The aim of this manuscript is to introduce solutions to optimize economic dispatch of loads and combined emissions (CEED) in thermal generators. We use metaheuristics, such as particle swarm ...optimization (PSO), ant lion optimization (ALO), dragonfly algorithm (DA), and differential evolution (DE), which are normally used for comparative simulations, and evaluation of CEED optimization, generated in MATLAB. For this study, we used a hybrid model composed of six (06) thermal units and thirteen (13) photovoltaic solar plants (PSP), considering emissions of contaminants into the air and the reduction in the total cost of combustibles. The implementation of a new method that identifies and turns off the least efficient thermal generators allows metaheuristic techniques to determine the value of the optimal power of the other generators, thereby reducing the level of pollutants in the atmosphere. The results are presented in comparative charts of the methods, where the power, emissions, and costs of the thermal plants are analyzed. Finally, the comparative results of the methods were analyzed to characterize the efficiency of the proposed algorithm.
The optimization of economic load dispatch (ELD) is one of the oldest and most important tasks in power plant management. The objective of this paper was to analyze a new solution of the old problem ...of the ELD optimization by differential evolution (DE) including turning off the most inefficient generators. The criteria of incremental fuel costs and losses are used to determine the best parameters of active power of each
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th generator unit, ensuring that the demand and total losses are equal to the total generated power but minimizing the total cost of fuel. Materials and methods have been developed to solve the ELD, including lambda iteration method, gradient method, Newton’s method and so on. The results found for this case study, with the application of DE, were outstanding having a reduction of 19.88 % in the total fuel cost, comparing to classical methods that distribute the generation of power among all generators, including the least efficient ones. This method improves not only the efficiency of generation but also of the power plant generation planning.
Direct lightning discharges in overhead distribution networks invariably cause serious insulation damage, frequently leading to the electric system’s partial or total shutdown. Installing lightning ...arresters can be very effective, and it is commonly used to minimize this problem; however, considering that typically, electric distribution grids exhibit a very large number of electrical nodes, the massive use of lightning arresters may not be economically viable. In this way, this article proposes a methodology for allocating lightning arresters that can significantly reduce the number of lightning arresters installed, but at the same time maintaining an adequate protection level for the distribution grid. The proposed methodology, named Direct Discharge Crossing (DDC), analyzes the network criticality based on two main factors, which are the overvoltage magnitudes and the number of flashovers provoked by lightning discharges, and defines a feeder lightning performance function that is used to indicate the recommended location for lightning arresters’ installation. The simulation studies are accomplished using the IEEE 34 bus distribution grid and ATP software to demonstrate the efficacy of the proposed solution, which is confirmed by the results presented.
This paper presents a new approach to training for hydroelectric unit of energy (HUE) by using virtual reality Non-immersive techniques. The software offers two modules of training: maintenance and ...operation. The first module, maintenance, uses the learning approach based on practice and offers different training levels, divided into three modes: automatic, guided, and exploratory, in which these modes are accessed according to the acquired degree of knowledge by the trainee in relation to maintenance procedures. The second module, allows the trainee to visualize the operation of HUE during a certain event as the electromechanical dynamics of the turbine-generator assemblage in the virtual world by the visualization of several requisite conditions before the startup-shutdown procedure of HUE.
The efficient protection of electric power distribution networks against lightning discharges is a crucial problem for distribution electric utilities. To solve this problem, the great challenge is ...to find a solution for the installation of surge arresters at specific points in the electrical grid and in a sufficient quantity that can ensure an adequate level of equipment protection and be within the utility’s budget. As a solution to this problem of using ATP (Alternative Transient Program), this paper presents a methodology for optimized surge arrester allocation based on genetic algorithm (GA), with a fitness function that maximizes the number of protected equipment according to the financial availability for investment in surge arresters. As ATP may demand too much processing time when running large distribution grids, an innovative procedure is implemented to obtain an overvoltage severity description of the grid and select only the most critical electric nodes for the incidence of lightning discharges, in the GA allocation procedure. The results obtained for the IEEE-123 bus electric feeder indicate a great reduction of flashover occurrence, thus increasing the equipment protection level.
•The methodology for compression of electrical power signals from waveform records uses GA and ANN.•The genetic algorithm is used to select and preserve the points that better characterize the ...waveform contours.•The ANN is used in the compression of the points not selected by GA and as well as on the signal reconstruction process.•The proposed methodology preserves a percentage of the original signal samples.
