Swarm intelligence algorithms for the problem of the optimal placement and operation control of reactive power sources into power grids

Deep reactive power compensation allows for reduction of active power losses in transmission lines of power supply systems. The efficiency of the compensation depends on the allocation of reactive power compensation units (RPCUs) at the nodes of a network. In general, investigations devoted to the study of optimal allocation of the compensation units have revealed that it is a static and deterministic optimization problem that can be solved by heuristic methods. However, in real systems, it is reasonable to consider such optimization problems, taking into account the dynamic and stochastic properties of the problems. These properties are the result of equipment failures and operational changes in technical systems. In addition, optimizing the allocation of the compensation units is the NP-hard multifactor problem. Under these circumstances, it is advisable to use the swarm intelligence algorithms. Swarm intelligence is a relatively new approach to solving the optimization problem, which takes inspiration from the behaviour of ants, birds, and other animals. Advantages of swarm algorithms are most evident if problems involve the dynamic or stochastic nature of the objective function and constraints. Contrary to a number of similar studies, this research considers the problem of the optimal allocation of compensation units as a dynamic problem, taking into account the possible random failures of the compensation equipment. The optimization problem has been solved by two Swarm Intelligence algorithms (the Particle Swarm optimization and the Artificial Bee Colony optimization) and Genetic algorithms. It has been aimed at comparing the effectiveness of the algorithms for solving such problems. It was found that swarm algorithms could be successfully applied in the operation control of compensation units in real-time. © 2017 WIT Press

Implementation of Population Algorithms to Minimize Power Losses and Cable Cross-Section in Power Supply System

The article dues to the arrangement of the reactive power sources in the power grid to reduce the active power losses in transmission lines and minimize cable cross-sections of the lines. The optimal arrangement is considered from two points of view. In the first case, it is possible to minimize the active power losses only. In the second case, it is possible to change the cross-sections of the supply lines to minimize both the active power losses and the volume of the cable lines. The sum of the financial cost of the active power losses, the capital investment to install the deep reactive power compensation, and cost of the cable volume is introduced as the single optimization criterion. To reduce the losses, the deep compensation of reactive power sources in nodes of the grid are proposed. This optimization problem was solved by the Genetic algorithm and the Particle Swarm optimization algorithm. It was found out that the deep compensation allows minimizing active power losses the cable cross-section. The cost-effectiveness of the suggested method is shown. It was found out that optimal allocation of the reactive power sources allows increasing from 9% to 20% the financial expenses for the enterprise considered

Computational and experimental study of air-core HTS transformer electrothermal behaviour at current limiting mode

The paper provides the results of the experimental and computational study of the processes occurring in high temperature superconducting transformer windings while secondary winding is short-circuited. The obtained mathematical simulation matches closely with the experimental results. The temperature variation curves for superconducting windings were analysed, and conclusions were made on the necessity of changes in HTS transformer design, namely the necessity of windings heat-insulation from each other and adding a high-resistance coating material for HTS wire in HTS transformer primary winding

Application of swarm intelligence algorithms to energy management of prosumers with wind power plants

The paper considers the problem of optimal control of a prosumer with a wind power plant in smart grid. It is shown that control can be performed in non-deterministic conditions due to the impossibility of accurate forecasting of the generation from renewable plants. A control model based on a priority queue of logical rules with structural-parametric optimization is applied. The optimization problem is considered from a separate prosumer, not from the entire distributed system. The solution of the optimization problem is performed by three swarm intelligence algorithms. Computational experiments were carried out for models of wind energy systems on Russky Island and Popov Island (Far East). The results obtained showed the high effectiveness of the swarm intelligence algorithms that demonstrated reliable and fast convergence to the global extreme of the optimization problem under different scenarios and parameters of prosumers. Also, we analyzed the influence of accumulator capacity on the variability of prosumers. The variability, in turn, affects the increase of the prosumer benefits from the interaction with the external global power system and neighboring prosumers

Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units

The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out

Multi-criteria analysis of fuzzy symptoms of electrical faults in power systems

The paper considers a possible method of technical diagnostics of electrical equipment of power supply systems and electrical substations based on the fuzzy sets and fuzzy logic. It is shown that, based on the matrix of fuzzy relationships, one can make a plausible enough prediction about possible malfunctions and causes of failures. The prerequisites for this analysis are the current condition (state) of the electrical equipment and expert assessments of diagnostic signs. The paper shows the comparison made using the features scale of Saaty, in accordance with nine degrees of preference. At the core of fuzzy expert assessments is an attempt to formalize linguistic information, namely linguistic variables whose meanings can be words or phrases. The paper presents a complete range of preconditioned defects consisting of m factors and their corresponding space conclusions as to the causes of these malfunctions (defects) of n symptoms. Fuzzy causal relations in the space of underlying factors are established between the assumptions and conclusions of the experts. The resulting system of equations is solved by the method based on the composition of fuzzy conclusions. Possible failures are ranked according to the experts’ preference, which reveals the most significant symptoms of malfunctioning and allows arriving at the conclusion as to the future operation of the facility. The validity of the provisions of the method presented is confirmed by appropriate calculations, which demonstrates the correct behavior of the model concerning the transformer equipment. It is shown that in case of the fuzzy symptoms occurrence and evaluation of these features by a scale of preferences, it is possible to conclude about the further operation of electrical equipment or its withdrawal for repair. Thus, the mathematical model based on the fuzzy relations of symptoms selected using the experts’ estimations contains elements of predicting the possible failures of power systems electrical equipment

