| Description |
1 online resource (430 p.) |
| Contents |
Front cover -- Half title -- Full title -- Copyright -- Contents -- About the authors -- Acknowledgment -- 1 -- Introduction -- 1.1 Introduction -- 1.2 Electric power system fault diagnosis importance -- 1.3 Electric power system fault diagnosis techniques -- 1.3.1 Fault detection and classification techniques -- 1.3.2 Fault location techniques -- 1.3.2.1 Impedance-based techniques -- 1.3.2.2 Traveling wave-based techniques -- 1.3.2.3 Knowledge-based techniques -- 1.3.2.4 High frequency-based techniques -- 1.3.2.5 Other techniques |
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1.3.3 Factors affecting the accuracy of fault diagnosis techniques -- 1.4 Wide area measurement system and phasor measurement units -- 1.4.1 Historical overview -- 1.4.2 Phasor definition -- 1.4.3 Phasor measurement concept -- 1.4.4 Synchrophasor and the generic phasor measurement unit -- 1.4.5 Phasor measurement systems -- 1.4.6 Phasor measurement systems application -- 1.5 Book organization -- 1.6 Summary -- References -- 2 -- Metaheuristic optimization techniques -- 2.1 Introduction -- 2.2 Classical optimization techniques -- 2.2.1 Linear programming |
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2.2.2 Mixed-integer linear programming -- 2.2.3 Nonlinear programming -- 2.3 Metaheuristic techniques -- 2.3.1 Trajectory-based metaheuristic techniques -- 2.3.1.1 Simulated annealing -- 2.3.1.2 Tabu search algorithm -- 2.3.2 Evolutionary metaheuristic techniques -- 2.3.2.1 Genetic algorithm -- 2.3.2.2 Differential evolution -- 2.3.2.3 Backtracking search algorithm -- 2.3.3 Swarm-based metaheuristic techniques -- 2.3.3.1 Particle swarm optimization -- 2.3.3.2 Constriction factor particle swarm optimization -- 2.3.3.3 Grey wolf optimization -- 2.3.4 Other metaheuristic techniques |
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2.4 Summary -- References -- 3 -- Artificial intelligence techniques -- 3.1 Introduction -- 3.1.1 Brief history of artificial intelligence -- 3.1.2 Artificial intelligence is the new electricity -- 3.1.3 Artificial intelligence foundation and basic components -- 3.1.4 Limitations of artificial intelligence -- 3.2 Artificial intelligence techniques -- 3.2.1 Artificial neural networks -- 3.2.2 Support vector machines -- 3.2.3 Extreme learning machines -- 3.2.4 Fuzzy logic model -- 3.2.5 Genetic programming -- 3.2.6 Deep learning technique |
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3.3 Hybrid, ensemble, and other artificial intelligence techniques -- 3.3.1 Adaptive neuro-fuzzy inference system -- 3.3.2 Ensemble techniques -- 3.3.3 Metaheuristic algorithm inspired artificial intelligence techniques -- 3.3.4 Other artificial intelligence techniques -- 3.4 Summary -- References -- 4 -- Advanced signal processing techniques for feature extraction -- 4.1 Introduction -- 4.2 Signal processing techniques -- 4.3 Wavelet transform -- 4.3.1 Continuous wavelet transform -- 4.3.1.1 Discrete wavelet transform -- 4.3.2 Stockwell transform |
| Notes |
Description based upon print version of record |
|
4.4 Feature extraction illustration |
| Form |
Electronic book
|
| Author |
Abido, M. A
|
|
Al-Mohammed, A. H
|
| ISBN |
9780323884303 |
|
032388430X |
|
