Adaptive Evolutionary Programming with Neural Network for Transient Stability Constrained Optimal Power Flow

An adaptive evolutionary programming (AEP) with a neural network is presented to solve transient stability constrained optimal power flow (TSCOPF). The AEP adjusts its population size automatically during an optimization process to obtain the TSCOPF solution. The artificial neural network is embedded into AEP to reduce the computational load caused by transient stability constraints. The fuel cost minimization is selected as the objective function of TSCOPF. The proposed method is tested on the IEEE 30-bus system with two types of the fuel cost functions, i.e. the conventional quadratic function and the quadratic function superimposed by sine component to model the cost curve without and with valve-point loading effects respectively. The numerical examples show that AEP is more effective than conventional EP in searching the global solution, and when the neural network is incorporated into AEP, it can significantly enhance the computational speed. A study of the architecture of the neural network is also conducted and discussed.