Characteristic Portrait Construction of Voltage in Substation Areas Using ISSA‐Based Cluster Prediction

ABSTRACT To effectively utilize power data for monitoring and issuing warnings in distribution‐substation areas, a clustering prediction model was developed by integrating an improved salp swarm optimization algorithm. First, power characteristics related to the target were collected using original data mining and dimensionality reduction techniques. A voltage‐characteristic label model for the substation areas was then developed, focusing on three dimensions: safety (S), stability (S), and economy (E). Next, because the improved salp swarm algorithm (ISSA) has better optimization effect and less iteration time, the fusion ISSA was combined with the K‐means clustering algorithm to analyse the data, while the ISSA‐BP neural network algorithm was used for time‐series prediction. This approach generated a voltage profile and detected abnormal conditions in the substation areas. Finally, the model was validated using real data from a substation area in Shanghai. The results demonstrated that the proposed algorithm was more efficient, with improved prediction accuracy and performance metrics.