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E-raamat: Computational Intelligence Paradigms for Optimization Problems Using MATLAB(R)/SIMULINK(R)

(PSG College of Technology, Coimbatore, Tamil Nadu, India), (PSG College of Technology, Coimbatore, India), (PES University, Bangalore, Karnataka, India)
  • Formaat: 623 pages
  • Ilmumisaeg: 03-Sep-2018
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781498743723
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Computational Intelligence Paradigms for Optimization Problems Using MATLAB(R)/SIMULINK(R)Suurem pilt
  • Formaat: 623 pages
  • Ilmumisaeg: 03-Sep-2018
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781498743723
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Considered one of the most innovative research directions, computational intelligence (CI) embraces techniques that use global search optimization, machine learning, approximate reasoning, and connectionist systems to develop efficient, robust, and easy-to-use solutions amidst multiple decision variables, complex constraints, and tumultuous environments. CI techniques involve a combination of learning, adaptation, and evolution used for intelligent applications.

Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/ Simulink® explores the performance of CI in terms of knowledge representation, adaptability, optimality, and processing speed for different real-world optimization problems.

Focusing on the practical implementation of CI techniques, this book:











Discusses the role of CI paradigms in engineering applications such as unit commitment and economic load dispatch, harmonic reduction, load frequency control and automatic voltage regulation, job shop scheduling, multidepot vehicle routing, and digital image watermarking Explains the impact of CI on power systems, control systems, industrial automation, and image processing through the above-mentioned applications Shows how to apply CI algorithms to constraint-based optimization problems using MATLAB® m-files and Simulink® models Includes experimental analyses and results of test systems

Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/ Simulink® provides a valuable reference for industry professionals and advanced undergraduate, postgraduate, and research students.
Preface xv
Acknowledgments xix
Authors xxi
1 Introduction 1(58)
Learning Objectives
1(1)
1.1 Computational Intelligence Paradigms
1(1)
1.2 Classification of Computational Intelligence Algorithms
2(35)
1.2.1 Global Search and Optimization Algorithms
3(15)
1.2.1.1 Evolutionary Computation Algorithms
4(5)
1.2.1.2 Ecology-Based Algorithms
9(2)
1.2.1.3 Bio-Inspired Algorithms
11(7)
1.2.2 Machine Learning and Connectionist Algorithms
18(11)
1.2.2.1 Artificial Neural Networks
26(2)
1.2.2.2 Artificial Intelligence
28(1)
1.2.3 Approximate Reasoning Approaches
29(4)
1.2.3.1 Fuzzy Logic
29(2)
1.2.3.2 Chaos Theory
31(2)
1.2.4 Conditioning Approximate Reasoning Approaches
33(4)
1.2.4.1 Hidden Markov Models
34(1)
1.2.4.2 Bayesian Belief Networks
35(2)
1.3 Role of CI Paradigms in Engineering Applications
37(3)
1.3.1 Aerospace and Electronics
37(1)
1.3.2 Computer Networking
37(1)
1.3.3 Consumer Electronics
38(1)
1.3.4 Control Systems
38(1)
1.3.5 Electric Power Systems
38(1)
1.3.6 Image Processing
