On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant
- Title
- On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant
- Author
- 김진오
- Keywords
- photovoltaic plant; on-line diagnosis; machine learning; operation and maintenance; health index; reliability
- Issue Date
- 2020-09
- Publisher
- MDPI
- Citation
- ENERGIES, v. 13, no. 17, article no. 4584
- 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.
- URI
- https://www.mdpi.com/1996-1073/13/17/4584https://repository.hanyang.ac.kr/handle/20.500.11754/170805
- ISSN
- 1996-1073
- DOI
- 10.3390/en13174584
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > ELECTRICAL AND BIOMEDICAL ENGINEERING(전기·생체공학부) > Articles
- Files in This Item:
- On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant.pdfDownload
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