双图注意网络用于光伏逆变器的可靠故障诊断
Ananta Bijoy Bhadra1, Most Humayra Khanom Rime2, Yeahia Sarker3
1Department of Electrical and Computer Engineering, Georgia Southern University, Statesboro, GA, 8151, USA.
这项研究引入了双图注意网络 (DualGAT),用于准确的光伏逆变器故障诊断. 这种新型深度学习模型即使在噪音条件下也能达到97.35%的准确性,确保可靠的能量转换.
科学领域:
- 电气工程
- 人工智能
- 可再生能源系统
背景情况:
- 光伏逆变器对于稳定的能源转换至关重要.
- 早期检测光伏逆变器的开放电路故障对于防止性能下降和设备故障至关重要.
研究的目的:
- 开发一个强大的深度学习框架来准确诊断光伏逆变器的故障.
- 通过智能状态监控提高光伏系统的可靠性.
主要方法:
- 一个双图注意力网络 (DualGAT) 框架被提出,整合了空间 (DisGAT) 和时间 (TempGAT) 的注意力机制.
- 在MATLAB/Simulink中创建了一个连接到电网的光伏逆变器的详细模拟模型.
- 使用离散波形转换 (DWT) 来从逆变器电流信号中提取特征.
主要成果:
- DualGAT模型在故障诊断中达到97.35%的测试准确度.
- 与传统的机器学习和其他深度学习方法相比,该框架表现出明显的优势.
- 在噪音和信号扭曲条件下保持高诊断性能.
结论:
- 双GAT框架有效地捕捉了光伏逆变器的复杂时空故障特征.
- 这种方法为基于光伏的电力系统的智能状态监测提供了有前途的解决方案.
- 该模型的强度确保了光伏基础设施的可靠运行和维护.
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