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Artificial Intelligence Applications in Electrical Transmission and Distribution Systems Protection [Kõva köide]

Edited by (Faculty of Engineering, Ain Shams University, Cairo, Egypt), Edited by (Department of Electrical Engineering National Institute of Technology Raipur, India), Edited by (15th of May Higher Institute of Engineering, Cairo, Egypt)
  • Formaat: Hardback, 482 pages, kõrgus x laius: 234x156 mm, kaal: 1110 g, 86 Tables, black and white; 235 Line drawings, black and white; 9 Halftones, black and white; 244 Illustrations, black and white
  • Ilmumisaeg: 22-Oct-2021
  • Kirjastus: CRC Press
  • ISBN-10: 0367552345
  • ISBN-13: 9780367552343
Teised raamatud teemal:
Artificial Intelligence Applications in Electrical Transmission and Distribution Systems ProtectionSuurem pilt
  • Formaat: Hardback, 482 pages, kõrgus x laius: 234x156 mm, kaal: 1110 g, 86 Tables, black and white; 235 Line drawings, black and white; 9 Halftones, black and white; 244 Illustrations, black and white
  • Ilmumisaeg: 22-Oct-2021
  • Kirjastus: CRC Press
  • ISBN-10: 0367552345
  • ISBN-13: 9780367552343
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367552343.html
Märksõnad:
"Artificial Intelligence (AI) can successfully help in solving real-world problems in power transmission and distribution systems as AI-based schemes are fast, adaptive, and robust and are applicable without any knowledge of the system parameters. This book considers the application of AI methods for the protection of different types and topologies of transmission and distribution lines. It explains the latest pattern-recognition- based methods as applicable to detection, classification, and location of a fault in the transmission and distribution lines, and to manage smart power systems including all the pertinent aspects. Features: Provides essential insight on uses of different AI techniques for pattern recognition, classification, prediction, and estimation, exclusive to power system protection issues. Presents introduction to enhanced electricity system analysis using decision-making tools. Covers AI Applications in different protective relaying functions. Discusses Issues and challenges in the protection of transmission and distribution systems. Includes dedicated chapter on case studies, and applications. This book is aimed at Graduate students, Researchers and Professionals in Electrical Power System Protection, Stability, and Smart Grids"--

The focus of this book is on the application of AI methods in the field of protection of different types and topologies of transmission and distribution lines explaining the latest pattern-recognition- based methods applied to detection, classification, and location of a fault in the transmission and distribution lines, for smart power systems.

Artificial Intelligence (AI) can successfully help in solving real-world problems in power transmission and distribution systems as AI-based schemes are fast, adaptive, and robust and are applicable without any knowledge of the system parameters. This book considers the application of AI methods for the protection of different types and topologies of transmission and distribution lines. It explains the latest pattern-recognition- based methods as applicable to detection, classification, and location of a fault in the transmission and distribution lines, and to manage smart power systems including all the pertinent aspects.

Features:

  • Provides essential insight on uses of different AI techniques for pattern recognition, classification, prediction, and estimation, exclusive to power system protection issues.

  • Presents introduction to enhanced electricity system analysis using decision-making tools.

  • Covers AI Applications in different protective relaying functions.

  • Discusses Issues and challenges in the protection of transmission and distribution systems.

  • Includes dedicated chapter on case studies, and applications.

This book is aimed at Graduate students, Researchers and Professionals in Electrical Power System Protection, Stability, and Smart Grids.

