algorithm steps Tuning of membership functions using genetic algorithms Application of genetic algorithms to neural networks Tuning of controllers using genetic algorithms 274 Bibliography Artificial-intelligence-based steady-state and transient analysis of d.c. machines, estimators, control General Al-based steady-state and transient analysis of d.c. machines Conventional analysis ANN-based analysis Speed estimation, ANN, ANN, ANN, ANN Electromagnetic torque estimation, ANN, ANN, ANN Simultaneous speed and torque estimation, ANN, ANN, ANN Simultaneous speed, field and armature current estimation, ANN, ANN Fuzzy-neural speed estimator for a d.c. motor D.C. drive with ANN speed estimator General introduction Selection of training data for AIB speed estimator design Design of neuro-speed estimators, simulation results Experimental results General introduction D.C. drive with feedforward multilayer ANN-based speed estimator D.C. drive with recursive ANN-based speed estimator D.C. drive with radial basis ANN-based speed estimator D.C. drive with fuzzy controllers Drive with fuzzy speed controller Drive with fuzzy speed and armature current controller Drive with fuzzy speed, armature current and flux controller Drive with fuzzy firing angle compensation, fuzzy speed controller and fuzzy armature current controller 319

7 XX General Drive scheme Linearization of converter non-linear characteristics Fuzzy firing angle compensator Fuzzy speed and current controllers Simulation results D.C. drive with fuzzy-neural estimator Design of fuzzy-neural speed estimators, simulation results Experimental results D.C. drives with ANN controllers, application of combined ANN controllers D.C. motor control using indirect adaptive neuro-control with reference model D.C. motor control using direct inverse adaptive neurocontrol D.C. drive with combined neuro-controller 345 Bibliography Artificial-intelligence-based steady-state and transient analysis of induction machines, estimators, control, design, condition monitoring, and diagnosis General Al-based steady-state and transient analysis of induction machines Conventional steady-state and transient analysis ANN-based steady-state and transient analysis ANNs for a slip-ring induction machine Speed estimation Stator flux linkage estimation Electromagnetic torque estimation ANNs for a squirrel-cage induction machine Speed and position estimation Simultaneous speed, torque and flux estimation Speed estimation in a vectorcontrolled drive Fuzzy-neural-network-based steady-state and transient analysis of induction machines Al.based condition monitoring, diagnosis, parameter estimation, harmonic analysis General Bearing fault monitoring in induction machines Bearing faults, different monitoring techniques Al-based bearing fault monitoring using stator current (r.m.s. value) and rotor speed 372

8 XXI ANN-based bearing fault monitoring Fuzzy-logic-based bearing fault monitoring Fuzzy-neural-network-based bearing fault monitoring Al-based monitoring of bearing faults in an induction,machine by using vibration signals Conventional vibroacoustic analysis Al-based vibroacoustic analysis Al-based monitoring of bearing faults in an induction machine using two accelerometers Al-based monitoring of bearing faults in an induction machine using a single accelerometer Al-based monitoring of bearing faults in an induction machine using monitored stator current spectra ANN-based broken rotor bar detection in a squirrel cage induction motor Supervised ANN-based diagnostic system Unsupervised ANN-based diagnostic system Fuzzy-logic-based broken rotor bar detection in a squirrel cage induction motor Fuzzy-neural network-based broken rotor bar detection in a squirrel cage induction motor Unsupervised ANN-based real-time fault diagnosis of a voltage source inverter Fuzzy-logic-based parameter estimation of an induction motor Fuzzy-neural-network-based parameter estimation ANN-based harmonic analysis Harmonic analysis using space vector loci Harmonic analysis using self-organizing neural networks ANN-based current control of a voltage source PWM inverter Induction motor drive with ANN-based modulator General Conventional space-vector modulator in a voltage source inverter ANN-based space-vector modulator in a voltage source inverter 415

