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Appl. Sci. 2017, 7(11), 1199; doi:10.3390/app7111199

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

School of Electrical and Electronic Engineering, North China Electric Power University, Changping District, Beijing 102206, China
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Received: 30 September 2017 / Revised: 8 November 2017 / Accepted: 20 November 2017 / Published: 21 November 2017
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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. View Full-Text
Keywords: fault monitoring; photovoltaic array; the LSSVM algorithm; Bayesian framework fault monitoring; photovoltaic array; the LSSVM algorithm; Bayesian framework
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Sun, J.; Sun, F.; Fan, J.; Liang, Y. Fault Diagnosis Model of Photovoltaic Array Based on Least Squares Support Vector Machine in Bayesian Framework. Appl. Sci. 2017, 7, 1199.

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