Photovoltaic systems are prone to breaking down due to harsh conditions. To improve the reliability of these systems, diagnostic methods using Machine Learning (ML) have been developed. However, many publications only focus on specific AI models without disclosing the type of learning used. In this article, we propose a supervised learning algorithm that can detect and classify PV system defects. We delve into the world of supervised learning-based machine learning and its application in detecting and classifying defects in photovoltaic (PV) systems. We explore the various types of faults that can occur in a PV system and provide a concise overview of the most commonly used machine learning and supervised learning techniques in diagnosing such systems. Additionally, we introduce a novel classifier known as Extra Trees or Extremely Randomized Trees as a speedy diagnostic approach for PV systems. Although this algorithm has not yet been explored in the realm of fault detection and classification for photovoltaic installations, it is highly recommended due to its remarkable precision, minimal variance, and efficient processing. The purpose of this article is to assist technicians, engineers, and researchers in identifying typical faults that are responsible for PV system failures, as well as creating effective control and supervision techniques that can minimize breakdowns and ensure the longevity of installed systems.

Run-of-river hydropower plants are a cost-efficient technology that produce a power output proportional to the instantaneous flow of water diverted from the exploited stream by exploiting several mechanical, hydraulic, and electric devices. However, as no storage is available, its design and operation is tailored according to the unpredictability of its power generation. Hence, the modelling of this type of power plants is a necessity for the promotion of its development. Accordingly, based on models from the literature, this study proposes a comprehensive methodology for optimally designed small run-of-river hydropower plants based on a levelized cost of energy (LCOE). The proposed methodology aims at facilitating a faster design for more cost-effective and energy-efficient small hydropower plants. Depending on the average daily flow rates and the gross head of a given site, the model proposed in this study calculates the diameter, thickness, and length of a penstock; it also suggests the optimal selection of a turbine, determines the admissible suction head of a turbine for its optimal implementation, and determines the optimal number of turbines, all in order to minimize the LCOE of the proposed project. The model is tested to design a small run-of-river hydropower plant with a capacity of 6.32 MW exploiting the river Nyong in Mbalmayo. The results confirm the profitability of the investment with an LCOE of around 0.05 USD/kWh, which is the lowest limit value of the LCOE range for small hydropower plants, as presented in the IPCC (Intergovernmental Panel on Climate Change) report, assuming a project lifespan of 50 years and a discount rate of 12.5%. These results also show that it may be worth to provide the energy sector with a small hydropower design tool with a graphical interface. In addition, it would be appropriate to use a similar method in an off-grid context where a hydropower plant, with or without storage, is combined with another source to meet the electrical needs of a given population.

Sub Saharan Africa has a great renewable energy potential. Rural areas are suffering from poor energy access. Some systems designed to address this issue are still faced with some difficulties. Appropriate approaches and energy plant development will help remote areas to address the issue of electricity access. The current development of some micro hydro and micro solar energy plants is of poor quality and maintenance, sometimes resulting in failures. There are also some common mistakes made when promoting (designing) an energy system in an African environment. Identifying issues from local constraints and lessons learned will contribute to determining the appropriate sizing, technology and tools to correctly develop micro hydro and micro solar energy plants.