Computational Intelligence in Power Engineering

1. Front Matter

   PDF

2. Robust Design of Power System Stabilizers for Multimachine Power Systems Using Differential Evolution

   • M. A. Abido

   Pages 1-18

3. An AIS-ACO Hybrid Approach for Multi-Objective Distribution System Reconfiguration

   • A. Ahuja, S. Das, A. Pahwa

   Pages 19-73

4. Intelligent Techniques for Transmission Line Fault Classification

   • A. K. Pradhan

   Pages 75-101

5. Fuzzy Reliability Evaluations in Electric Power Systems

   • Dusmanta Kumar Mohanta

   Pages 103-130

6. Load Forecasting and Neural Networks: A Prediction Interval-Based Perspective

   • Abbas Khosravi, Saeid Nahavandi, Doug Creighton

   Pages 131-150

7. Neural Network Ensemble for 24-Hour Load Pattern Prediction in Power System

   • Krzysztof Siwek, Stanislaw Osowski

   Pages 151-169

8. Power System Protection Using Machine Learning Technique

   • S. R. Samantaray, P. K. Dash, G. Panda

   Pages 171-198

9. Power Quality

   • Kyeon Hur, Surya Santoso

   Pages 199-234

10. Particle Swarm Optimization PSO: A New Search Tool in Power System and Electro Technology

    • Adel M. Sharaf, Adel A. A. El-Gammal

    Pages 235-294

11. Particle Swarm Optimization and Its Applications in Power Systems

    • M. R. AlRashidi, M. F. AlHajri, A. K. Al-Othman, K. M. El-Naggar

    Pages 295-324

12. Application of Evolutionary Optimization Techniques for PSS Tuning

    • S. P. Ghoshal, A. Chatterjee, V. Mukherjee

    Pages 325-366

13. A Metaheuristic Approach for Transmission System Expansion Planning

    • Ashu Verma, P. R. Bijwe, B. K. Panigrahi

    Pages 367-379

14. Back Matter

    PDF

Computational Intelligence (CI) is one of the most important powerful tools for research in the diverse fields of engineering sciences ranging from traditional fields of civil, mechanical engineering to vast sections of electrical, electronics and computer engineering and above all the biological and pharmaceutical sciences. The existing field has its origin in the functioning of the human brain in processing information, recognizing pattern, learning from observations and experiments, storing and retrieving information from memory, etc. In particular, the power industry being on the verge of epoch changing due to deregulation, the power engineers require Computational intelligence tools for proper planning, operation and control of the power system. Most of the CI tools are suitably formulated as some sort of optimization or decision making problems. These CI techniques provide the power utilities with innovative solutions for efficient analysis, optimal operation and control and intelligent decision making. This edited volume deals with different CI techniques for solving real world Power Industry problems. The technical contents will be extremely helpful for the researchers as well as the practicing engineers in the power industry.

• artificial intelligence
• computational intelligence
• control
• electronics
• evolution
• fuzzy
• intelligence
• learning
• linear optimization
• machine learning
• mechanical engineering
• neural network
• neural networks
• optimization
• power systems

• Indian Institute of Technology, New Delhi, India

  Bijaya Ketan Panigrahi

• Jadavpur University, Calcutta, India

  Ajith Abraham

• Norwegian University of Science and Technology, Trondheim, Norway

  Swagatam Das

Policies and ethics