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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 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.

Smart grids are a reality in distribution systems. They have assisted in the operation, control, and most of all, the protection of urban networks, significantly solving the contingencies of these networks. This paper treats the initial stage of implementing smart grid switching devices in distribution networks. In this stage, smart grid technologies need to operate with the traditional protection elements (such as fuses, reclosers, and sectionalizers). This fact can create trouble in the protection schemes because there are two distinctive philosophies. In some companies, especially those without substantial capital, these two protection philosophies can run together for many years. The most popular intelligent electronic devices (IEDs) available in the market are studied to verify their features and the possibility to incorporate techniques to allow the two philosophies to work together. After that, the proposed approach shows how the existing IEDs can interact with the traditional devices. Special functions can also be incorporated to inform the control center of an operational problem, increasing the observability of the network. With the proposed approach, the IEDs are transformed into intelligent agents. Practical examples using real distribution systems are presented and discussed, proving the efficacy of the proposed methodology.

Este trabalho trata do amortecimento de propagação harmônica em linhas de distribuição, visando melhora na qualidade da tensão. A propagação harmônica ocorre devido às ressonâncias entre as impedâncias indutivas do sistema e bancos de capacitores utilizados em filtros passivos e compensadores de reativos, amplificando a distorção causada por cargas não lineares nas proximidades, e pode ser amortecida através do uso de filtros ativos emulando resistências harmônicas. Esta abordagem (ao invés do uso convencional dos filtros ativos shunt para diretamente compensar as correntes harmônicas) é muito atrativa para sistemas de distribuição, aonde as cargas não lineares estão distribuídas e inacessíveis. A contribuição principal deste artigo é a implementação desta resistência harmônica em um equipamento de armazenamento de energia e suporte de rede como uma funcionalidade extra, sem necessidade de sensores adicionais. Resultados experimentais são apresentados em uma bancada de testes.

Motor current signature analysis (MCSA) enables non-invasive monitoring, without interruption of machine operation in a remote and online way, allowing the identification of various types of faults of electrical and mechanical nature without the need of accessing the motor itself, but only its supply cables. Despite its advantages, it has limitations in accurately diagnosing incipient roller bearing faults. For the detection of incipient roller bearing faults, envelope analysis of vibration signals is a well-known and stablished technique used by motor condition monitoring experts for a long time, overcoming MCSA for that purpose. Thus, it is proposed in this paper, that the fault characteristic frequencies of roller bearings are identified in the current spectrum with the aid of envelope analysis on the bearing vibration signal. After this aided identification, the fault related spectral components in the current spectrum can be correctly tracked over time for trending evaluation and decision-making. This approach can represent a significant economic value in a motor condition monitoring program, since vibration envelope analysis is performed only at a first step and, after that, its results can be applied for the MCSA monitoring of all same-model motor drivers in an industrial site. This approach is even more valuable considering the concept of the Self-Supplied Wireless Current Transducer (SSWCT) also proposed in this paper. The SSWCT is an Industrial Internet of Things (IIOT) device for MCSA application in an Industry 4.0 environment. This proposed device has wireless communication interface and wireless/battery less power supply, being supplied by the energy harvested from the magnetic field of the same currents it is transducing. So, it is a completely galvanic isolated monitoring device, without batteries and without any electric connections to the industry electric system, easily installable to the motor cables, not using precious space in the electric panels of the motor control centers and not having any physical contact to the monitored asset.

The condition of synchronous generators (SGs) is a matter of great attention, because they can be seen as equipment and also as fundamental elements of power systems. Thus, there is a growing interest in new technologies to improve SG protection and maintenance schemes. In this context, electrical signature analysis (ESA) is a non-invasive technique that has been increasingly applied to the predictive maintenance of rotating electrical machines. However, in general, the works applying ESA to SGs are focused on isolated machines. Thus, this paper presents a study on the condition monitoring of SGs in bulk electric systems by using ESA. The main contribution of this work is the practical results of ESA for fault detection in in-service SGs interconnected to a power system. Two types of faults were detected in an SG at a Brazilian hydroelectric power plant by using ESA, including stator electrical unbalance and mechanical misalignment. This paper also addresses peculiarities in the ESA of wound rotor SGs, including recommendations for signal analysis, how to discriminate rotor faults on fault patterns, and the particularities of two-pole SGs.

Este trabalho descreve a construção de um medidor do conjugado eletromagnético de motores de indução trifásicos a partir das amostras de correntes e de tensões trifásicas. A estratégia para o cálculo do conjugado é a síntese do Fluxo do Estator através de Filtros Passa-Baixas em Cascata Programáveis (PCLPF - Programmable Cascaded Low-Pass Filters). A implementação é feita através de Microprocessador tipo Digital Signal Processor (DSP). O uso deste tipo de filtro contorna o problema da integração numérica necessária ao cálculo do fluxo a partir das amostras de tensão e de corrente. Na implementação do Filtro Passa-Baixas em Cascata Programável é utilizada uma Rede Neural Recorrente (RNN - Recurrent Neural Network) treinada com um algoritmo baseado em filtro de Kalman. O resultado desta implementação é um equipamento com precisão compatível com os medidores de conjugado baseados em eixos metálicos, com constante elástica conhecida e strain gauges.

Induction motors consume a great portion of the generated electrical energy. Moreover, most of them work at underloaded conditions, so they have low efficiencies and waste a lot of energy. Because of this, the efficiency estimation of in-service induction motors is a matter of great importance. This efficiency estimation is usually performed through indirect methods, which do not require invasive measurements of torque or speed. One of these methods is the modified Air-Gap Torque (AGT) method, which only requires voltage and current data, the stator resistance value, and the mechanical losses. This paper approaches the computation of a modified stator resistance including the mechanical losses effect to be applied in the AGT method for torque and efficiency estimation of induction motors. Some improvements are proposed in the computation of this resistance by using a direct method, as well as the possibility to estimate this parameter directly from the nameplate data of the induction motor. The proposed methodology only relies on line voltages, currents, and nameplate data and is not intrusive. The proposed methodology is analyzed through simulation and validated through experimental results with three-phase induction motors. Also, a comparison of methods for in-service induction motors efficiency estimation is presented for the tested motors.

This paper proposes a novel adaptive hysteresis current control strategy of a voltage-fed PWM inverter, where the switching frequency always remains constant and the harmonic ripple is minimum, irrespective of dc link voltage fluctuation and load parameter variation. The developed algorithm works well for single-phase as well as multi-phase inverters, although single-phase inverter operation, particularly in active harmonic filter mode, is emphasized in this paper. The algorithm uses fuzzy logic-based current error compensation, and it is simple to implement and does not involve any hysteresis-band calculation. The control strategy has been implemented digitally with a high-speed digital signal processor (TI-TMS320F2812). The proposed scheme has been thoroughly evaluated in laboratory by simulation as well as in experimental studies on a single-phase inverter, and its performance was found to be excellent.