38(1)
1.3.7 Medical Imaging
39(1)
1.3.8 Robotics and Automation
39(1)
1.3.9 Wireless Communication Systems
39(1)
1.4 Applications of CI Focused in This Book
40(11)
1.4.1 Unit Commitment and Economic Load Dispatch Problem
40(2)
1.4.2 Harmonic Reduction in Power Systems
42(2)
1.4.3 Voltage and Frequency Control in Power Generating Systems
44(3)
1.4.4 Job Shop Scheduling Problem
47(2)
1.4.5 Multidepot Vehicle Routing Problem
49(1)
1.4.6 Digital Image Watermarking
50(1)
1.5 Summary
51(1)
References
52(7)
2 Unit Commitment and Economic Load Dispatch Problem 59(92)
Learning Objectives
59(1)
2.1 Introduction
59(2)
2.2 Economic Operation of Power Generation
61(1)
2.3 Mathematical Model of the UC-ELD Problem
62(2)
2.4 Intelligent Algorithms for Solving UC-ELD
64(19)
2.4.1 UC Scheduling Using Genetic Algorithm
64(3)
2.4.2 Fuzzy c-Means-Based Radial Basis Function Network for ELD
67(3)
2.4.2.1 Fuzzy c-Means Clustering
67(2)
2.4.2.2 Implementation of FCM-Based RBF for the ELD Problem
69(1)
2.4.3 Solution to ELD Using Enhanced Particle Swarm Optimization Algorithm
70(3)
2.4.4 Improved Differential Evolution with Opposition-Based Learning for ELD
73(4)
2.4.4.1 Concept of OBL
73(1)
2.4.4.2 IDE-OBL for ELD
74(3)
2.4.5 ELD Using Artificial Bee Colony Optimization
77(2)
2.4.5.1 Employed Bees Phase
77(1)
2.4.5.2 Onlooker Bee Phase
78(1)
2.4.5.3 Scout Bee Phase
79(1)
2.4.6 ELD Based on Cuckoo Search Optimization
79(4)
2.4.6.1 Breeding Behavior
80(1)
2.4.6.2 Levy Flights
80(1)
2.4.6.3 Search Mechanism
81(1)
2.4.6.4 ELD Using CSO
82(1)
2.5 MATLAB® m-File Snippets for UC-ELD Based on CI Paradigms
83(46)
2.5.1 Test Systems
83(1)
2.5.1.1 Six-Unit Test System
83(1)
2.5.1.2 Ten-Unit Test System
83(1)
2.5.1.3 Fifteen-Unit Test System
83(1)
2.5.1.4 Twenty-Unit Test System
84(1)
2.5.2 GA-Based UC Scheduling
84(5)
2.5.2.1 Six-Unit Test System
86(1)
2.5.2.2 Ten-Unit Test System
86(1)
2.5.2.3 Fifteen-Unit Test System
87(1)
2.5.2.4 Twenty-Unit Test System
88(1)
2.5.3 ELD Using FRBFN
89(10)
2.5.3.1 Six-Unit Test System
91(2)
2.5.3.2 Ten-Unit System
93(2)
2.5.3.3 Fifteen-Unit Test System
95(3)
2.5.3.4 Twenty-Unit Test System
98(1)
2.5.4 EPSO for Solving ELD
99(9)
2.5.4.1 Six-Unit Test System
103(2)
2.5.4.2 Ten-Unit Test System
105(1)
2.5.4.3 Fifteen-Unit Test System
106(1)
2.5.4.4 Twenty-Unit Test System
107(1)
2.5.5 DE-OBL and IDE-OBL for Solving ELD
108(8)
2.5.5.1 Six-Unit Test System
111(2)
2.5.5.2 Ten Unit Test System
113(1)
2.5.5.3 Fifteen-Unit Test System
114(1)
2.5.5.4 Twenty-Unit Test System
115(1)
2.5.6 ABC-Based ELD
116(8)
2.5.6.1 Six-Unit Test System
120(1)
2.5.6.2 Ten-Unit Test System
121(1)
2.5.6.3 Fifteen-Unit Test System
122(1)
2.5.6.4 Twenty-Unit Test System
123(1)
2.5.7 CSO-Based ELD
124(5)
2.5.7.1 Six-Unit Test System
126(1)
2.5.7.2 Ten-Unit Test System
127(1)
2.5.7.3 Fifteen-Unit Test System
127(1)
2.5.7.4 Twenty-Unit Test System
128(1)
2.6 Discussion
129(5)
2.6.1 Optimal Fuel Cost and Robustness
129(3)
2.6.2 Computational Efficiency
132(1)
2.6.3 Algorithmic Efficiency
133(1)
2.7 Advantages of CI Algorithms
134(14)
2.8 Summary
148(1)
References
149(2)
3 Harmonic Reduction in Power Systems 151(110)
Learning Objectives
151(1)
3.1 Harmonic Reduction in Power System
151(1)
3.2 Harmonic Effects
152(1)
3.3 Harmonics Limits and Standards
153(2)
