Quantum-Inspired Evolutionary Programming-Artificial Neural Network for Prediction of Undervoltage Load Shedding

This paper presents new intelligent-based technique namely Quantum-Inspired Evolutionary Programming-Artificial Neural Network (QIEP-ANN) to predict the amount of load to be shed in a distribution systems during undervoltage load shedding. The proposed technique is applied to two hidden layers feedforward neural network with back propagation. The inputs to the ANN are the load buses and the minimum voltage while the outputs are the amount of load shedding. ANN is trained to perform a particular function by adjusting the values of the connections (weights) between elements, so that a particular input leads to a specific target output. The network is trained based on a comparison of the output and the target, until the network output matches the target. The parameters of ANN are optimally selected using Quantum-Inspired Evolutionary Programming (QIEP) optimization technique for accurate prediction. The QIEP-ANN is developed to search for the optimal training parameters such as number of neurons in hidden layers, the learning rate and the momentum rate. This method has been tested on IEEE 69-bus distribution test systems. The results show better prediction performance in terms of mean square error (MSE) and coefficients of determination (R-2) as compared to classical ANN.