Power Grid Fault Location Method Based on Pretraining of Convolutional Autoencoder

Abstract

In the power grid system, the accurate determination and precise positioning of faulty equipment is an important foundation for realizing the self-healing function of the power grid. With the increasing complexity of the power grid topology, traditional fault location methods are prone to misjudge the faulty equipment and the normal equipment in the station, causing problems such as low positioning accuracy and poor stability. To locate faulty equipment quickly and accurately, this paper proposes a power grid fault location method based on pre-training of convolutional autoencoder. The method is based on the synchronous phasor measurement unit (PMU), which converts its sequence data into images to reduce noise interference. The method pre-trains lots of samples by using convolutional autoencoder, and then uses a classifier to fine-tune small batches of balanced samples. Finally, the faulty equipment in the power grid can be accurately located by dividing the area multiple times and positioning the faulty equipment gradually. Experiments and simulations show that compared with other mainstream methods, this method has higher robustness and stability, can effectively alleviate the impact of data imbalance, and improve the accuracy and accuracy of faulty equipment location.