基于连续PSO-GA算法的发电系统故障定位
Wenzhang Xu1, Jiachun Li2, Lv Yang3
1School of Mechanical Engineering, Guizhou University, Guiyang, 550000, Guizhou, China.
Scientific reports
|May 16, 2024
概括
一个新的连续粒子群遗传算法 (SPSO-GA) 准确地定位了分布式发电复杂的发电网络中的故障. 与现有方法相比,这种先进的算法提供了更好的准确性,速度和容错性.
科学领域:
- 电气工程 电气工程
- 计算智能是一种计算智能.
- 电力系统 电力系统
背景情况:
- 配电网络容易发生故障,由于分布式发电集成,复杂性增加.
- 传统的故障定位算法与现代配送网络的多源性质作斗争.
- 准确的故障定位对于电网的可靠性和稳定性至关重要.
研究的目的:
- 开发和评估一个新的故障定位算法,用于分布式发电的分布网络.
- 在复杂的电力系统场景中解决现有离散算法的局限性.
- 为了提高故障定位技术的准确性,速度和容错性.
主要方法:
- 开发一个连续的粒子群遗传算法 (SPSO-GA),利用连续的实数代.
- 对SPSO-GA与离散粒子群遗传算法和人工鱼群算法进行比较分析.
- 在包含分布式电源的IEEE33节点分布网络上进行测试和验证.
主要成果:
- 在单个,双重和三重故障场景中,SPSO-GA表现出高的优化准确性和稳定性.
- 该算法表现出快速的融合速度,并且需要更少的计算资源 (粒子).
- 即使存在扭曲的网络信息,也可以实现准确的故障段定位.
结论:
- 在现代复杂的配送网络中,SPSO-GA为故障定位提供了卓越的方法.
- 算法的连续代方法克服了传统离散方法的局限性.
- SPSO-GA为提高发电网的可靠性提供了强大而高效的解决方案.
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