Photovoltaic string fault diagnosis method based on kernel function limit learning machine

Abstract

The invention relates to a photovoltaic string fault diagnosis method based on a kernel function limit learning machine. The method specifically comprises the following steps that 1, voltage-current characteristic scanning is conducted on a photovoltaic string, and curve fitting is conducted to acquire five photovoltaic internal equivalent parameters; 2, the acquired five photovoltaic internal equivalent parameters are integrated and unified; 3, a plurality of coefficients of an optimal KELM algorithm kernel function are calculated by adopting a pattern search algorithm; 4, the calculated coefficients are brought into the KELM, and samples are trained to obtain a training model; 5, fault detection and classification are conducted on the photovoltaic string through the training model. According to the photovoltaic string fault diagnosis method based on the kernel function limit learning machine, the accuracy of fault detection and classification on a photovoltaic power generation array can be effectively improved.

Description

technical field [0001] The invention relates to photovoltaic power generation string fault detection and classification technology, in particular to a photovoltaic string fault diagnosis method based on a kernel function extreme learning machine. Background technique [0002] Since the photovoltaic module array is installed and works in a complex outdoor environment, and is affected by various environmental factors such as thermal cycle, humidity, ultraviolet rays, and wind excitation, it is prone to local material aging, performance degradation, cracks, open circuits or short circuits, etc. A kind of failure problem, the generation of failure will reduce the power generation efficiency of the power station, and even a fire will occur in severe cases, endangering the safety of social property. Highly efficient power generation, thereby reducing the cost of photovoltaic power generation, improving the safety of the power station in the operation process, timely, effective and...

AI Technical Summary

Problems solved by technology

[0005] At present, there is no research on applying the kernel function extreme learning machine algorithm to the fault diagnosis and classification of photovoltaic power generation strings in published literature and patents

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