Adaptive wavelet neural network-based fast dynamic available transfer capability determination

Abstract

An adaptive wavelet neural network (AWNN)-based method has been proposed to determine dynamic available transfer capability (DATC) in the electricity markets, having bilateral as well as multilateral contracts. Mexican hat wavelet basis function has been used as the activation function in the hidden layer of the network. Wavelet parameters, that is, translations and dilations of the AWNN, have been initialised using Euclidean distance-based clustering method. The AWNN has been trained using back propagation gradient descent training algorithm. The relevant features to be used, as input to the AWNN, are identified using a random forest technique. To demonstrate the effectiveness of the proposed AWNN-based method for the DATC determination, it has been tested on 39-bus New England system and a 246-bus Indian system and its results have been compared to the radial basis function neural network (RBFNN).