3.4 Method to Eliminate Harmonics
155(3)
3.4.1 Passive Filters
155(1)
3.4.2 Phase Multiplication Technique
155(1)
3.4.3 Active Power Filters
155(2)
3.4.3.1 Drawbacks of Using APF
156(1)
3.4.4 Hybrid Active Filters
157(1)
3.4.5 PWM Rectifier
158(1)
3.5 Voltage Source Inverter-Fed Induction Motor Drives
158(12)
3.5.1 Two-Pulse Rectifier Drive
159(3)
3.5.1.1 Two-Pulse Rectifier Drive Operation
159(3)
3.5.2 Six-Pulse Rectifier Drive
162(1)
3.5.3 Twelve-Pulse Rectifier Drive
162(8)
3.5.3.1 PWM Inverter
166(4)
3.5.4 Selection of PWM Switching Frequency
170(1)
3.6 Case Study: Pulp and Paper Industry
170(27)
3.6.1 Power Distribution
171(1)
3.6.2 Harmonics Measurement at Grid and Turbo Generators
172(4)
3.6.3 Harmonic Measurement at Recovery Boiler Distribution
176(7)
3.6.3.1 Six-Pulse Drive Harmonic Study
177(2)
3.6.3.2 Twelve-Pulse Drive Harmonic Study
179(4)
3.6.4 MATLAB®/Simulink® Model for 6-Pulse Drive
183(6)
3.6.5 MATLAB®/Simulink® Model for 12-Pulse Drive
189(8)
3.7 Genetic Algorithm-Based Filter Design in 2-, 6-, and 12-Pulse Rectifier
197(40)
3.7.1 Problem Formulation
197(1)
3.7.2 Genetic Algorithm-Based Filter Design
198(3)
3.7.2.1 Steps Involved in GA Based Filter Design
198(3)
3.7.3 MATLAW/Simulink® Model of Filters
201(1)
3.7.4 Series LC Filter Configuration
201(3)
3.7.5 Parallel LC Filter Configuration
204(2)
3.7.5.1 Determination of L and C Values Using Conventional Method
204(2)
3.7.6 Shunt LC Filter Configuration
206(1)
3.7.7 LCL Filter Configuration
207(6)
3.7.7.1 Analysis of the Observations
209(1)
3.7.7.2 Comparison of Results
210(1)
3.7.7.3 Input LCL Filter for Two-pulse Drive
211(2)
3.7.8 Genetic Algorithm-Based Filter Design in 6- and 12-Pulse Rectifier-Fed Drive
213(24)
3.7.8.1 Steps for Genetic Algorithm
216(1)
3.7.8.2 Simulation Results
217(1)
3.7.8.3 Experimental Results
218(1)
3.7.8.4 Output Sine Wave Filter Design in 6-Pulse Drive
219(7)
3.7.8.5 Input LC Filter Design in 12-Pulse Drive
226(11)
3.8 Bacterial Foraging Algorithm for Harmonic Elimination
237(17)
3.8.1 Inverter Operation
238(3)
3.8.1.1 Mode of Operations
238(3)
3.8.2 Basics of Bacterial Foraging Algorithm
241(3)
3.8.2.1 Chemotaxis
242(1)
3.8.2.2 Swarming
242(1)
3.8.2.3 Reproduction
243(1)
3.8.2.4 Elimination and Dispersal
243(1)
3.8.3 Application of BFA for Selective Harmonic Elimination Problem
244(2)
3.8.4 Performance Analysis of BFA for Selective Harmonic Elimination
246(19)
3.8.4.1 Convergence Characteristics
247(3)
3.8.4.2 Solution Quality
250(1)
3.8.4.3 Experimental Validation of BFA Results
251(3)
3.9 Summary
254(1)
References
255(6)
4 Voltage and Frequency Control in Power Systems 261(106)
Learning Objectives
261(1)
4.1 Introduction
261(3)
4.2 Scope of Intelligent Algorithms in Voltage and Frequency Control
264(1)
4.3 Dynamics of Power Generating System
265(20)
4.3.1 Control of Active and Reactive Power
267(2)
4.3.2 Modeling of Synchronous Generator
269(1)
4.3.3 Modeling of LFC
269(5)
4.3.3.1 Generator Model
269(1)
4.3.3.2 Load Model
270(1)
4.3.3.3 Prime Mover Model
271(1)
4.3.3.4 Governor Model
272(2)
4.3.4 Modeling of AVR
274(5)
4.3.4.1 Amplifier Model
277(1)
4.3.4.2 Exciter Model
277(1)
4.3.4.3 Generator Model
277(1)
4.3.4.4 Sensor Model
278(1)
4.3.4.5 Excitation System Stabilizer
278(1)
4.3.5 Interconnection of Power Systems
279(6)
4.3.5.1 AGC in Multiarea System
