在使用ANN和GEO技术的独立光伏系统中,故障原因及其检测
R Jai Ganesh1, S Muralidharan2
1Department of Electrical and Electronics Engineering, K.Ramakrishnan College of Technology, Trichy, Tamil Nadu, India.
ISA transactions
|July 18, 2024
概括
本研究介绍了一种使用人工神经网络 (ANN) 和金优化 (GEO) 来检测和诊断光伏 (PV) 系统故障的新方法,提高能源生产效率.
科学领域:
- 可再生能源系统可再生能源系统
- 在工程领域的人工智能.
- 检测和诊断故障的检测和诊断.
背景情况:
- 光伏 (PV) 系统已被广泛采用,用于高效的发电.
- 部分遮阳是一种常见的缺陷,可以显著降低光伏系统的性能和能源质量.
- 有效的故障检测和诊断对于保持光伏系统最佳运行至关重要.
研究的目的:
- 提出一种用于预测,检测和诊断独立光伏系统故障的新方法.
- 为了提高光伏发电的性能和可靠性.
- 为了解决光伏系统中常见的部分遮阳缺陷.
主要方法:
- 整合人工神经网络 (ANN) 用于故障分类和识别.
- 应用金优化 (GEO) 进行高效的ANN培训,减少时间和提高准确性.
- 使用 MATLAB 模拟实现和测试拟议的技术.
主要成果:
- 拟议的ANN和GEO技术有效地分类和识别光伏系统缺陷.
- GEO优化大大减少了ANN培训时间,并提高了模型的准确性.
- 对比分析显示,比基因算法 (GA),大象群群优化 (EHO) 和粒子优化 (PSO) 等现有方法的性能优越.
结论:
- 联合ANN和GEO的方法为光伏系统故障检测和诊断提供了高度准确和有效的解决方案.
- 这种方法成功地恢复了受缺陷影响的光伏系统的最佳性能.
- 该研究表明,在保持光伏发电效率和可靠性方面取得了重大进展.
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