基于卷积神经网络的光伏逆变器故障诊断  

Fault Diagnosis of Photovoltaic Inverter Based on Convolution Neural Network

作　　者：陈旺斌 孟献蒙 程凡永 徐杰[1,2] CHEN Wangbin;MENG Xianmeng;CHENG Fanyong;XU Jie(Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment of Ministry of Education,Anhui Polytechnic University,Wuhu Anhui 241000,China;Anhui Key Laboratory of Electric Drive and Control,Anhui Polytechnic University,Wuhu Anhui 241000,China)

机构地区：[1]安徽工程大学高端装备先进感知与智能控制教育部重点实验室,安徽芜湖241000 [2]安徽工程大学电气传动与控制安徽省重点实验室,安徽芜湖241000

出　　处：《湖南工业大学学报》2021年第1期25-30,共6页Journal of Hunan University of Technology

基　　金：安徽省教育厅基金资助重点项目(KJ2019A0149);福建省自然科学基金资助面上项目(2018J01806);福建省教育厅基金资助项目(JAT170457)。

摘　　要：光伏逆变器将太阳能电池板的直流电压转换为交流电压以驱动家用电器或者升压并入能源互联网,而绝缘栅双极型晶体管(IGBT)是光伏逆变器的核心组件,其状态异常将直接影响系统的正常运行。从减少传感器数量的角度出发,以直流侧的电流信号作为输入信号,设计了基于卷积神经网络(CNN)的故障诊断模型来监测IGBT的开路状态。并利用设计的Simulink模块生成的仿真数据对模型进行了训练和测试,都取得了很好的故障诊断性能。此外,还设计了不同噪声水平下的故障诊断测试,测试结果表明该故障诊断模型在噪声环境下具备有效性和鲁棒性。Photovoltaic inverters are characterized with the ability to convert DC voltage of solar panels into AC voltage to drive household appliances or boost voltage into the energy internet.As the core component of photovoltaic inverter,insulated gate bipolar transistor(IGBT)under an abnormal state will directly affect the normal operation of the system.In order to reduce the number of sensors,a fault diagnosis model based on convolutional neural network(CNN)is designed to monitor the open circuit state of IGBT with the current signal of DC side as the input signal.The simulation data generated by the designed Simulink module is used to train and test the model,thus achieving a good fault diagnosis performance.In addition,experimental tests under different level noises help to validate the effectiveness and robustness of the proposed method in noisy environments.

关 键 词：三相逆变器 能源互联网 卷积神经网络 故障诊断 

分 类 号：TM769[电气工程—电力系统及自动化]