281(2)
4.3.5.2 Tie-Line Bias Control
283(2)
4.4 Fuzzy Logic Controller for LFC and AVR
285(15)
4.4.1 Basic Generator Control Loops
286(1)
4.4.1.1 LFC Loop
286(1)
4.4.1.2 AVR Loop
287(1)
4.4.2 Design of Intelligent Controller Using MATLAWD/Simulink®
287(1)
4.4.3 Fuzzy Controller for Single-Area Power System
288(8)
4.4.3.1 Fuzzification
291(1)
4.4.3.2 Knowledge Base
292(3)
4.4.3.3 Defuzzification
295(1)
4.4.4 Fuzzy Controller for Single-Area Power System
296(1)
4.4.5 Fuzzy Controller for Two-Area Power System
297(3)
4.5 Genetic Algorithm for LFC and AVR
300(9)
4.5.1 Design of GA-Based PID Controller
300(2)
4.5.1.1 GA Design Procedure
300(2)
4.5.2 Simulink® Model of Single-Area Power System
302(5)
4.5.3 Simulink® Model of Interconnected Power System
307(2)
4.6 PSO and ACO for LFC and AVR
309(16)
4.6.1 Evolutionary Algorithms for Power System Control
309(1)
4.6.2 ACO-Based PID Controller
310(2)
4.6.3 PSO-Based PID Controller
312(3)
4.6.4 Simulink® Model of an AVR
315(1)
4.6.5 Simulink® Model of LFC
315(1)
4.6.6 Effect of PID Controller Using ACO
315(4)
4.6.7 Impact of PSO-Based PID Controller
319(3)
4.6.8 Simulation Model for LFC in a Two-Area Power System
322(3)
4.7 Hybrid Evolutionary Algorithms for LFC and AVR
325(35)
4.7.1 Design of EA-Based Controller Using MATLAB®/Simulink®
326(3)
4.7.2 EPSO-Based PID Controller
329(4)
4.7.3 MO-PSO-Based PID Controller
333(4)
4.7.4 SPSO-Based PID Controller
337(3)
4.7.5 FPSO-Based PID Controller
340(5)
4.7.6 BF-PSO-Based PID Controller
345(4)
4.7.7 Hybrid Genetic Algorithm-Based PID Controller
349(5)
4.7.8 Performance Comparison of Single-Area System
354(2)
4.7.9 Performance Comparison of Two-Area System
356(3)
4.7.10 Computational Efficiency of EAs
359(1)
4.8 Summary
360(1)
References
360(7)
5 Job Shop Scheduling Problem 367(54)
Learning Objectives
367(1)
5.1 Introduction
367(2)
5.2 Formulation of JSSP
369(5)
5.2.1 Problem Description
370(1)
5.2.2 Mathematical Model of JSSP
371(2)
5.2.3 Operation of the Job Shop Scheduling System
373(1)
5.2.3.1 Scheduling of Job Sequences
373(1)
5.2.3.2 Makespan Optimization
374(1)
5.3 Computational Intelligence Paradigms for JSSP
374(12)
5.3.1 Lambda Interval—Based Fuzzy Processing Time
375(5)
5.3.1.1 Preliminaries
375(2)
5.3.1.2 JSSP with Fuzzy Processing Time
377(3)
5.3.2 Genetic Algorithm for JSSP
380(1)
5.3.3 Solving JSSP Using Stochastic Particle Swarm Optimization
381(2)
5.3.4 Ant Colony Optimization for JSSP
383(2)
5.3.5 JSSP Based on Hybrid SPSO
385(1)
5.4 m-File Snippets and Outcome of JSSP Based on CI Paradigms
386(26)
5.4.1 Description of Benchmark Instances
387(1)
5.4.2 MATLAB® m-File Snippets
388(2)
5.4.3 Fuzzy Processing Time Based on (λ, 1) Interval Fuzzy Numbers
390(1)
5.4.4 Performance of GA-Based JSSP
391(1)
5.4.5 Analysis of JSSP Using SPSO
392(10)
5.4.6 Outcome of JSSP Based on ACO
402(1)
5.4.7 Investigation of GSO on JSSP
402(10)
5.5 Discussion
412(3)
5.5.1 Optimal Makespan
412(1)
5.5.2 Computational Efficiency
413(1)
5.5.3 Mean Relative Error
414(1)
5.6 Advantages of CI Paradigms
415(1)
5.7 Summary
415(1)
References
416(5)
6 Multidepot Vehicle Routing Problem 421(56)
Learning Objectives
421(1)
6.1 Introduction
421(3)
6.2 Fundamental Concepts of MDVRP
424(6)
6.2.1 Mathematical Formulation of MDVRP
425(2)
6.2.2 Grouping Assignment
