Research on electrical power quality disturbance recognition method based on edge computing and LightGBM

Abstract

In conventional cloud computing, method that data is transmitted and calculated on the cloud cannot satisfy the real-time demand for energy quality disturbance recognition. This paper proposed a power quality disturbance recognition method based on edge computing and LightGBM classification algorithm. Our main idea is that the feature of disturbance is extracted on the edge sides and used to classified on the cloud. Firstly, a multi-group feature set was extracted at the edge side intelligent fusion terminal by wavelet transform. Secondly, we used feature training accuracy to select the optimal feature collection. Finally, the optimal feature set was selected to determine the disturbance recognition method of this paper. Experiments had shown that the proposed method meets demand on data transmission by 99.5%, and achieves 97.53% recognition accuracy. Our method not only guarantees high accuracy of the power quality disturbance recognition but also alleviates the bandwidth load pressure brought by large amounts of data transmission.

Keywords

• edge computing
• wavelet transform
• lightgbm
• power quality disturbance recognition