Preface xvii
Editors xxv
Contributors xxvii
Chapter 1 Application of Metaheuristic Algorithms in Various Aspects of Electrical Transmission and Systems Protection 1(32)
Vijay Kale
Anamika Yadav
Prashant Bedekar
1.1 Introduction
1(1)
1.2 Mathematical Representation of Optimization Problem
2(1)
1.3 Metaheuristic Algorithms
3(2)
1.4 Optimal Relay Coordination
5(6)
1.4.1 Formulation of Relay Coordination Problem
6(3)
1.4.2 Illustrative Example
9(1)
1.4.3 State of Research in Optimal Relay Coordination
10(1)
1.5 Optimal PMU Placement
11(5)
1.5.1 Formulation of PMU Placement Problem
12(1)
1.5.2 Illustrative Example
13(2)
1.5.3 State of Research in Problem of PMU Placement
15(1)
1.6 Estimation of Fault Section on Distribution Network
16(7)
1.6.1 Formulation of Fault Section Estimation Problem as an Optimization Problem
16(2)
1.6.2 Illustrative Example
18(4)
1.6.3 State of Research in Fault Section Estimation
22(1)
1.7 Estimation of Fault Location on Transmission Lines
23(4)
1.7.1 Formulation of Fault Location Estimation Problem as an Optimization Problem
24(1)
1.7.2 Illustrative Example
25(1)
1.7.3 State of Research In Fault Location Estimation
26(1)
1.8 Conclusion
27(1)
References
28(5)
Chapter 2 AI-based Scheme for the Protection of Power Systems Networks Due to Incorporation of Distributed Generations 33(26)
Bhavesh Kumar R. Bhalja
Yogesh M. Makwana
2.1 Introduction to Distributed Generation (DG)
33(4)
2.1.1 What is Distributed Generating (DG)?
33(1)
2.1.2 Advantages of DG Over Conventional Power Generation
34(2)
2.1.3 Applications of DG
36(1)
2.2 Impact of Integration of Distributed Generation on the Power System
37(1)
2.3 Problems During DG Interconnection
37(2)
2.3.1 Operating (Economic) Issues
37(1)
2.3.2 Technical Issues
38(1)
2.3.3 Protection/Safety Issues
38(1)
2.4 Islanding (Formation of Electrical Island)
39(2)
2.4.1 Power Quality Issue
40(1)
2.4.2 Personnel Safety
40(1)
2.4.3 Out of Synchronism Reclose
41(1)
2.5 Islanding Detection
41(2)
2.5.1 Remote Method
41(1)
2.5.2 Active Islanding Detection Method
42(1)
2.5.3 Passive Islanding Detection Method
42(1)
2.5.4 Hybrid Method of Islanding Detection
43(1)
2.6 Application of Artificial Intelligence for Islanding Detection
43(4)
2.6.1 Fuzzy Logic
44(1)
2.6.2 Artificial Neural Network (ANN)
45(1)
2.6.3 Machine Learning Classifier
46(1)
2.7 Case Study of Classifier (Machine Learning)-Based Islanding Detection
47(5)
2.7.1 Relevance Vector Machine
48(1)
2.7.2 Simulation and Test Cases
49(1)
2.7.3 Feature Vector Formation
50(1)
2.7.4 Training of RVM Classifier
51(1)
2.7.5 Result and Discussion
52(1)
2.8 Protection Miscoordination Due to DG Interconnection
52(3)
2.8.1 Issue of Protection Miscoordination
52(2)
2.8.2 Application of AI Technique for Restoration of Protection Coordination
54(1)
2.9 Summary
55(1)
References
55(4)
Chapter 3 An Intelligent Scheme for Classification of Shunt Faults Including Atypical Faults in Double-Circuit Transmission Line 59(20)
Valabhoju Ashok
Anamika Yadav
Mohammad Pazoki
Almoataz Y. Abdelaziz
3.1 Introduction
59(3)
3.2 Description of an Indian Power System Network
62(1)
3.3 Ensemble Tree Classifier (ETC) Model for Classification of CSFs, CCFs, and EVFs
63(7)
3.3.1 Designing of Exclusive Data Sets
63(5)
3.3.2 Discrete Wavelet Transform (DWT)
68(1)
3.3.3 Bagged Decision Tree
68(1)
3.3.4 Boosted Decision Tree
69(1)
3.3.5 Training/Validation of Proposed ETC Model
69(1)
3.4 Comparative Assessment of Proposed ETC-Model based Classifier Modules
70(3)
3.5 Relative Assessment of Proposed Scheme with Other AI Technique-based Fault Classification Schemes
73(2)
3.6 Effect of Variation in Sampling Rate on Performance of Proposed Classification Scheme
75(1)
3.7 Conclusion
76(1)
References
77(2)
Chapter 4 An Artificial Intelligence -Based Detection and Classification of Faults on Transmission Lines 79(32)
Dalia Allam
Almoataz Y. Abdelaziz
4.1 Introduction
79(5)
4.2 The Basic Concepts of Distance Protection
84(7)
4.2.1 Causes of Current Increase Upon Fault Occurrence
84(1)
4.2.2 Causes of Faults
84(4)
4.2.3 Types of Faults
88(1)
4.2.4 Sources of Errors In Detection and Classification of Faults
88(3)
4.2.5 Distance Relay Mho Characteristic
91(1)
4.3 AI-Based Fault Diagnosis System
91(16)
4.3.1 Training Data For Artificial Neural Network: (Input/target) Pairs
93(1)
4.3.2 Feed Forward Artificial Neural Network
94(6)
4.3.2.1 Multi-Layer Perceptron Neural Network
96(1)
4.3.2.2 Radial Basis Function Network
97(1)
4.3.2.3 Chebyshev Neural Network
97(1)
4.3.2.4 Probabilistic Neural Network as a Detailed Example of FFNN
