Method for diagnosing and classifying faults of photovoltaic power generation arrays on basis of particle swarm optimization support vector machines

Abstract

The invention relates to a method for diagnosing and classifying faults of photovoltaic power generation arrays on the basis of particle swarm optimization support vector machines. The method particularly includes steps of S1, acquiring a plurality of electric parameters of the photovoltaic power generation arrays to obtain electric parameter sample combinations when the photovoltaic power generation arrays work at the maximum power points; S2, normalizing each electric parameter sample; S3, acquiring test sample combinations according to normalized electric parameter sample combinations; S4, computing the optimal SVM (support vector machine) kernel function parameters g and penalty parameters c by the aid of PSO (particle swarm optimization) algorithms; S5, training the samples according to the optimal kernel function parameters g and the penalty parameters c to obtain training models; S6, detecting and classifying the faults of the photovoltaic power generation arrays by the aid of the training models. The method has the advantage that the photovoltaic power generation array fault detection and classification accuracy can be effectively improved by the aid of the method.

Description

technical field [0001] The invention relates to the technical field of photovoltaic power generation array fault detection and classification, in particular to a photovoltaic power generation array fault diagnosis and classification method based on particle swarm optimization support vector machine. Background technique [0002] Photovoltaic power generation arrays usually work in complex outdoor environments, and are affected by various environmental factors, and are prone to various failures such as open circuit, short circuit, hard shadow, hot spot, etc. The occurrence of faults will reduce the power generation efficiency of the power station, and even cause fires in severe cases, endangering the safety of social property. Therefore, if the faults of the photovoltaic power generation array in the running state can be diagnosed, classified and further alarmed in time, the energy loss caused by the abnormal operation of the photovoltaic system can be reduced, the possibilit...

AI Technical Summary

Problems solved by technology

[0004] However, these solutions have some shortcomings: the infrared image detection method cannot distinguish the state where the temperature difference is not obvious, the accuracy and efficiency of fault detection depend on the level of the detection equipment (infrared thermal imaging camera), the cost is large, and the real-time performance is poor; Based on the time domain reflection analysis method, the photovoltaic array in operation cannot be operated online, it is not real-time, and it has high requirements for the equipment, and the diagnostic accuracy is limited; the multi-sensor fault detection method has many sensors and the detection structure is large. Disadvantages such as difficulty in promotion in the application of large-scale photovoltaic arrays

At present, there is no research on the application of particle swarm optimization support vector machine algorithm to the fault diagnosis and classification of photovoltaic power generation arrays in published literature and patents

Images