A New Energy High-Impact Process Weather Classification Method Based on Sensitivity Factor Analysis and Progressive Layered Extraction

For the electricity system with a high proportion of new energy, the extreme weather events caused by climate change will make the new energy power supply present an extremely complicated situation, thus affecting the safe and stable operation of the power system. In order to solve the above problems, this study proposes a classification method of the extreme weather process based on the Progressive Layered Extraction (PLE) model considering the weather-sensitive factors with high impact on new energy. This method analyses the sensitive factors affecting the new energy output from the two perspectives of abnormal output and abnormal prediction error, defines the high-impact weather process, and divides the standard set. According to the standard set, a high-impact weather process identification model based on PLE is constructed to provide more accurate early warning information. The proposed method is applied to a new energy cluster in Jiangxi Province, China. Compared with the traditional classification task model, the accuracy of the proposed method is increased by 1.30%, which verifies the effectiveness of the proposed method.