DC arc faults detection in photovoltaic arrays based on EEMD-SVM
With the growth of the operation time of the photovoltaic array,a large number of connecting cables and connectors in the array are prone to damage and connection failure,which causes DC arc faults and seriously endangers the safe operation of the photovoltaic system.It is necessary to adopt appropriate detection methods for fault diagnosis in order to find arc faults as soon as possible.The detection methods of DC arc faults can be roughly divided into two categories,based on physical characteristics and time-frequency characteristics.The former is costly,difficult,and not suitable for large-scale photovoltaic systems.With the rise of artificial intelligence in recent years,most of the latter is to extract the time-frequency domain eigenvalues of DC arc faults to form a dataset,and use neural networks or intelligent algorithms to identify,train,and generalize them so as to achieve the purpose of detection,which is focus on practical applications at present.The ensemble empirical mode decomposition and support vector machine combination method based on the characteristics of the time-frequency domain are selected for detection,and the photovoltaic array model and DC arc fault simulation model are built on the MATLAB/Simulink simulation platform to simulate the series and parallel arc faults at different positions of the photovoltaic array,and the current signals are collected,analyzed and processed.Experimental results show that the SVM model can better identify and detect the DC arc faults of photovoltaic arrays,and effectively distinguish the normal working state and fault working state of photovoltaic arrays.
DCarcfault detectiontime-frequency domain characteristicsEnsemble Empirical Mode Decomposition(EEMD)Support Vector Machine(SVM)simulation model
NETL
NSTL
万方数据
