Open Access

ARTICLE

Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

by Huanan Yu, Hangyu Li, He Wang, Shiqiang Li^*

Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, 132012, China

* Corresponding Author: Shiqiang Li. Email:

(This article belongs to the Special Issue: Key Technologies of Renewable Energy Consumption and Optimal Operation under )

Energy Engineering 2024, 121(6), 1535-1555. https://doi.org/10.32604/ee.2024.046936

Received 19 October 2023; Accepted 08 January 2024; Issue published 21 May 2024

Abstract

The escalating deployment of distributed power sources and random loads in DC distribution networks has amplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimal configuration of measurement points, this paper presents an optimal configuration scheme for fault location measurement points in DC distribution networks based on an improved particle swarm optimization algorithm. Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing. The model aims to achieve the minimum number of measurement points while attaining the best compressive sensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and network-wide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Halton sequence for population initialization, generating uniformly distributed individuals. This enhancement reduces individual search blindness and overlap probability, thereby promoting population diversity. Furthermore, an adaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the global search capability and search speed. The established model for the optimal configuration of measurement points is solved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configuration reduces the number of measurement points, enhances localization accuracy, and improves the convergence speed of the algorithm. These findings validate the effectiveness and utility of the proposed approach.

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Keywords

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