A Fault Diagnosis Method for PV Arrays Based on New Feature Extraction and Improved the Fuzzy C-Mean Clustering

Photovoltaic (PV) array fault diagnosis is vital for the safe and stable operation of PV systems. Up to now, there are many methods to diagnose and classify PV array faults successfully. However, the difficulty of fault diagnosis is increased because the PV arrays have nonlinear output characteristi...

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Vydané v:IEEE journal of photovoltaics Ročník 12; číslo 3; s. 833 - 843
Hlavní autori: Xu, Liuchao, Pan, Zhiheng, Liang, Chuandong, Lu, Min
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Arrays
Circuit faults
Clustering
Clustering algorithms
Degradation
Fault detection
Fault diagnosis
Faults
Feature extraction
fuzzy C-mean clustering (FCM) algorithm
ICMMD
Integrated circuit modeling
Parameters
photovoltaic (PV) array
Photovoltaic cells
Resistance
ISSN:2156-3381, 2156-3403
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Shrnutí:Photovoltaic (PV) array fault diagnosis is vital for the safe and stable operation of PV systems. Up to now, there are many methods to diagnose and classify PV array faults successfully. However, the difficulty of fault diagnosis is increased because the PV arrays have nonlinear output characteristics and complex working environments. In practice, the difference in characteristic parameters of different faults is not apparent, and it is not easy to effectively obtain labels of many samples. In order to address the above problems, this article proposes a new fault detection method for PV arrays based on the output current-voltage ( I - V ) characteristic, an improved fuzzy C-mean clustering (FCM) algorithm to identify four common PV array faults. The measured I - V characteristic curves are used to extract the initial feature parameters and then calculate the initial parameters to obtain characteristic parameters. In addition, it used characteristic parameters as feature variables of the FCM fault diagnosis model. Finally, classification results are verified by inner cluster maximum mean discrepancy and reclassification pseudoparameter based on the relationship between characteristic parameters. This method defines new characteristic parameters and achieves the purpose of fault detection by reclassification; in addition, we verify the high accuracy and simplicity of the method through simulation and experiment.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2156-3381
2156-3403
DOI:10.1109/JPHOTOV.2022.3151330