Evolution of radial basic function neural network for fast restoration of distribution systems with load variations
This paper proposes a new algorithm to construct the optimal radial basic function (RBF) neural network for fast restoration of distribution systems with load variations. Service restoration of distribution systems is to restore power to the blacked out but unfaulted area. Basically, it is a stressful and urgent task that must be performed by system operators. In this paper, a new algorithm which combines orthogonal least-squares (OLS) and enhanced differential evolution (EDE) methods is developed to construct the optimal RBF network that shall further 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 OLS and EDE algorithms 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 was tested on a typical distribution system of the Taiwan Power Company (TPC). Results show that the proposed method outperforms the existing methods in terms of convergence performance and forecasting accuracy.
radial basic function service restoration orthogonal least-squares enhanced differential evolution distribution engineering