A Transformer District Line Loss Calculation Method Based on Data Mining and Machine Learning

Abstract

Transformer district (TD) line loss is an important economic and technical index for the economic operation of power system. The traditional TD line loss management adopts the uniform fixed interval as a reasonable interval, which is not conducive to lean management. This paper presents an accurate quantization method of TD line loss based on data mining and machine learning. Firstly, the proposed method forms the electrical characteristic index system of TD. On this basis, the proposed method combined the improved K-means method with clustering effect comprehensive evaluation index to obtain the optimal classification results of massive TDs. Then, according to the objective weight extraction method, the key impact factor set of the TD line loss are obtained, so as to obtain the feature images of each TD. Finally, the accurate quantification of TD line loss is realized by the proposed TD line loss calculation model based on the BP neural network and kernel density estimation. The effectiveness of the proposed method are verified by simulations based on the real TD data.