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.

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This paper presents a two-step cost-based method of optimally sizing and selecting BESS in standalone solar PV system applications considering predicted solar radiation data and economic performance (BESS cost analysis). The methodology is basically divided into two distinct parts; the first part is the sizing process and the second part is the selection process. In the first part, several BESS sizes suitable for a particular standalone PV system are determined using energy deficit and supply interruption outcomes of a PV system simulation with predicted hourly solar radiation series, hourly load demand and battery storage capacity as simulation parameters. In the second step, the economic performance of the determined BESS sizes is evaluated through a cost analysis process where two financial metrics; net present value (NPV) and payback period (PBP), are utilized. This step is necessary in order to ascertain the investment risks and benefits of the BESS sizes. To test its adequacy, the methodology was applied to two case studies; a residential load and a commercial load, and the results obtained for both case studies suggests that combining BESS sizing using predicted solar radiation data and BESS selection considering economic performance is an adequate process of incorporating BESS in standalone PV system applications.