Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Electronics Letters

DOI: 10.1049/ell2.70313

Authors: Sicheng Huang, Lijia Ren, Dongbing Tong

Institution: Shanghai University of Engineering Science

Language: English

Topic: Smart Grid and Power Systems

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,590