Big data analysis based identification method of low- voltage substation area

Aiming at the problems of low efficiency, low reliability and difficulty in cross substation identification of traditional identification methods, this paper proposes a low- voltage substation identification method based on big data analysis. Firstly, selecting the daily, weekly, monthly or annual measurement data from the big data database of low-voltage substation area to construct the trend curve of user voltage change. The improved grey correlation analysis is used to analyze the correlation degree of line voltage between the unrecognized or cross substation area meter and the meter with clear substation area ownership. The connection relationship between the transformer in substation area and the unrecognized meter is automatically analyzed, which can realize intelligent substation area identification. Finally, the identification method is used to identity the household transformer relationship of a low-voltage distribution network in Wuhan. The test results show that the results are consistent with the real household transformer relationship, and the identification accuracy is relatively high, which has a certain engineering application value.