Massive Data Processing Method for Battery Energy Storage Power Stations Based on Rough Sets - Juan CHEN; Dong HUI; Maosong FAN; Juan HU; Yongjin CHU (Zhongguo dianli)
During the operation of energy storage power stations with lithium-ion batteries, huge amounts of uploaded data and high-frequency data sampling increase the difficulty of online real-time evaluation. How to extract and compress effective data and ensure the fidelity of the compressed data has become the main content of preprocessing in data mining. Given the above problems, the attributes of the data from a battery cluster are first reduced by the method using rough set theory. The battery cluster is selected from the energy storage power station under a specific working condition. Then, according to the \begin{document}$ 2σ $\end{document} principle of normal distribution, the attribute values are divided on the basis of binary logic, and the battery cells are classified into frequent detection objects and infrequent detection objects, which reduces the amount of data processed online. Finally, the data of the same battery cluster under different working conditions in 20 days are taken to verify the effectiveness of the processing method.