97(3)
4.3.3 Support Vector Machine as an Example of ML
100(3)
4.3.4 Convolution Neural Network as an Example of DL
103(4)
4.4 Conclusion
107(1)
References
108(3)
Chapter 5 Intelligent Fault Location Schemes for Modern Power Systems 111(42)
Tamer A. Kawady
Mahmoud A. Elsadd
Nagy I. Elkalashy
5.1 Introduction
111(1)
5.2 Conventional Fault Location Review
112(6)
5.2.1 Traveling Wave-Based Fault Locators
112(2)
5.2.2 Impedance Measurement-Based Fault Locators
114(3)
5.2.3 Requirements for Fault Location Process
117(1)
5.3 AI-based Fault Location Schemes
118(11)
5.3.1 ANN-Based Fault Location Computation
119(5)
5.3.2 FL-Based Fault Location Computation
124(1)
5.3.3 GA-Based Fault Location Computation
125(3)
5.3.4 WT-Based Fault Location Computation
128(1)
5.4 Recent Trends in Distribution Network and Smart Grid Requirements
129(4)
5.5 Smart Fault Location Techniques
133(6)
5.5.1 Fault Indicators
134(1)
5.5.2 Distributed Smart Meters
135(1)
5.5.3 IoT for Data Collections
136(1)
5.5.4 Unmanned Aerial Vehicles (Drones)
137(2)
5.6 Concluding Remarks
139(4)
References
143(10)
Chapter 6 An Integrated Approach for Fault Detection, Classification and Location in Medium Voltage Underground Cables 153(26)
M. Karthikeyan
R. Rengaraj
6.1 Introduction
153(1)
6.2 Autoregressive Modeling
154(4)
6.3 Extreme Learning Machine
158(3)
6.3.1 Training Extreme Learning Machine
161(1)
6.4 Integrated Approach of the Protection Scheme
161(2)
6.5 Test System
163(2)
6.5.1 Simulation Parameters for Training and Testing
165(1)
6.6 Fault Detection
165(1)
6.7 Fault Classification
166(4)
6.8 Fault Location
170(1)
6.9 Results and Discussion
171(3)
6.9.1 Comparative Evaluation
172(2)
6.10 Summary
174(2)
References
176(3)
Chapter 7 A New High Impedance Fault Detection Technique Using Deep Learning Neural Network 179(18)
M.M. Eissa
M.H. Awadalla
A.M. Sharaf
7.1 Introduction
179(1)
7.2 Fault Model
180(1)
7.3 The Proposed Deep Learning Approach
180(8)
7.4 The Simulated Experiments and Discussions
188(2)
7.5 Case Study
190(2)
7.6 Conclusions
192(2)
Appendix
194(1)
References
194(3)
Chapter 8 AI-based Scheme for the Protection of Multi-Terminal Transmission Lines 197(24)
Bhavesh Kumar R. Bhalja
8.1 Introduction to Multi-Terminal Transmission Line
197(1)
8.2 Need of a Multi-Terminal Transmission Line
198(1)
8.2.1 Benefits of a Multi-Terminal Transmission Line
199(1)
8.2.2 Limitations of a Multi-Terminal Transmission Line
199(1)
8.2.3 Protection and Other Technical Issues with Multi-Terminal Transmission Line
199(1)
8.3 Conventional Protection Schemes
199(4)
8.3.1 Distance Protection Scheme
200(2)
8.3.2 Current Differential Scheme
202(1)
8.4 Advanced Multi-End Protection Schemes
203(7)
8.4.1 Synchronized and Unsynchronized Measurement-based Schemes
203(3)
8.4.2 Fundamental and Transient Frequency-based Schemes
206(4)
8.5 AI or Knowledge-based Schemes
210(5)
8.5.1 ANN-Based Schemes
211(1)
8.5.2 Fuzzy Interference Systems
211(1)
8.5.3 Support Vector Machine-based Schemes
211(4)
8.6 Adaptive Protection Schemes
215(4)
8.7 Conclusions
219(1)
References
219(2)
Chapter 9 Data Mining-Based Protection Methodologies for Series Compensated Transmission Network 221(16)
S.K. Singh
D.N. Vishwakarma
R.K. Saket
9.1 Introduction
221(1)
9.2 Relaying Challenges in Series Compensated Transmission Network
222(3)
9.2.1 Under- and Overreaching of Relays
222(1)
9.2.2 Current and Voltage Inversion
223(1)
9.2.3 Precarious Operation of MOV
223(1)
9.2.4 Harmonics and Transients
224(1)
9.3 Data Mining-Based Protection Mechanism
225(2)
9.3.1 DWT and Non-Parametric ML (KNN) based Fault Events Classification Scheme
226(1)
9.3.2 DWT and Non-Parametric ML (SVM) based Fault Events Classification Scheme
226(1)
9.3.3 DWT and Non-Parametric ML (PNN) based Fault Events Classification Scheme
227(1)
9.4 Feasibility and Competency analysis
227(6)
9.4.1 Transforming Fault Events Identification
228(5)
9.5 Summary
233(1)
Appendix
234(1)
References
235(2)
Chapter 10 AI-Based Protective Relaying Schemes for Transmission Line Compensated with FACTS Devices 237(18)
Bhupendra Kumar
Anamika Yadav
Almoataz Y. Abdelaziz
10.1 Introduction
237(1)
10.2 FACTS Technology
238(1)
10.3 Protection Issues with FACTS Technology Integration
239(1)
10.4 Overview of AI
240(2)
10.5 AI-based Application in FACTS-Compensated Transmission Line Protection
242(8)
10.5.1 Training Data Collection and Processing
242(2)
10.5.2 Training Algorithms
244(6)
10.6 Conclusion and Perspectives
250(1)
References
250(5)
Chapter 11 AI-based PMUs Allocation for Protecting Transmission Lines 255(36)
