Abstract
The capacity of photovoltaic (PV) generators can increase owing to the 4030 policy of the Government of South Korea.. In addition, there has been significant interest in developing a technology for the maintenance of PV generators owing to an increase in the number of outdated PV generators. This paper describes a failure diagnosis method that uses operational data for power generation and solar radiation of PV generators. The measured data stored since four years in an operational 50-kW PV generator that was installed in 2014, were analyzed. The proposed failure diagnosis logic uses support vector machine classification as a failure diagnosis method that can classify normal and failure data. The failure data were processed to be used as the fault diagnosis logic for solar power generators. A new 50-kW PV generator, which contained no fault data, was used for a case study in this paper. Fault data were generated and the operation data of the PV generators were diagnosed by applying the proposed method. In addition, the accuracy was calculated and the results were analyzed.
Similar content being viewed by others
Change history
08 July 2020
The authors would like to correct the article as follows.
References
Kang K, Ko S, Chung Y (2015) Types of solar photovoltaic system failures and complaints. Bull Korea Photovolt Soc 1(1):42–48
Ministry of Trade, Industry and Energy (2019) The government’s third energy basic plan. June 2019
Electric power statistics information system (2019). https://epsis.kpx.or.kr. Accessed 1 Sept 2019
Official potal of the Korean government (2017) Announcement of implementation plan of renewable energy 3020. http://www.gov.kr. Accessed 19 Dec 2017
Kim Y, Shim K (2013) Efficiency computation and failure detection of solar power generation panels. J KIISE Comput Syst Theory 40(1):1–7
Ji P, Lee C, Lim J (2019) Failure diagnosis algorithm using radial basis function for solar radiation sensor. Trans Korean Inst Electr Eng 68P(1):41–46
Cho H, Jung Y, Lee G (2013) A study on failure detection for photovoltaic power modules using statistical comparison scheme. J Korean Sol Energy Soc 33(4):89–93
Chine W, Mellit A, Pavan AM, Kalogirou SA (2014) Failure detection method for grid-connected photovoltaic plants. Renew Energy 66:99–110
Lim J, Ji P (2016) Development of failure diagnosis algorithm using correlation analysis and ELM. Trans Korean Inst Electr Eng 65P(3):204–209
Wang L, Liu J, Guo X, Yang Q, Yan W (2017) Online failure diagnosis of photovoltaic modules based on multi-class support vector machine. In: 2017 Chinese automation congress
Zhao Y, Ball R, Mosesian J, de Palma J-F, Lehman B (2015) Graph-based semi-supervised learning for fault detection and classification in solar photovoltaic arrays. IEEE Trans Power Electron 30(5):2848–2858
Yi Z, Etemadi AH (2017) Line-to-line fault detection for photovoltaic arrays based on multiresolution signal decomposition and two-stage support vector machine. IEEE Trans Ind Electron 64(11):8546–8556
Appiah AY, Zhang X, Ayawli BBK, Kyeremeh F (2019) Long short-term memory networks based automatic feature extraction for photovoltaic array fault diagnosis. IEEE Access 7:30089–30101
Zhao Y, Yang L, Lehman B, de Palma J-F, Mosesian J, Lyons R (2012) Decision tree-based failure detection and classification in solar photovoltaic arrays. In: Twenty-seventh annual IEEE applied power electronics conference and exposition (APEC)
Park HY, Lee KY (2011) Pattern recognition and machine learning. Ehan Media 6–7:279–285
Funding
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20173010013610).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Cho, KH., Jo, HC., Kim, Es. et al. Failure Diagnosis Method of Photovoltaic Generator Using Support Vector Machine. J. Electr. Eng. Technol. 15, 1669–1680 (2020). https://doi.org/10.1007/s42835-020-00430-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42835-020-00430-9