Fault diagnosis of photovoltaic array based on deep belief network optimized by genetic algorithm

When using deep belief networks (DBN) to establish a fault diagnosis model, the objective function easily falls into a local optimum during the learning and training process due to random initialization of the DBN network bias and weights, thereby affecting the computational efficiency. To address the problem, a fault diagnosis method based on a deep belief network optimized by genetic algorithm (GA-DBN) is proposed. The method uses the restricted Boltzmann machine reconstruction error to structure the fitness function, and uses the genetic algorithm to optimize the network bias and weight, thus improving the network accuracy and convergence speed. In the experiment, the performance of the model is analyzed from the aspects of reconstruction error, classification accuracy, and time-consuming size. The results are compared with those of back propagation optimized by the genetic algorithm, support vector machines, and DBN. It shows that the proposed method improves the generalization ability of traditional DBN, and has higher recognition accuracy of photovoltaic array faults.