Abdelazeem A. Abdelsalam
Karim M. Hassanin
11.1 Introduction
255(1)
11.2 Basics of PMUs and WAMS
256(8)
11.2.1 Basic PMU Structure
256(2)
11.2.2 PMU Placement Rules
258(1)
11.2.3 PMU Placement Problem Formulation
259(5)
11.2.3.1 Case #1: Base Case
259(2)
11.2.3.2 Case #2: Considering ZIBs
261(3)
11.2.3.3 Case #3: Loss of a Single PMU
264(1)
11.2.3.4 Case #4: Single Line Outage
264(1)
11.3 Conventional Mathematical Techniques for PMUs Allocation
264(4)
11.3.1 Exhaustive Search
264(1)
11.3.2 Integer Programming
265(3)
11.3.3 Integer Quadratic Programming
268(1)
11.4 AI Application to PMUs Allocation
268(1)
11.5 Case Study
269(14)
11.5.1 IEEE 14-Bus System
269(11)
11.5.1.1 Case #1: Base case
269(1)
11.5.1.2 Case #2: Considering ZIBs
269(10)
11.5.1.3 Case #3: Loss of a Single PMU
279(1)
11.5.1.4 Case #4: Single Line Outage
279(1)
11.5.2 IEEE 30-Bus System
280(15)
11.5.2.1 Case #1: Base case
280(1)
11.5.2.2 Case #2: Considering ZIBs
280(1)
11.5.2.3 Case #3: Loss of a single PMU
280(2)
11.5.2.4 Case #4: Single line Outage
282(1)
11.6 Application of PMUs in Protecting Transmission Lines
283(1)
References
284(7)
Chapter 12 An Expert System for Optimal Coordination of Directional Overcurrent Relays in Meshed Networks 291(20)
Hajjar A. Ammar
12.1 Introduction
291(1)
12.2 Importance of the ES and its Objectives
292(1)
12.3 Problem Formulation of the Optimal Coordination of DOCR
293(2)
12.4 Structure of the Introduced ES
295(1)
12.4.1 The Mechanism by Which the Introduced ES Work
296(1)
12.5 An ES for Optimal Coordination of DOCR
296(1)
12.5.1 Optimal Coordination Facts
296(1)
12.5.2 Optimal Coordination Rules
296(1)
12.6 Verification of the Introduced ES
297(12)
12.6.1 IEEE 3-Bus Test System
298(4)
12.6.2 The 8-Bus Test System
302(5)
12.6.3 The IEEE 5-Bus Test System
307(2)
12.7 Conclusion
309(1)
References
309(2)
Chapter 13 Optimal Overcurrent Relay Coordination Considering Standard and Non-Standard Characteristics 311(28)
Ahmed Korashy
Salah Kamel
Loai Nasrat
Francisco Jurado
13.1 Introduction
312(1)
13.1.1 Methods for Coordination of DOCRs
313(1)
13.2 DOCRs Coordination Problem
313(2)
13.2.1 Boundaries of the Coordination Problem
314(1)
13.2.1.1 Limits on Relay Characteristics
314(1)
13.2.1.2 Boundaries on DOCRs Coordination
315(1)
13.3 Recent Optimization Techniques
315(6)
13.3.1 WCA and MWCA
316(3)
13.3.1.1 Conventional WCA
316(2)
13.3.1.2 MWCA algorithm
318(1)
13.3.2 MFO and IMFO Algorithms
319(2)
13.3.2.1 The MFO Algorithm
319(1)
13.3.2.2 The IMFO Algorithm
320(1)
13.4 Results and Discussion
321(13)
13.4.1 Description of Test Systems
322(1)
13.4.1.1 The Nine-Bus Network
322(1)
13.4.1.2 The 15-Bus Test System
323(1)
13.4.2 Formulated the Coordination Problem Using Standard-CRC
323(4)
13.4.2.1 Using MWCA for Solving the Coordination Problem
323(4)
13.4.3 Solving the Problem of Coordination with Conventional CRC and Non-Conventional CRC
327(5)
13.4.3.1 Scenario 1: Using Conventional CRC in Solving the Problem of Coordination
328(4)
13.4.4 Scenario 2: Using Non-Conventional CRC in Solving the Problem of Coordination
332(8)
13.4.4.1 Nine-Bus Network
332(1)
13.4.4.2 15-Bus Network
333(1)
13.5 Conclusions
334(1)
References
334(5)
Chapter 14 Artificial Intelligence Applications in DC Microgrid Protection 339(32)
Morteza Shamsoddini
Behrooz Vahidi
14.1 Introduction
339(1)
14.2 Technical Considerations of DC Microgrid Protection
340(13)
14.2.1 DC Fault Current Characteristics
340(4)
14.2.1.1 Analysis of the First Stage of the Fault Current
341(1)
14.2.1.2 Analysis of the Second Stage of the Fault Current
342(2)
14.2.2 Technical Issues
344(12)
14.2.2.1 Equipment Fault-Tolerant
344(1)
14.2.2.2 Grounding System
344(3)
14.2.2.3 DC Protective Devices
347(5)
14.2.2.4 Protection Algorithm Capabilities
352(1)
14.3 DC Microgrid Protection Approaches
353(3)
14.4 AI-based Approaches Effectiveness Investigation
356(9)
14.4.1 WT Principles
357(2)
14.4.2 Feature Extraction
359(1)
14.4.3 Feature Extraction Results
360(3)
14.4.4 Pattern Recognition with ANN
363(1)
14.4.5 Classification Results
364(1)
14.5 Conclusion
365(1)
References
366(5)
Chapter 15 Soft Computing-Based DC-Link Voltage Control Technique for SAPF in Harmonic and Reactive Power Compensation 371(16)
Pravat Kumar Ray
Sushree Diptimayee Swain
15.1 Introduction
371(1)
15.2 System Topology of SAPF
372(1)
15.3 Reference generation techniques for SAPF system
372(3)
15.3.1 Hybrid Control Approach Based Synchronous Reference Frame Method For Active Filter Design (HSRF)
372(3)
