Application of Deep Learning Algorithm in Generator Fault Prediction

Recent rapid development of information and communication technology boosts the advance of distributed management and control system, especially for power system. Massive data and information have been accumulated, however, the meaningful fault information hidden in these data is not fully utilized, as the existing fault detection technologies are usually based on monitoring and diagnosis rather than prediction. In this paper, we introduce the deep learning algorithm into the fault prediction of generators in power system, and explore the validity and feasibility of generator operation data in fault prediction application. Our method includes two parts, the first is a Partial Least Square (PLS)-based pre-process module which is used to reduce the feature dimension, the second is a deep linear regression model which is dedicated to regressing the generator operation data and predicting the fault behavior of generators. Experimental results demonstrate the effectiveness of our method.