基于数字孪生技术和随机森林算法的光伏组件故障诊断方法研究OA

With the continuous expansion of the installed capacity of PV power generation,fault diagnosis of PV modules has become a key issue in improving the operation and maintenance efficiency of PV power stations.Due to the complex terrain and harsh environment of some PV power stations,traditional manual inspection methods are difficult to meet the real-time and accurate needs of PV module fault diagnosis.Based on this,this paper proposes a PV module fault diagnosis method based on digital twin technology and random forest algorithm.Firstly,a 5D model of the PV power station digital twin system is constructed,which includes a physical entity layer,a virtual model layer,a data interaction layer,a service application layer,and a twin data layer.Then,based on time coefficient optimization of operating parameters of PV modules and simulation data fusion analysis,combined with random forest algorithm,fault diagnosis and classification of PV modules are achieved.Finally,taking a certain PV power station as an example,the proposed fault diagnosis method for PV modules is experimentally verified.The research results show that the constructed digital twin system for PV power stations has successfully achieved real-time interaction between physical entities and virtual models,accurately reflecting the operational status of the PV power station.The fault classification accuracy of the PV module fault diagnosis method proposed for the five operating states of normal,open circuit,short circuit,shadow obstruction,and hot spot in PV modules is good,which can significantly improve the operation and maintenance efficiency of PV power stations.However,there is still a certain misjudgment rate in the classification results,and the accuracy of PV module fault classification varies under different weather conditions.The research results can provide reliable technical support for the intelligent operation and maintenance of PV power stations.