Performance comparison of neural networks for intelligent management of distributed generators in a distribution system

Abstract

Abstract The Multilayer Perceptron (MLP) neural network has been proven to be a very successful type of neural network in many applications. The MLP activation function is one of the important elements to be considered in neural network training in which proper selection of the activation function will give a huge impact on the network performance. This paper presents a comparative study of the four most commonly used activation functions in the neural network which include the sigmoid, hyperbolic tangent and linear functions used in the MLP neural network and the Gaussian function used in the Radial Basis Function (RBF) network for managing active and reactive power of distributed generation (DG) units in distribution systems. Simulation results show that the sigmoid activation functions give better performance in predicting the optimal power reference of the DG units. However, the RBF neural network gives the fastest conversion time compared to the MLP neural network.