This paper proposes a methodology for compression of electrical power signals from waveform records in electric systems, using genetic algorithm (GA) and artificial neural network (ANN). The genetic algorithm is used to select and preserve the points that better characterize the waveform contours; and the artificial neural network is used in the compression of other points as well as on the signal reconstruction process. Thus, the data resulting from the proposed methodology are formed by a part of the original signal and by a compressed complementary part in the form of synaptic weights. The proposed methodology selects and preserves a percentage of the original signal samples, which are aspects not explored in the literature. The method was tested using field data obtained from an oscillographic recorder installed in a 230kV electrical power system. The results presented compression rates ranging from 8.59:1.00 to 24.16:1.00 for preservation rates ranging from 2.5% to 10%, respectively.
The optimization of economic emission load dispatch is one of the most significant tasks in power plants. This article aims to analyze a new application of the computational optimization by simulated ...annealing technique including turning off the motors with greatest losses. The incremental cost of fuel consumption and the lambda iteration methods are combined to determine the best parameters of active power of each
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generator unit, ensuring that the total losses and demand are equal to the total generated power but minimizing the total cost of fuel consumption and carbon emission. Many materials and methods have been elaborated to fix the economic emission load dispatch, among them are as follows: differential evolution method, gradient method and Newton’s method. The results found for this case study, with the new application of simulated annealing, were outstanding having a reduction of 20.14% in the total fuel cost, comparing to classical methods that distribute the generation of power among all motors, including the least efficient ones. This method helps the expert in the decision making of preventive maintenance of machines that are not working in the moment of multi-objective optimization, improving not only the yield of generation and carbon emission reduction but also of the power plant generation planning.
Several dynamic projects and fault diagnosis of mechanical structures require the knowledge of the acting external forces. However, the measurement of such forces is often difficult or even ...impossible; in such cases, an inverse problem must be solved. This paper proposes a force identification method that uses the response surface methodology (RSM) based on central composite design (CCD) in conjunction with a random forest regression algorithm. The procedure initially required the finite element modal model of the forced structure. Harmonic analyses were then performed with varied parameters of forces, and RSM generated a dataset containing the values of amplitude, frequency, location of forces, and vibration acceleration at several points of the structure. The dataset was used for training and testing a random forest regression model for the prediction of any location, amplitude, and frequency of the force to be identified with information on only the vibration acquisition at certain points of the structure. Numerical results showed excellent accuracy in identifying the force applied to the structure.
The main equipment responsible for connection and transmission of electric power from generating centers to consumers are power transformers. This type of equipment is subject to various types of ...faults that can affect its components, in some cases also compromising its operation and, consequently, the electric power supply. Thus, in this paper, electromagnetic, thermal, and structural analysis of power transformers was carried out with the objective of providing the operator with information on the ideal moment for performing predictive maintenance, avoiding unplanned shutdowns. For this, computational simulations were performed using the finite element method (FEM) and, from that, the different transformer operation ways, nominal currents, inrush current, and short-circuit current were analyzed. In this perspective, analyses of the effects that thermal expansion, axial forces, and radial forces exerted were carried out, contributing to possible defects in this type of equipment. As a study object, simulations were carried out on a 50 MVA single-phase transformer. It is important to emphasize that the simulations were validated with real data of measurements and with results presented in the current literature.
This paper proposes a new approach to fault diagnosis in electrical power systems, which presents an aspect little explored in the literature that is the protective device failure detection together ...with the fault section estimation, since the majority of the methodologies so far proposed to fault diagnosis are limited to the fault section estimation alone. The proposed methodology makes use of operation states of protective devices as well as information related to the protection philosophy. Initially, these data undergo a preprocessing step to convert the format of 0 and 1 to percentage values. The conversion to percentage values allows the use of artificial neural networks, whose numbers of inputs do not depend on the number of alarms of the protection philosophy, or the type of bus arrangement or the number of circuit breakers. This allows the same set of neural networks to be trained and applied in different power systems with different protection schemes and bus arrangements. The proposed system has five neural networks, each containing few neurons and requiring 30 μs to perform fault diagnosis. The proposed system was trained considering the IEEE 57-bus system, containing different selective protection schemes, and subsequently tested in the IEEE 14-bus, 30-bus, and 118-bus systems, and Eletronorte 230-kV real power system.
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