427(1)
6.2.3 Routing Algorithm
428(2)
6.3 Computational Intelligence Algorithms for MDVRP
430(12)
6.3.1 Solution Representation and Fitness Function
430(2)
6.3.2 Implementation of MDVRP Using GA
432(2)
6.3.2.1 Initial Population
432(1)
6.3.2.2 Selection
432(1)
6.3.2.3 Crossover
433(1)
6.3.2.4 Mutation
433(1)
6.3.3 Solving MDVRP Using MPSO
434(1)
6.3.3.1 Solution Representation and Fitness Evaluation
434(1)
6.3.3.2 Updating Particles
434(1)
6.3.3.3 Algorithm
435(1)
6.3.4 Artificial Bee Colony-Based MDVRP
435(3)
6.3.4.1 Generation of Initial Solutions
436(1)
6.3.4.2 Constraints Handling
436(1)
6.3.4.3 Neighborhood Operators
436(2)
6.3.5 GSO for MDVRP
438(1)
6.3.5.1 Solution Coding and Fitness Evaluation
438(1)
6.3.5.2 Offspring Generation
439(1)
6.3.5.3 Stopping Condition
439(1)
6.3.6 IGSO for MDVRP
439(3)
6.3.6.1 Solution Coding and Fitness Evaluation
441(1)
6.3.6.2 Enhancement
441(1)
6.3.6.3 Selection
441(1)
6.3.6.4 Offspring Generation
441(1)
6.3.6.5 Stopping Condition
441(1)
6.4 MATLAB® m-File Snippets for MDVRP Based on CI Paradigms
442(18)
6.4.1 Experimental Benchmark Instances
443(1)
6.4.2 Grouping and Routing
443(5)
6.4.3 Impact of GA on MDVRP
448(4)
6.4.4 Evaluation of MPSO for MDVRP
452(1)
6.4.5 Solution of MDVRP Based on ABC
453(4)
6.4.6 MDVRP based on GSO and IGSO
457(3)
6.5 Discussions
460(5)
6.5.1 Optimal Distance
460(2)
6.5.2 Robustness
462(1)
6.5.3 Computational Time
462(2)
6.5.4 Algorithmic Efficiency
464(1)
6.6 Advantages of CI Paradigms
465(1)
6.7 Summary
466(1)
References
466(11)
7 Digital Image Watermarking 477(64)
Learning Objectives
477(1)
7.1 Introduction
477(4)
7.2 Basic Concepts of Image Watermarking
481(1)
7.2.1 Properties of Digital Watermarking Technique
481(1)
7.2.2 Watermarking Applications
482(1)
7.3 Preprocessing Schemes
482(5)
7.3.1 Image Segmentation
483(2)
7.3.2 Feature Extraction
485(1)
7.3.3 Orientation Assignment
485(1)
7.3.4 Image Normalization
486(1)
7.4 Discrete Wavelet Transform for DIWM
487(3)
7.4.1 Watermark Embedding
487(2)
7.4.2 Watermark Extraction
489(1)
7.5 Performance Metrics
490(2)
7.5.1 Perceptual Image Quality Metrics
491(1)
7.5.2 Robustness Evaluation
492(1)
7.6 Application of CI Techniques for DIWM
492(7)
7.6.1 Genetic Algorithm
493(1)
7.6.2 Particle Swarm Optimization
494(2)
7.6.2.1 Watermark Embedding Using PSO
494(2)
7.6.2.2 Watermark Extraction
496(1)
7.6.3 Hybrid Particle Swarm Optimization
496(3)
7.7 MATLAB® m-File Snippets for DIWM Using CI Paradigms
499(8)
7.7.1 Feature Extraction Using Difference of Gaussian
501(1)
7.7.2 Orientation and Normalization
502(3)
7.7.3 DWT Watermark Embedding and Extraction
505(2)
7.8 Optimization in Watermarking
507(23)
7.8.1 Genetic Algorithm
508(4)
7.8.1.1 Variation in the Number of Generations
508(1)
7.8.1.2 Variation in the Population Size
509(1)
7.8.1.3 Variation in Crossover Rate
509(1)
7.8.1.4 Variation in Mutation Rate
510(2)
7.8.2 Particle Swarm Optimization
512(2)
7.8.2.1 Effect of Number of Iterations
512(1)
7.8.2.2 Effect of Acceleration Constants c1 and c2
512(1)
7.8.2.3 Effect of Inertia Weight w
513(1)
7.8.3 Hybrid Particle Swarm Optimization
514(2)
7.8.4 Robustness against Watermarking Attacks
516(5)
7.8.4.1 Filtering Attacks
516(1)
7.8.4.2 Additive Noise
517(1)
7.8.4.3 JPEG Compression
518(1)
7.8.4.4 Rotation
519(1)
7.8.4.5 Scaling
