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.

Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman neural network (ENN), boosted tree algorithms (BTA), multi-layer perceptron (MLP), and Gaussian processes regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages its strengths for the accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction. The fault detection and diagnosis were assessed using several statistical criteria including PBAIS, MAE, NSE, RMSE, and MAPE. Two intelligent learning scenarios are carried out. The first scenario is conducted for PV array fault detection with DC power (DCP) as output. The second scenario is conducted for inverter fault detection with AC power (ACP) as the output. The proposed technique is capable of detecting faults in PV arrays and inverters, providing a reliable solution for enhancing the performance and reliability of solar energy systems. A real-world solar energy dataset is used to evaluate the proposed technique with results compared to existing detection techniques and obtained results showing that it outperforms existing fault detection techniques, achieving higher accuracy and better performance. The GPR-M4 optimization justified its reliably among all the models with MAPE = 0.0393 and MAE = 0.002 for inverter fault detection, and MAPE = 0.091 and MAE = 0.000 for PV array fault detection.

Issues such as climate change, water scarcity, population growth, and distribution losses have stimulated the use of new technologies to manage water resources. This is how the concept of smart water management emerged as a subcategory of the concept of smart cities. This article aimed first to identify the applications of smart water-management systems described in academic articles either as applications in development or as applications already implemented or as future trends; and, second, to classify them according to the processes in the value chain of public water supply services. To this end, a systematic review of the literature was carried out, in which 100 mentions of applications were identified in 62 selected articles; then, the mentions were grouped into 10 categories. The most frequent application categories were smart meters, implementation models and architectures, and loss management. Among the processes of the value chain, applications in processes of distribution and water use were highly predominant. The lack of detail about the integration between the different applications for a smart water-management system was pointed out as a limitation and an opportunity for future research development, especially in terms of a technological roadmap study based on the relationship between smart meters and loss management.

Routine inspections programs are procedures that utilities can use to improve their power quality standards and public perception. However, due to budget restrictions, it is usually not possible to inspect all elements in the whole concession area. In this scenario, strategies to efficiently allocate available teams at scheduled inspections are an interesting alternative. The challenge is to define which data are relevant and at hand to develop an strategy that is comprehensive, efficient and outperforms the current old fashioned way based on personal experience applied to automated spreadsheets. This paper proposes an approach that deals with a large amount of data from corporate systems as input to a proposed optimization engine based on Genetic Algorithm and heuristics. Primary feeders are split into segments among protection devices, referred to as metazones. Inspections are performed to produce a set of prioritized metazones by maximizing the sum of merit indexes related to loss of energy, voltage and current violations, as well as outages, while satisfying teams' inspections capacities. A case study with 56 primary feeders is presented, and results show that the yielded prioritized metazones are compatible with and improve current power utilities practices.

The recent developments in the design, planning, and operation of distribution systems indicate the need for a modern integrated infrastructure in which participants are managed through the perceptions of a utility company in an economic network (e.g., energy loss reduction, restoration, etc.). The penetration of distributed generation units in power systems are growing due to their significant influence on the key attributes of power systems. As a result, the placement, type, and size of distributed generations have an essential role in reducing power loss and lowering costs. Power loss minimization, investment and cost reduction, and voltage profile improvement combine to form a conceivable goal function for distributed generation allocation in a constrained optimization problem, and they require a complex procedure to control them in the most appropriate way while satisfying network constraints. Such a complex decision-making procedure can be solved by adjusting the dynamic optimal power flow problem to the associated network. The purpose of the present work is to handle the distributed generation allocation problem for photovoltaic units, attempting to reduce energy and investment costs while accounting for generation unpredictability as well as load fluctuation. The problem is analyzed under various scenarios of solar radiation through a stochastic programming technique because of the intense uncertainty of solar energy resources. The formulation of photovoltaic distributed generation allocation is represented as a mixed-integer second-order conic programming problem. The IEEE 33-bus and real-world 136-bus distribution systems are tested. The findings illustrate the efficacy of the proposed mathematical model and the role of appropriate distributed generation allocation.

Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman Neural Network (ENN), Boosted Tree Algorithms (BTA), Multi-layer Perceptron (MLP), and Gaussian Processes Regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages their strengths for accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction, the fault detection and diagnosis were assessed using several statistical criteria including PBAIS, MAE, NSE, RMSE, and MAPE. Two intelligent learning scenarios are carried out, the first scenario is done for PV array fault detection with DC power (DCP) as output. The second scenario is done for inverter fault detection with AC power (ACP) as the output. The proposed technique is capable of detecting faults in PV arrays and inverters, providing a reliable solution for enhancing the performance and reliability of solar energy systems. Real-world solar energy dataset is used to evaluate the proposed technique, with results compared to existing detection techniques and obtained results show that it outperforms existing fault detection techniques, achieving higher accuracy and better performance. The GPR-M4 optimization justified reliably among all the models with MAPE=0.0393, and MAE=0.002 for inverter fault detection and MAPE=0.091, and MAE=0.000 for PV array fault detection.