Fault Detection and Classification of a Photovoltaic Generator Using the BES Optimization Algorithm Associated with SVM

被引：4

作者：

Koloko, R. J. Koloko ^{[1
,2
]}

Ele, P. ^{[1
,2
,3
]}

Wamkeue, R. ^{[1
,2
,4
]}

Melingui, A. ^{[1
]}

机构：

[1] Univ Yaounde I, Natl Adv Sch Engn, Lab Elect Engn Mechatron & Signal Proc, Soa, Cameroon

[2] Univ Yaounde1, Univ Ctr Res Energy Hlth CURES, Natl Adv Sch Engn, Soa, Cameroon

[3] Univ Douala, Doctoral Training Unit Engn Sci, Lab Technol & Appl Sci LTSA, Soa, Cameroon

[4] Univ Yaounde I, Ctr Excellence Informat & Commun Technol CET, Natl Adv Sch Engn, Soa, Cameroon

来源：

INTERNATIONAL JOURNAL OF PHOTOENERGY | 2022年 / 2022卷

关键词：

SOLAR-CELL MODELS; CUCKOO SEARCH ALGORITHM; I-V MODEL; PARAMETER-ESTIMATION; IDENTIFICATION; EXTRACTION;

D O I：

10.1155/2022/6841861

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

In this work, an innovative approach based on the estimation of the photovoltaic generator (GPV) parameters from the Bald Eagle Search (BES) optimization algorithm, associated with a support vector machine (SVM) classification algorithm, allowed to highlight a new tool for the classification of the signatures of shading and moisture PV defects. It recognizes signatures generated by the GPV in healthy and erroneous operation using the optimized parametric vector and classifies defects using the same optimized vector. The technique emphasizes the resilience of parameter estimate in terms of error on all parameters. The classification accuracy is 93%. The residuals between the estimated curve in healthy operation with a minimum error of the order of 10(-4) and the one at fault are used as an indicator of faults.

引用

页数：14

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