520(1)
7.8.4.6 Combination of Attacks
520(1)
7.8.5 PSNR Computation
521(4)
7.8.6 DIWM with Real-Time Color Image
525(5)
7.8.6.1 Image Segmentation Using Expectation Maximization
527(1)
7.8.6.2 Feature Extraction Using Difference of Gaussian
527(1)
7.8.6.3 DWT Watermark Embedding and Extraction
527(1)
7.8.6.4 Genetic Algorithm
528(1)
7.8.6.5 Particle Swarm Optimization
529(1)
7.8.6.6 Hybrid Particle Swarm Optimization
530(1)
7.9 Discussion
530(4)
7.9.1 Perceptual Transparency of Gray-Scale Images
531(1)
7.9.2 Robustness Measure of Gray-Scale Images
531(2)
7.9.3 PSNR and NCC of Real-Time Color Image
533(1)
7.9.4 Computational Efficiency
534(1)
7.10 Advantages of CI Paradigms
534(1)
7.11 Summary
535(1)
References
536(5)
Appendix A: Unit Commitment and Economic Load Dispatch Test Systems 541(8)
Appendix B: Harmonic Reduction—MATLAB®/Simulink® Models 549(10)
Appendix C: MATLAB®/Simulink® Functions—An Overview 559(6)
Appendix D: Instances of Job-Shop Scheduling Problems 565(6)
Appendix E: MDVRP Instances 571(12)
Appendix F: Image Watermarking Metrics and Attacks 583(6)
Index 589
S. Sumathi completed her BE in Electronics and Communication Engineering and her ME in Applied Electronics at the Government College of Technology, Coimbatore. She earned her PhD in the area of Data Mining and is an Associate Professor in the Department of Electrical and Electronics Engineering at PSG College of Technology, Coimbatore. Widely published and highly decorated, Dr. Sumathi has 25 years of teaching and research experience. Her research interests include neural networks, fuzzy systems and genetic algorithms, pattern recognition and classification, data warehousing and data mining, and operating systems and parallel computing.

L. Ashok Kumar completed his graduate program in Electrical and Electronics Engineering, his postgraduate studies with an Electrical Machines major, his MBA with a specialization in Human Resource Development, and his PhD in Wearable Electronics. He was previously a project engineer at ITC Limited, Paperboards and Specialty Papers Division, Kovai Unit, Coimbatore. Widely published and highly decorated, Dr. Ashok is currently a Professor in the Department of Electrical and Electronics Engineering at PSG College of Technology, Coimbatore. His research areas include wearable electronics, solar PV and wind energy systems, textile control engineering, smart grid, energy conservation and management, and power electronics and drives.

Surekha P. completed her BE in Electrical and Electronics Engineering at PARK College of Engineering and Technology, Coimbatore, and her masters degree in Control Systems at PSG College of Technology, Coimbatore. She earned her PhD in Computational Intelligence for Electrical Engineering Applications at Anna University, Chennai. Widely published and highly decorated, Dr. Surekha P. is an Associate Professor in the Department of Electrical and Electronics Engineering at PES University, Bangalore. A member of several technical bodies, she is a popular reviewer of journal and IEEE-sponsored conference publications. Her areas of research include robotics, virtual instrumentation, control systems, smart grid, evolutionary algorithms, and computational intelligence.
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