Two-Stage Classification of Typical Joint Scenarios in Power Systems Based on Improved Iterative Selforganizing Data Analysis Technique Algorithm and Feature Hierarchical Clustering

With the increasing proportion of renewable energy in the power system, the load, photovoltaic and wind power characteristics show complex dynamic changes, and the traditional single-element scenario classification method is difficult to cope with this challenge. In order to solve this problem, this paper proposes a two-stage typical joint scene classification method for power systems based on improved iterative selforganizing data analysis technique algorithm (ISODATA) and elemental hierarchical clustering. In the first stage, for load, photovoltaic (PV) and wind power time-series data, the improved ISODATA algorithm is used to reconstruct the time-series data by variance and extreme variance thresholds to consider local differences, and combined with the composite Euclidean dynamic time warping (DTW) metrics to measure the overall differences, which optimizes the classification accuracy of the single-element time-series. In the second stage, on the basis of the elemental typical scenes obtained in the first stage, the interrelationships and dynamic change characteristics of load, PV and wind power are analyzed, the relevant feature quantities are set, and the elements are jointly analyzed by using the hierarchical clustering method, and a typical joint scene of multiple elements is finally obtained. The joint scene not only integrates the characteristics of multiple elements, but also is more complex and comprehensive than the single-element scene. The experimental results show that the method proposed in this paper can effectively improve the accuracy of typical scene classification, accurately capture the complex change characteristics of various elements in the power system, and provide strong data support for intelligent scheduling and optimization.