*Review* **Review of Artificial Intelligence-Based Failure Detection and Diagnosis Methods for Solar Photovoltaic Systems**

**Ahmad Abubakar \*, Carlos Frederico Meschini Almeida and Matheus Gemignani**

Department of Electrical Engineering and Automation, Escola Politecnica da Universidade de São Paulo, São Paulo 05508-010, Brazil; cfmalmeida@usp.br (C.F.M.A.); matheusg@alumni.usp.br (M.G.) **\*** Correspondence: namatoyaa@usp.br; Tel.: +55-11997680626

**Abstract:** In recent years, the overwhelming growth of solar photovoltaics (PV) energy generation as an alternative to conventional fossil fuel generation has encouraged the search for efficient and more reliable operation and maintenance practices, since PV systems require constant maintenance for consistent generation efficiency. One option, explored recently, is artificial intelligence (AI) to replace conventional maintenance strategies. The growing importance of AI in various real-life applications, especially in solar PV applications, cannot be over-emphasized. This study presents an extensive review of AI-based methods for fault detection and diagnosis in PV systems. It explores various fault types that are common in PV systems and various AI-based fault detection and diagnosis techniques proposed in the literature. Of note, there are currently fewer literatures in this area of PV application as compared to the other areas. This is due to the fact that the topic has just recently been explored, as evident in the oldest paper we could obtain, which dates back to only about 15 years. Furthermore, the study outlines the role of AI in PV operation and maintenance, and the main contributions of the reviewed literatures.

**Keywords:** artificial intelligence; photovoltaics; fault detection; machine learning; operation and maintenance; renewable energy