15.4 Design of Proposed Fuzzy Logic Controller in SAPF System
375(1)
15.5 Proposed Controller Design Technique for Switching Pattern Generation in SAPF System
375(4)
15.6 Simulation Results for Harmonic Compensation Using SAPF
379(4)
15.7 Experimental Results
383(2)
15.8 Conclusions
385(1)
References
385(2)
Chapter 16 Artificial Intelligence Application for HVDC Protection 387(32)
Zahra Moravej
Amir Imani
Mohammad Pazoki
16.1 Introduction
387(2)
16.1.1 Protection Tools Based on Artificial Intelligence
388(4)
16.1.1.1 Generation
388(1)
16.1.1.2 Description
389(1)
16.1.1.3 Decision Making
389(1)
16.2 Overview of HVDC Technology
389(3)
16.3 HVDC Protection
392(5)
16.3.1 DC Fault Phenomena
392(4)
16.3.2 Multi-Terminal HVDC Protection
396(1)
16.4 AI-based Fault Detection
397(5)
16.5 AI-based Fault Classification
402(1)
16.6 Al-based Fault Location
403(9)
16.7 AI-based Commutation Failure (CF) Identification
412(1)
16.8 Discussion
412(1)
16.9 Conclusion
413(1)
References
414(5)
Chapter 17 Intelligent Schemes for Fault Detection, Classification, and Location in HVDC Systems 419(34)
Mohammad Farshad
17.1 Introduction
420(1)
17.2 An Overview of HVDC Systems
421(12)
17.2.1 CSC-HVDC Systems
422(3)
17.2.2 VSC-HVDC Systems
425(7)
17.2.3 Requirements and Challenges
432(1)
17.3 Fault Detection and Classification in CSC-HVDC Systems
433(2)
17.3.1 Input Features
434(1)
17.3.2 Learning Algorithms/Models
435(1)
17.4 Fault Location in CSC-HVDC Systems
435(2)
17.4.1 Input Features
436(1)
17.4.2 Learning Algorithms/Models
436(1)
17.5 Fault Detection and Classification in VSC-HVDC Systems
437(2)
17.5.1 Input Features
437(1)
17.5.2 Learning Algorithms/Models
438(1)
17.6 Fault Location in VSC-HVDC Systems
439(2)
17.6.1 Input Features
440(1)
17.6.2 Learning Algorithms/Models
441(1)
17.7 Considerations for Practical Implementations
441(3)
17.7.1 Implementation Costs
442(1)
17.7.2 Unseen New Cases
442(1)
17.7.3 High-Resistance Faults
442(1)
17.7.4 Temporary Arc Faults
442(1)
17.7.5 Fault Locations Very Close to Line Terminals
442(1)
17.7.6 Operation of Adjacent Circuit Breakers
443(1)
17.7.7 Lightning Disturbances
443(1)
17.7.8 Measurement Noises/Errors
443(1)
17.7.9 Inaccurate Line Parameters
443(1)
17.7.10 Communication Delay, Disturbance, and Failure
443(1)
17.7.11 Time Synchronization Errors
444(1)
17.8 Conclusion
444(1)
References
444(9)
Chapter 18 Fault Classification and Location in MT-HVDC Systems based on Machine Learning 453(28)
Raheel Muzzammel
Ali Raza
18.1 Introduction
453(2)
18.2 Machine Learning-Based Fault Diagnostic Technique
455(2)
18.2.1 Support Vector Machines
456(1)
18.2.2 Feature Extraction and Selection
457(1)
18.3 DC Faults in MT-HVDC Systems
457(1)
18.4 Voltage Source Converters
458(1)
18.5 Control System of Voltage Source Converters
459(2)
18.6 Control of MT-HVDC System
461(1)
18.7 MT-HVDC Test System and Simulation Results
461(15)
18.7.1 DC Voltage Analysis
461(5)
18.7.2 Frequency-Based Analysis
466(1)
18.7.3 Machine Learning Algorithm
466(10)
18.8 Conclusions
476(1)
References
477(4)
Index 481
Almoataz Y. Abdelaziz (SM15) received the B.Sc. and M.Sc. degrees in electrical engineering from Ain Shams University, Cairo, Egypt, in 1985 and 1990, respectively, and the Ph.D. degree in electrical engineering according to the channel system between Ain Shams University, Egypt, and Brunel University, U.K., in 1996. He has been a Professor of electrical power engineering with Ain Shams University, since 2007. He was the Vice Dean for Education and Students Affairs in Faculty of Engineering and Technology, Future University in Egypt from 2018-2019. He has authored or coauthored more than 450 refereed journal and conference papers, 30 book chapters, and six edited books with Elsevier, Springer and CRC Press. In addition, he has supervised 80 Masters and 35 Ph.D. theses. His research areas include the applications of artificial intelligence, evolutionary and heuristic optimization techniques to power system planning, operation, and control. Dr. Abdelaziz is a senior member in IEEE and member in the Egyptian Sub-Committees of IEC and CIGRE. He has been awarded many prizes for distinct researches and for international publishing from Ain Shams University and Future University in Egypt. He is the chairman of the IEEE Education Society chapter in Egypt. He is a senior Editor of Ain Shams Engineering Journal, an editor of Electric Power Components and Systems journal, an Editorial Board member, an Editor, an Associate Editor, and an Editorial Advisory Board member for many international journals.

