Variational Autoencoder Based Fault Detection and Location Method for Power Distribution Network

With the rapid development of the world economy and the continuous improvement of people's living standards, users put forward higher requirements for the quality and reliability of the power system. As a representative of deep learning, variational autoencoders play an important role in processing high-dimensional big data. In this paper, a novel variational autoencoder based distribution network abnormal monitoring and positioning method is proposed. Through an indepth study of the variational autoencoder, we utilize the real-time dynamic distribution network status information provided by the wide-area measurement system, combined with the abilities of feature extracting and data reconstruction. Finally, the IEEE-33 simulation model was built in Matlab software and the experimental results were used to verify the correctness of this method. The results show that this method can quickly and accurately achieve the fault detection and location for the distribution network with strong robustness.