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ResGRU:一种新的混合深度学习模型,用于在光伏阵列中复合故障诊断,考虑到尘埃影响

Xi Liu¹, Hui Hwang Goh², Haonan Xie¹

¹School of Electrical Engineering, Guangxi University, Nanning 530004, China.

Sensors (Basel, Switzerland)

|February 26, 2025

概括

本研究介绍了ResGRU,这是一种混合深度学习模型,用于准确的光伏 (PV) 阵列故障诊断. 它有效地识别单个和复合故障,即使有尘埃积累,提高光伏系统的效率.

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.
材料科学材料科学材料科学

背景情况:

光伏发电站需要及时识别缺陷,以获得最佳的性能和寿命.
尘埃积累和环境因素会导致光伏阵列中复杂的复合故障,导致错误分类.
现有的诊断方法难以区分类似的单个和复合故障.

研究的目的:

开发一种新的混合深度学习模型,用于准确的光伏阵列故障诊断.
为了提高模型在不同尘埃积累水平下识别故障的能力.
提高故障诊断模型的辨别能力.

主要方法:

一个混合深度学习模型,ResGRU,集成剩余网络 (ResNet) 和双向门式循环单元 (BiGRU).
整合了一个Squeeze-and-Excitation (SE) 模块,以改进特征提取.
利用中心损失函数来改善类间的分离性和类内部的紧性.

主要成果:

ResGRU模型在原始数据上达到99.94%的准确性,在噪音数据上达到98.21%的准确性.
与人工神经网络 (ANN),随机森林 (RF) 和各种基于ResNet的方法相比,表现出优异的性能.
在尘埃积累下成功诊断了个别故障,部分阴影和复合故障.

结论:

拟议的ResGRU模型为光伏阵列故障诊断提供了可靠和准确的解决方案.
从I-V曲线中自动提取特征,可以精确识别各种故障类型.
该模型有助于优化光伏发电厂的清洁时间表.

关键词:

这是一个IV-V曲线.双向封闭循环单元 (BiGRU) 是一个双向封闭循环单元.尘埃冲击冲击的影响.错误诊断错误诊断错误诊断是一个问题.太阳能光伏 (PV) 系统剩余网络 (ResNet) 的使用.

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