On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant

Jun-Hyun Shin and Jin-O Kim
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Jun-Hyun Shin: Department of Electrical Engineering, Hany4ang University, Seoul 04763, Korea
Jin-O Kim: Department of Electrical Engineering, Hany4ang University, Seoul 04763, Korea

Energies, 2020, vol. 13, issue 17, 1-12

Abstract: This paper presents an on-line diagnosis method for large photovoltaic (PV) power plants by using a machine learning algorithm. Most renewable energy output power is decreased due to the lack of management tools and the skills of maintenance engineers. Additionally, many photovoltaic power plants have a long down-time due to the absence of a monitoring system and their distance from the city. The IEC 61724-1 standard is a Performance Ratio (PR) index that evaluates the PV power plant performance and reliability. However, the PR index has a low recognition rate of the fault state in conditions of low irradiation and bad weather. This paper presents a weather-corrected index, linear regression method, temperature correction equation, estimation error matrix, clearness index and proposed variable index, as well as a one-class Support Vector Machine (SVM) method and a kernel technique to classify the fault state and anomaly output power of PV plants.

Keywords: photovoltaic plant; on-line diagnosis; machine learning; operation and maintenance; health index; reliability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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