基于调制光电流和机器学习的光伏阵列故障诊断和定位方法
Yebo Tao1, Tingting Yu2, Jiayi Yang3,4
1College of Intelligent Manufacturing, Jiaxing Vocational & Technical College, Jiaxing 314036, China.
本研究引入了一种新的方法来诊断光伏 (PV) 阵列故障,使用调制光电流和机器学习. 它通过低成本设备实现高速,准确的故障识别和定位.
科学领域:
- 可再生能源系统可再生能源系统
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
背景情况:
- 光伏 (PV) 阵列因户外暴露而随着时间的推移而降解,导致各种故障.
- 有效的故障诊断系统对于光伏阵列的可靠性和性能至关重要.
- 当前的方法往往会在诊断准确性和故障定位能力之间妥协.
研究的目的:
- 为光伏阵列开发故障识别和本地化方法.
- 通过实现高精度和精确的定位来克服现有方法的局限性.
- 为了实现快速和成本效益的光伏阵列故障检测.
主要方法:
- 使用调制光电流和机器学习进行故障诊断.
- 使用调频光来分离光电流并测量单个面板的效率.
- 应用机器学习分类算法来分析电流幅度和频率以识别故障.
主要成果:
- 使用神经网络算法实现了高速 (5800次/秒) 和高精度 (97.8%) 的故障识别和定位.
- 通过实践实验证明了该方法的有效性.
- 证实了通过仅测量短路电流来识别故障的能力.
结论:
- 拟议的模块化光电流和机器学习方法为光伏阵列故障诊断提供了卓越的解决方案.
- 这种方法为故障识别和定位提供了一个实用,低成本,高速和高度准确的系统.
- 这些发现支持这种技术广泛采用,以提高光伏系统的可靠性.
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