- Introduction to evolutionary programming and neural networks serving as a foundation for later discussion of the benefits of hybrid systems
- Practical application of evolutionary programming to reactive power planning and dispatch for speedy, cost–effective increases in transmission capacity plus generator parameter estimation
- Examination of economic dispatch, power flow control in FACTS and co–generation scheduling and fault diagnosis for HVDC systems and transformers
- Consideration of power frequency and harmonic evaluation to maximise supply quality
- Employment of distance protection, faulty section estimation and calculation of fault clearing time for transient stability assessment
Hybrid Evolutionary Algorithms and Artificial Neural Networks
An Evolutionary Programming Approach to Reactive Power Planning
Optimal Reactive Power Dispatch Using Evolutionary Programming.
Application of Evolutionary Programming to Transmission Network Planning.
Application of Evolutionary Programming to Generator Parameter Estimation.
Evolutionary Programming for Economic Dispatch of Units with Non–Smooth Input–Output Characteristic Functions.
Power Flow Control in Facts Using Evolutionary Programming.
Multi–Time–Interval Scheduling for Daily Operation of a Two–Co–Generation System with Evolutionary Programming.
Application of Evolutionary Programming to Fault Section Estimation.
Neural Networks for Fault Diagnosis in HVDC Systems.
An Ann Approach to the Diagnosis of Transformer Faults
Real–Time Frequency and Harmonic Evaluation Using Artificial Neural Networks.
Artificial Neural Network Applications in Digital Distance Relay.
Application of Artificial Neural Networks to Transient Stability Assessment.
Application of Neural Networks and Evolutionary Programming to Short–Term Load Forecasting.