Research on Power System Load Clustering based on Signal Analysis and K-Prototypes

Based on the K-prototypes algorithm, this study discusses the method of load clustering in power system. After the data is acquired, the outliers are processed, and then the data is filtered and analyzed using the Butterworth higher-order filter. Then, the feature engineering project is carried out to extract the representative features in the time domain and frequency domain, and a mixed-attribute clustering model is constructed, which organically combines numerical attributes and categorical attributes. This model is designed to fully exploit the latent information in the load data and more accurately characterize the load. Through this model, we can realize the fine division of load data and provide more detailed and effective information for power system management, which provides new ideas and methods for power system load management, and has certain theoretical and practical value.