This paper applies finite control set model predictive control (FCS-MPC) for dynamic reactive power compensation using a hybrid active power filter (HAPF). The FCS-MPC uses a model based on LCL-filter equations to predict the system behavior and optimize the control action. In fact, the application of FCS-MPC in grid-connected converters with LCL-Filter is quite recent. This algorithm is a very promising control technique for power electronics converters and its use for reactive power control of hybrid filter has not been reported in the literature yet. This paper uses the FCS-MPC in a multivariable structure along with an adaptive notch filter to damp resonance. The main purpose is to improve the dynamic response of the HAPF. Simulation as well as practical results prove the feasibility of FCS-MPC application in HAPF reactive power control. The dynamic response of the equipment was significantly improved and represents the main contribution of this paper. As a result, the FCS-MPC allows tracking fluctuations and abrupt changes in load reactive power.

Optimal scheduling is a requirement for microgrids to participate in current and future energy markets. Although the number of research articles on this subject is on the rise, there is a shortage of papers containing detailed mathematical modeling of the distributed energy resources available in a microgrid. To address this gap, this paper presents in detail how to mathematically model resources such as battery energy storage systems, solar generation systems, directly controllable loads, load shedding, scheduled intentional islanding, and generation curtailment in the microgrid optimal scheduling problem. The proposed modeling also includes a methodology to determine the availability cost of battery and solar systems assets. Simulations were carried out considering energy prices from an actual time-of-use tariff, costs based on real market data, and scenarios with scheduled islanding. Simulation results provide support to validate the proposed model. Data illustrate how energy arbitrage can reduce microgrid costs in a time-of-use tariff. Results also show how the microgrid’s self-sufficiency and the storage system’s capacity can impact the microgrid’s energy bill. The findings also bring out the need to consider the scheduled islanding event in the day-ahead optimization for microgrids.

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.

Energy and economy are increasing the relationship over the years, where the energy becomes a significant resource to keep a country developing, and it supports its economy. Then, more reliable the energy should become, especially the distribution network, to keep the entire process running. In this level of energy distribution, where residential consumers and medium and small industries are supplied, the number of interconnections of the network is enormous. However, for economic and environmental aspects, these complex systems, which are operating close to their capacity, needs to increase the automation, appearing the concept of smart grids and the Advanced Distribution Management System (ADMS) and its methods to control. Inside of the ADMS, there are a lot of essential techniques. Among them, there are two techniques which are the most relevant for this paper: the self-healing and load management. In an ADMS system, these two techniques are treated separately, but the best solution occurs when they are computed together. In this paper, it is proposed an approach that can address both problems at the same time or individually, i.e., in place to have a sequential method to solve step-by-step the issues in the networks. The proposed algorithm, through reinforcement learning technique, can handle both problems together. The proposed approach is tested in a real urban distribution network with some created scenarios to compare the results with outages and overloads. Some comparisons with other methods are carried out.

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.