Dr. Shady Abdel Aleem received the B.Sc., M.Sc. and Ph.D. degrees in Electrical Power and Machines from the Faculty of Engineering, Helwan University, Egypt, in 2002, and the Faculty of Engineering, Cairo University, Egypt, in 2010 and 2013 respectively. From September 2018 to September 2019, he has been an Associate Professor at the 15th May Higher Institute of Engineering and the quality assurance unit director. Since September 2019, he is an Adjunct Associate Professor in the Arab Academy for Science, Technology & Maritime Transport, College of Engineering and Technology, Smart Village Campus for teaching power quality energy efficiency, wind energy, and energy conversion courses. Also, he is a consultant of power quality studies in ETA Electric Company, Egypt. His research interests include harmonic problems in power systems, power quality, renewable energy, smart grid, energy efficiency, optimization, green energy, and economics. Dr. Shady is the author or co-author of many refereed journals and conference papers. He has published 100 plus journal and conference papers, 18 plus book chapters, and 7 edited books with the Institution of Engineering and Technology (IET) (2), Elsevier (3), Springer (1) and InTech (1). He was awarded the State Encouragement Award in Engineering Sciences in 2017 from Egypt. He was also awarded the medal of distinction from the first class of the Egyptian State Award in 2020 from Egypt. Dr. Shady is a member of the Institute of Electrical and Electronics Engineers (IEEE). Dr. Shady is also a member of the Institution of Engineering and Technology (IET). He is an Editor/Associate Editor for the International Journal of Renewable Energy Technology, Vehicle Dynamics, IET Journal of Engineering, Technology and Economics of Smart Grids and Sustainable Energy, and International Journal of Electrical Engineering Education.

