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Photovoltaic array fault diagnosis method based on semi-supervised extreme learning machine

An extreme learning machine and photovoltaic array technology, which is applied in the field of electrical and electrical equipment to achieve high fitting accuracy and avoid deviations

Active Publication Date: 2021-11-26
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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  • Photovoltaic array fault diagnosis method based on semi-supervised extreme learning machine
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  • Photovoltaic array fault diagnosis method 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 photovoltaic array fault diagnosis method based on a semi-supervised extreme learning machine. First, the output voltage-current curve of the photovoltaic array is obtained through the acquisition device; then, the feature extraction of the current-voltage curve is carried out, and the fitting characteristic output equation with adjustment coefficient is constructed; secondly, the particle swarm-trust region reflection optimization based on the The nonlinear least squares method is used to solve the adjustment coefficient; the feature standardization equation is obtained by transposing and standardizing the feature output equation; moreover, a semi-supervised extreme learning machine based on artificial bee colony optimization is used as a classifier for a small number of labeled samples combined with a large number of unlabeled samples. Fault identification of the photovoltaic array of the sample; finally, regular measurement of the current-voltage curve of the normal operation of the photovoltaic array to update the standardized equation, which can adapt to the 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 Patents(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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