Fault diagnosis of PV array based on optimised BP neural network by improved adaptive genetic algorithm

Abstract

The fault diagnosis model based on back propagation (BP) neural network is especially suitable for multi-fault and complex pattern recognition. However, the selection of initial weights and thresholds of BP neural network is lack of basis and it is easy to fall into the local optimum. Genetic algorithm (GA) can be used to optimise the initial weights and thresholds of the BP neural network. However, the selection process of GA based on roulette wheel selection is a random operation and the parameters of GA, the crossover probability and the mutation probability, are given a constant value which reduces the efficiency. Thus improved adaptive GA provides improvement in the selection, crossing and mutation process of GA, which prevents the neural network from falling into local optimum more effectively. Results of cases study show that this method can determine the fault types of photovoltaic array effectively.