Diagnostics of the technical condition of electric network equipment based on fuzzy expert estimates

The paper describes a new possible method of diagnostics of the current technical condition of equipment using a mathematical model based on fuzzy expert estimates and the theory of fuzzy sets. The specifics of the task is determined mainly by the type of the obtained estimates, namely: causal relationships between the controlled parameters of the transformer equipment and defects that could entail their change and the possibility of further operation of the facility. At the same time, attention is paid to the problem of the degree of consistency of expert opinions that affects the quality of the assessment of the current technical condition of the studied object. The paper provides a comparative analysis of the arithmetic mean estimates and median estimates of the consistency of expert opinions. It is shown that the significant drawback of the arithmetic mean approach is its instability towards outliers of individual opinions moving the resulting value under the influence of the “dissident expert opinions”. On the other hand, the median estimate is free of such shortage; it is more outlier-resistant and simply discards a part of radically outlying expert opinions. For the first time, the Kemeny median has been used for technical diagnostics. Kemeny median is based on the introduction of a metric to the set of expert opinions, and axiomatic introduction of the distance between them. Also, the paper formulates a criterion on how to determine the optimal number of experts in the group. © 202

Alzheimer’s Disease Studies in the Tex-Mex Border: Dissecting a Complex Multifactorial Problem

Purpose: Alzheimer’s Disease (ALZ) is the leading cause of dementia in the aging population, and Latinos have \u3e3 times higher risk to develop dementia than the overall US population. Although several studies have examined for possible causes of this increased risk, lack of comprehensive information plus a reduced number of Latino samples available in each study have hindered the answers. Description: The University of Texas Rio Grande Valley has joined two large studies looking for multiple biomarkers associated with ALZ: The South Texas Alzheimer’s Center Clinical Data Repository and Biobank (STAC) and the Texas Alzheimer’s Research and Care Consortium (TARCC). We are now collecting clinical data along with neuroimaging and lab biomarkers from each individual enrolled in these studies, with the aim to enroll a large majority of Latinos in our site sample, which will help to elucidate the differences and risk factors inherent to our population in the border. We are also analyzing data from different Latin-American studies to study specific genetic risks, environmental factors, and their interactions. Partners: UTRGV has partnered with UTHSCSA for the STAC study and with many other academic research institutions at TARCC. We aim to provide experiences of clinical training to our psychology students and residents of medical specialties, as well as analysis opportunities and opening postdoctoral positions related to the development of this field at UTRGV. Looking Ahead: We expect to generate substantial contributions to the knowledge of cognitive decline in underserved populations, which can lead to improved treatments and better clinical care. Postdoctoral positions will be opening soon at the Institute of Neuroscience

Improving accuracy and generalization performance of small-size recurrent neural networks applied to short-term load forecasting

The load forecasting of a coal mining enterprise is a complicated problem due to the irregular technological process of mining. It is necessary to apply models that can distinguish both cyclic components and complex rules in the energy consumption data that reflect the highly volatile technological process. For such tasks, Artificial Neural Networks demonstrate advanced performance. In recent years, the effectiveness of Artificial Neural Networks has been significantly improved thanks to new state-of-the-art architectures, training methods and approaches to reduce overfitting. In this paper, the Recurrent Neural Network architecture with a small-size model was applied to the short-term load forecasting of a coal mining enterprise. A single recurrent model was developed and trained for the entire four-year operational period of the enterprise, with significant changes in the energy consumption pattern during the period. This task was challenging since it required high-level generalization performance from the model. It was shown that the accuracy and generalization properties of small-size recurrent models can be significantly improved by the proper selection of the hyper-parameters and training method. The effectiveness of the proposed approach was validated using a real-case dataset. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Optimal Management of Energy Consumption in an Autonomous Power System Considering Alternative Energy Sources

This work aims to analyze and manage the optimal power consumption of the autonomous power system within the Pamir region of Republic of Tajikistan, based on renewable energy sources. The task is solved through linear programming methods, production rules and mathematical modeling of power consumption modes by generating consumers. It is assumed that power consumers in the considered region have an opportunity to independently cover energy shortage by installing additional generating energy sources. The objective function is to minimize the financial expenses for own power consumption, and to maximize them from both the export and redistribution of power flows. In this study, the optimal ratio of power generation by alternative sources from daily power consumption for winter was established to be hydroelectric power plants (94.8%), wind power plant (3.8%), solar photovoltaic power plant (0.5%) and energy storage (0.8%); while it is not required in summer due to the ability to ensure the balance of energy by hydroelectric power plants. As a result, each generating consumer can independently minimize their power consumption and maximize profit from the energy exchange with other consumers, depending on the selected energy sources, thus becoming a good example of carbon-free energy usage at the micro-and mini-grid level. © 2022 by the authors. Licensee MDPI, Basel, Switzerland