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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 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.

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.

The importance of power in society is indisputable. Virtually all economic activities depend on electricity. The electric power systems are complex, and move studies in different areas are motivated to make them more efficient and solve their operational problems. The smart grids emerged from this approach and aimed to improve the current systems and integrate electric power using alternative and renewable sources. Restoration techniques of these networks are being developed to reduce the impacts caused by the usual power supply interruptions due to failures in the distribution networks. This paper presents the development and evaluation of the performance of a multi-objective version of the Bacterial Foraging Optimization Algorithm for finding the minor handling switches that maximize the number of buses served, keeping the configuration radial system and within the limits of current in the conductors and bus voltage. An electrical system model was created, and routines were implemented for the network verification, which was used as a function of the Multi-Objective Bacterial Foraging Optimization Hybrid Algorithm. The proposed method has been applied in two distribution systems with 70 buses and 201 buses, respectively, and the algorithm’s effectiveness to solve the restoration problem is discussed.

The control of expenses related to electricity has been showing significant growth, especially in residential environments. Monitoring of electrical loads that are turning on and off from home are often performed using smart plugs, providing to the consumers' information about operation intervals and power consumed by each device. Despite a practical solution to control and reduce electricity costs, it has a high cost due to the number of meters required. The high-cost problem can be worked around by using a non-intrusive load monitoring proposal (NILM), where voltage and current measurements are taken at the home entrance, in counterpart demand an extra processing step. In this extra step, various actions like computation of powers, identification of the occurrence of events, and identification of the status of which equipment (on/ off) must be made. The purpose of this work was the elaboration of a heuristic type event detector using floating analysis windows for locating stability zones on power signals after indicating a power change above a predetermined value. For that, tests of the best arrangement of event identifier data to identify which load has been added or removed from the monitored circuit are made. The proposed hybrid approach optimizes the processes using the Fireworks Algorithm (FWA) were used in the Random Forest classifier to improve classification performance. The proposed event identifier and classifier tests were performed on the dataset BLUED, which contains data collected at a North-American residence over one week. The event identifier results were compared with other publications that used different approaches, and the results of the classifications were compared to each other, using various data entry forms, and as an ideal classifier.