This chapter is aimed at developing a unified neural network based framework that can be utilized in prediction and control of complex dynamic system behaviors. In particular, in power systems, accurate prediction of system load behavior provides vital information to allow for optimal planning and most economic operation of power systems; on the other hand, the real-time system stability must be maintained against various random factors, disturbances and contingencies. The hierarchical neural networks are studied in depth in the context of prediction, optimization and control; and unified design techniques are developed for providing control robustness, optimality and prediction accuracy as well. The unified methodology builds upon hierarchical neural networks, and may be utilized and extended for other practical applications.
Optimization and Control of Electric Power Systems
