Abstract With the growing demand for solutions to prevent and resolve faults in gas-insulated substations that affect the operation of the electrical system, a fault simulation laboratory for gas-insulated substations was developed to develop solutions for monitoring and locating these faults. The laboratory is composed of instruments for a fault location system based on traveling wave theory. The laboratory also has computational programs for fault simulation control and signal acquisition, in addition to having tools to analyze the data. The results were obtained through tests carried out that confirmed the laboratory's ability to simulate the operation of faults in gas-insulated substations, which allowed demonstrating the capacity of the fault location system using the theory of traveling waves.

Abstract Surge arresters are at the forefront of protection against voltage surges. The loss of the protective effect of surge arresters imposes accidents and unplanned shutdowns on the power system. It directly affects the interruption of power supplies and imposes fines on service providers. Monitoring and diagnostics in the lightning rod field require studies and work that present the degradation behaviour of surge arresters to help develop and improve existing techniques. This paper presents the development of a laboratory with a low-energy, mobile current waveform impulse generator. It allows for conducting aging tests by applying discharges to low-voltage zinc oxide surge arresters used in the energy distribution sector. Samples of surge arresters were subjected to the discharges produced by the developed generator, and they exhibited degradation, thus showing asymmetry between the positive and negative half-cycles of leakage current.

Abstract Shielded substations with SF6 gas insulation (GIS) play a critical role in supplying power to urban areas, and it is crucial to quickly identify and isolate faults to restore system operation and meet supply demands. However, locating faults in such substations can involve extensive and time-consuming procedures, such as opening and closing hermetically sealed compartments containing SF6. This paper presents the development of an acoustic test bench that accurately replicates the dynamic characteristics of SF6 shielded substations. By utilizing the traveling wave technique, partial discharges occurring in high-voltage insulation can be detected and precisely located. The method employs various techniques, including the analysis of the arrival time of traveling waves at different points within the substation and triangulation methods. The use of traveling waves in partial discharge detection offers several advantages, such as precise fault location and non-interference with normal substation operations. Additionally, acoustic methods are highly sensitive to other sources of partial discharges, such as moving particles and fluctuating potential discharges. The results demonstrate that the combination of the acoustic test bench and the disturbance localization technique presents an innovative approach to fault location studies in GIS. The acoustic test bench enables controlled simulation of failure conditions, providing accurate data on the acoustic behavior of substations. Through the disturbance location technique, these data can be analyzed and interpreted to pinpoint the exact fault location. This integrated approach enhances the efficiency of the troubleshooting process and reduces the time required to repair GIS.

Abstract The presented development is an intelligent diagnostic system for transformers that studied machine learning techniques to determine the operational status of these transformers. The study of these techniques is initiated by observing the quantities that define the operational behavior of large transformers, aiming to identify anomalies in their operation from data from sensors that equipment it in the functioning environment. This large power transformer has a theoretical service life of above 20 years and a low failure rate. Thus, obtaining failure values, which have their evolution monitored for large transformers, is almost nil. Therefore, a supervised machine training methodology to diagnose these cases is practically unfeasible. The study carried out with several traditional intelligent techniques can verify this. Several supervised methods (Closest Neighbor K-th Neighbor, Support Vector Machine, Radial Base Function, Decision Trees, Random Forest, Neural Network, AdaBoost, Gaussian Naive Bayes, and Quadratic Discriminant Analysis) were studied.

Abstract As high-voltage circuit breakers (HVCBs) are responsible for switching off the load in the event of anomalies, they suffer various wear and tear, both on their main contacts and on the other actuation mechanisms. Not only load maneuvers but also weather conditions can bring factors that contribute to deterioration and, consequently, contribute to failures of this component that is so important for energy supply. Both failures and maintenance shutdowns generate costs for substations, something that could be minimized if there was monitoring of the condition of the HVCBs. This paper shows a methodology to analyze the vibration signal of HVCB in order to identify and quantify contact failures. The proposed methodology is verified through an experimental setup. The results show that it is possible not only to identify the fault but also to assess its intensity using vibration analysis.

Abstract The need for companies to seek new techniques that generate productivity gains made them carry out the mapping of their processes using Business Process Management (BPM). Well-mapped processes make it possible to identify repetitive tasks, which can be automated using Robotic Process Automation (RPA), where a computer program mimics the actions of an employee. Furthermore, recent advances in Artificial Intelligence have brought new possibilities to RPA. The systematic review developed aims to raise information on Robotic Process Automation (RPA) integrated with Artificial Intelligence (AI) for autonomously automating work processes.

Abstract The planning of a new thermal power plant is linked to the various decision elements and evaluation criteria. Factors such as the plant's geographic positioning, primary energy supply points, paths, and means of delivery of this primary energy should be analyzed. Similar studies are imposed when studying the change of a thermoelectric plant's primary energy source occurs. In Brazil, several plants are currently investigating the exchange of their primary fuel from oil to gas due to the decrees issued by ANEEL. This paper presents software, which uses virtual reality to assist in the various stages of the planning process and in the analyses that must be performed. This software was developed for the Hidrotermica Group and had as the primary target the Borborema Thermoelectric Power Plant.

Abstract The multicriteria decision-making process is still an open problem, especially when the decision criteria are not numerical or fully outlined. Several numerical, intelligent, or hybrid techniques have been developed, creating contributions to this problem's solution. This paper is another step in this direction. Based on the Modified Analytic Hierarchy Process (MAHP), a methodology for diagnosis and performance analysis is presented for the dispatch ranking of generating units in a thermoelectric plant. The problem is complex because it covers a power plant, where 99 generating units may be dispatched, according to 10 possible evaluation criteria, which should be used together. This article also presents details of the implementation of the sensors necessary to add to the supervisory system existing in the Palmeiras de Goias Thermal Power Plant.

Abstract Microgrids (MGs) have increasingly attracted the attention of researchers, government officials, and electricity companies for the values and services they can add to the grid. They can operate independently as a single controllable unit in a coordinated way while connected or islanded, which make them suitable to integrate the growing amount of distributed energy resources (DERs) into the distribution network (DN). An active distribution network with high penetration of DERs could be redefined into a collection of microgrids in different layers, pooling their resources together to enhance the grid’s performance in the sense of electricity as a service. However, enabling a microgrid-based power system infrastructure poses challenges concerning operation and control. In this paper, a comprehensive overview of research topics regarding microgrid operation are shown in a hierarchical or stratified manner, looking for a holistic vision of a microgrid-based distribution system. First, multi-objective optimization within one microgrid is formulated aiming to minimize costs and maximize battery life is presented. Second, the interaction among MGs, DERs and the DN was modeled using the OPF technique to represent the operation of the grid as a whole. Following, a methodology for collaborative optimization of multiple MGs operating together is presented and at last a theoretical framework for MG operation through decentralized energy markets is discussed. As a result, this paper aims to contribute to a broad vision of the distribution system based on microgrid operation in all its levels.

Abstract The continuity of an electrical system's services is linked to its equipment's correct operation. The more correctly they worked, the higher the quality of the services provided. Thus, they must have their functioning scanned closely and that minor defects have their evolution monitored. This fact creates the possibility of making predictions about how long equipment can operate without compromising the system's continuity. This follow-up is proper for predictive maintenance. This paper presents three on-going COPEL Generation and Transmission projects in essential elements of its electrical system: circuit breakers, lightning arresters, and SF6 substation. The central idea of all monitoring projects is to check the early problem in this equipment and facilities.