Evolving radial basic function neural network for fast restoration of distribution systems

Abstract

This paper presents an optimal radial basic function (RBF) neural network for fast restoration of distribution systems under different load levels. Basically, service restoration of distribution systems is a stressful and urgent task that must be performed by system operators. In this paper, a RBF network evolved by an enhanced differential evolution (EDE) algorithm is developed to achieve the fast restoration of distribution systems. The proposed scheme comprises training data creation phase and network construction phase. In the training data creation phase, a heuristic-based fuzzy inference (HBFI) method is employed to build the restoration plans under various load levels. Then an optimal RBF network is constructed by the EDE algorithm in the network construction phase. Once the RBF network is constructed properly, the desired restoration plan can be produced as soon as the inputs are given. The proposed method has been verifiedd on a typical distribution system of the Taiwan Power Company (TPC). Results show the proposed method can provide better convergence performance and forecasting accuracy than the existing methods.