Anamika Yadav received the Bachelor of Engineering degree in Electrical Engineering from RGPV, Bhopal, in 2002, the Master of Technology degree in Integrated Power System from V.N.I.T., Nagpur, India, in 2006, and the Ph.D. degree from CSVTU, Bhilai, India, in 2010 at National Institute of Technology (NIT) Raipur as research center. She was an Assistant Engineer with the Chhattisgarh State Electricity Board, Raipur, from 2004 to 2009. She served as an Assistant Professor in the Department of Electrical Engineering, NIT Raipur, during 2009-2018. Presently she is an Associate Professor with the Department of Electrical Engineering, NIT Raipur, Raipur, since 2018. Additionally, she is also serving as Associate Dean (Research and Consultancy) at NIT Raipur since 2018. She has supervised 7 PhD thesis and 22 M.Tech thesis. Her research interests include Digital Protection & Automation, Smart Grid Technologies and Applications, Distributed Generation, Micro-grid, and Application of Artificial Intelligence, Power system protection, FACTS technologies etc. She received the Institution of Engineers India Young Engineers Award in the Electrical Engineering Division during 2015 to 2016, VIFFA young faculty award 2015, VIFRA young scientist award 2015, Chhattisgarh young scientist award in 2016 given by Chief Minister of CG. She is also a Senior Member of IEEE from 2014. She has more than 120 publications in International journals and conferences and around 1800 citations as per Google scholar.