Fault Diagnosis Model of Photovoltaic Array Based on Least Squares Support Vector Machine in Bayesian Framework

Jiamin Sun,Yutu Liang,Fengjie Sun,Jieqing Fan

doi:10.3390/app7111199

Jiamin Sun, Yutu Liang + Show 2 more

Open Access

https://doi.org/10.3390/app7111199

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Journal: Applied sciences	Publication Date: Nov 21, 2017
Citations: 22	License type: CC BY 4.0

Affiliation: North China Electric Power University

Abstract

With the rapid development of the photovoltaic industry, fault monitoring is becoming an important issue in maintaining the safe and stable operation of a solar power station. In order to diagnose the fault types of photovoltaic array, a fault diagnosis method that is based on the Least Squares Support Vector Machine (LSSVM) in the Bayesian framework is put forward. First, based on the elaborate analysis of the change rules of the output electrical parameters and the equivalent circuit internal parameters of photovoltaic array in different fault states, the input variables of the photovoltaic array fault diagnosis model are determined. Second, through the LSSVM algorithm in the Bayesian framework, the fault diagnosis model based on the output electrical parameters and the equivalent circuit internal parameters of the photovoltaic array is built, which can effectively detect the photovoltaic array faults of short circuit, open circuit, and abnormal aging. Then, the simulation model is built to verify the validity of the LSSVM algorithm in the Bayesian framework by comparing it with the model of LSSVM and the Support Vector Machine (SVM). Moreover, a 5 × 3 photovoltaic array and a reference photovoltaic string are established and experimentally tested to validate the performance of the proposed method.

Highlights

With the aggravation of the global energy crisis and regional environmental pollution, Chinese photovoltaic power generation still faces key problems of sustainable development [1], of which maintaining solar power station safety and maintaining stable operations are important issues.At present, research on solar power station fault monitoring is mainly focused on the photovoltaic strings, modules, and inverters, but rarely on the photovoltaic arrays
In order to diagnose whether there areare faulty photovoltaic modules in a in photovoltaic arrayarray or notor notfurther and further its type, fault this type,paper this paper presents fault diagnosis that ison based on and judgejudge its fault presents a faultadiagnosis methodmethod that is based
The Least Squares Support Vector Machine (LSSVM) multi-classifiers were converted into six two-classifiers by the classification algorithm of “One vs. One”, which are “the normal vs. the short-circuits”, “the normal vs. the open-circuits”, “the normal vs. the abnormal aging”, “the short-circuits vs. the open-circuits”, “the short-circuits vs. the abnormal aging”, and “the open-circuits vs. the abnormal aging”

Summary

Introduction

With the aggravation of the global energy crisis and regional environmental pollution, Chinese photovoltaic power generation still faces key problems of sustainable development [1], of which maintaining solar power station safety and maintaining stable operations are important issues.At present, research on solar power station fault monitoring is mainly focused on the photovoltaic strings, modules, and inverters, but rarely on the photovoltaic arrays. The diagnosis of the photovoltaic arrays is an important issue, because the performance of photovoltaic modules affects the output characteristics of photovoltaic arrays directly, further affecting the stability of the photovoltaic generation system, so the fault monitoring of the solar power station can diagnose the photovoltaic arrays, locate the faulty photovoltaic modules in a certain area first, and further precisely position the faulty photovoltaic modules. This diagnostic method can greatly reduce the number of sensors, reducing costs while ensuring that the solar power station operates safely and stably. The algorithm of the artificial neural network was presented to diagnose the fault [2,3,4,5]

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Results

Conclusion