9 General Classical competitive ANN Modified competitive ANN ANN-based space-vector modulator in a current source inverter Induction motor drives with ANN-based flux, torque position and speed estimators Vector drive with ANN-based flux and torque estimators and using conventional speed transducer ANN-based flux estimation in high-performance drives Sensorless vector drive with ANN-based speed and position sensors Model Reference Adaptive System based estimators Conventional MRAS speed estimator MRAS-based system with Al-based adaptive model and various possibilities for speed turning signal General, estimators using artificial intelligence MRAS containing a two-layer ANN: speed is praportional to weights Multilayer feedforward ANN-based speed and position estimator Sensorless scalar drive with ANN-based speed sensor Induction motor neurocontrol using reference model General Identification of the induction machine Conventional discrete electromagnetic model of the induction machine NARMAX model of the induction machine Neuroidentification of the induction machine stator currents Adaptive neurocontrol of the stator currents Adaptive neurocontrol of the rotor speed Identification of the rotor speed Neurocontrol of the rotor speed Simultaneous on-line neuroidentification and neuro speed control Vector drive with fuzzy controllers General Vector drive with four controllers, design and tuning of fuzzy controllers 467

10 XXU Experimental results, drive with single fuzzy controller Experimental results, drive with two fuzzy controllers Experimental results, drive with four fuzzy controllers (full fuzzy control) Scalar drive with fuzzy-neural controllers General Drive with fuzzy-neural stator current reference generation Drive with fuzzy-neural stator current reference generation and fuzzy-neural speed controller Vector drive with a fuzzy-neural or neural controller General Vector drive with a self-organizing fuzzy-neural speed controller, experimental results, minimal configuration Vector drive with a neural speed controller, experimental results, minimal configuration Induction motor drive with real-time Fuzzy Logic Efficiency Optimization Controller (FLEOC) Efficiency optimization Fuzzy logic efficiency optimization controller (FLEOC), general, drive scheme FLEOC and pulsating torque compensator Loss mechanism, principle of adaptive optimization control, fuzzy rule base Fuzzifier, defuzzifier, scaling factors Pulsating torque compensator Results, practical implementation Fuzzy controlled DTC induction motor drives Switching vector selection Conventional DTC drive Fuzzy switching vector selection to obtain faster torque and flux responses General Switching vector selection using a fuzzy rule base Fuzzy inference Simulation results Fuzzy-logic-based DTC drive with constant switching frequency Fuzzy-logic-based stator resistance estimation Fuzzy switching vector selection to obtain reduced stator flux and torque ripples Fuzzy-logic-based automated design of a squirrel cage induction motor 532

11 General Classical and automated design procedure structure Results Genetic algorithm-based flux observer design General Reduced-order rotor flux observer GA-based optimal rotor flux observer design GA-assisted ANN-based optimal switching vector selection in a DTC induction motor drive GA-assisted parameter estimation of an induction motor 545 Bibliography Artificial-intelligence-based steady-state and transient analysis of synchronous machines, estimators, control, design, condition monitoring, and diagnosis General ANN-based steady-state and transient analysis, ANN-based speed, position, torque, and flux estimators Application of ANNs for steady-state and transient analysis ANN-based speed, position, and flux estimators ANN-based parameter estimation ANN-based saturated inductance estimation for a synchronous generator General ANN-based saturated inductance estimators Condition monitoring, diagnosis ANN-based detection of interturn short circuits in the field winding of turbogenerators General, travelling wave technique Multilayer feedforward ANN-based interturn short-circuit detection, fault localization ANN-based interturn short-circuit detection in operational turbogenerators Unsupervised ANN-based diagnosis of a synchronous generator Fuzzy logic control of p.m. synchronous motor drives General High-performance drive with adaptive fuzzy-logic speed controller Tuning of fuzzy scaling factors Experimental results Fuzzy-logic-based hysteresis controller Fuzzy-logic-based automated design of a p.m. synchronous motor with trapezoidal e.m.f. 581

12 XXV General Classical and automated design procedure structure Results Fuzzy-logic-based automatic voltage regulator for a synchronous generator Fuzzy-logic-based optimal switching vector selection in DTC permanent magnet synchronous motor drives 591 Bibliography Artificial-intelligence-based switched reluctance motors, estimation, design, and control General Fuzzy-logic-based inductance estimation of a switched reluctance motor Artificial-intelligence-based position estimation in switched reluctance motor drives Artificial-intelligence-based electromagnetic torque estimation in switched reluctance motor drives Artificial-intelligence-based switched reluctance motor design Fuzzy logic control of a switched reluctance motor General Drive scheme, rule base for fuzzy speed controller Fuzzy synchronization block Results Artificial-intelligence-based torque ripple minimization 613 Bibliography 615 Appendix: Texas Instruments motion control DSPs 617 Index 619

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