Fault diagnosis method for photovoltaic array based on semi-supervised extreme learning machine

An extreme learning machine, photovoltaic array technology, applied in the field of electric power equipment

Active Publication Date: 2020-06-23
FUZHOU UNIV
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Problems solved by technology

The current semi-supervised learning algorithm applied to photovoltaic faults is still insufficient, and there is still a lot of room for improvement

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  • Fault diagnosis method for photovoltaic array based on semi-supervised extreme learning machine
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  • Fault diagnosis method for photovoltaic array based on semi-supervised extreme learning machine

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Embodiment Construction

[0101] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0102] Please refer to figure 1 , the present invention provides a photovoltaic array fault diagnosis method based on semi-supervised extreme learning machine, comprising the following steps:

[0103] Step S1: Obtain the voltage-current curve of the photovoltaic system in different fault states;

[0104] Step S2: according to the voltage-current curve of the different fault states that obtains, carry out feature extraction, and construct the fitting characteristic output equation of band adjustment coefficient;

[0105] Step S3: Calculate the characteristic coefficient based on the particle swarm-trust region reflection algorithm and the nonlinear least square method, and obtain a complete photovoltaic parameter characteristic equation;

[0106] Step S4: carry out transposition and standardization processing to complete photovoltaic parameter characteri...

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Abstract

The invention relates to a fault diagnosis method for a photovoltaic array based on a semi-supervised extreme learning machine, which comprises the steps of firstly, acquiring an output voltage-current curve of the photovoltaic array through an acquisition device; then performing feature extraction on the current-voltage curve, and constructing a fitting feature output equation with an adjustmentcoefficient; secondly, solving the adjustment coefficient by adopting a nonlinear least square method based on particle swarm-trust region reflection optimization; obtaining a feature standardizationequation by performing item shifting and standardization on the feature output equation; furthermore, carrying out fault identification on the photovoltaic array in combination of a small number of label samples and a large number of label-free samples by adopting a semi-supervised extreme learning machine based on artificial bee colony optimization as a classifier; and finally, regularly measuring a current-voltage curve of normal operation of the photovoltaic array to update the standardized equation so as to adapt to natural aging of the photovoltaic array.

Description

technical field [0001] The invention relates to the field of electric power equipment, in particular to a photovoltaic array fault diagnosis method based on a semi-supervised extreme learning machine. Background technique [0002] With the vigorous development of the photovoltaic industry, the construction cost of photovoltaic systems has gradually decreased, the installed capacity and quantity have continued to increase, and the cost of operation and maintenance has also continued to increase. Since the photovoltaic system needs to be installed in an outdoor environment with many uncertain factors, it is easily affected by various environmental factors such as thermal cycle, humidity, ultraviolet rays, and shadows during operation, which may easily lead to various unknown faults, lower power generation efficiency, and serious faults In some cases, the equipment may even be damaged, causing serious hazards such as fire. The complex outdoor environment makes photovoltaic mod...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H02S50/00H02S50/10G06N3/00
CPCG06N3/006H02S50/00H02S50/10Y02E10/50
Inventor 高伟黄俊铭郭谋发杨耿杰
Owner FUZHOU UNIV
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