Root cause analysis for inverters in solar photo-voltaic plants

• Published in: Engineering failure analysis Vol. 118; p. 104856
• Main Author: Arias Velásquez, Ricardo Manuel
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2020
• Subjects: Automated; Failure assessment curve; Inverters; Photo-voltaic; Root cause analysis; Root cause analysis; Inverters; Automated; Failure assessment curve; Photo-voltaic
• ISSN: 1350-6307; 1873-1961
• DOI: 10.1016/j.engfailanal.2020.104856

•Novel framework for autonomous root cause fault analysis.•There is no need for a priori process knowledge and expert intervention.•Root cause analysis based on 65,000 inverters, 10,273,928 millions of data structured•Random committee and Logistic Model Tree algorithms has 99.21% accuracy. This research proposes a novel framework for autonomous root cause fault analysis, in a complex process with continuous learning. The potential root cause candidates are selected according a data mining process with 2 algorithms fully automated: Random Committee (RC) and Logistic Model Trees (LMT); they are competing for the best result. To determine the performance and application, it has been developed in a real case study, with the root cause analysis based on 65,000 inverters, 10,273,928 millions of data structured from February 2019 to February 2020, and their failures analysis; the results provide high accuracy, with a performance of 99.21% for the root cause analysis; it has been validated in a real solar photo-voltaic plant.