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

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

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

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

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

Data Mining Applied to Decision Support Systems for Power Transformers’ Health Diagnostics

This manuscript addresses the problem of technical state assessment of power transformers based on data preprocessing and machine learning. The initial dataset contains diagnostics results of the power transformers, which were collected from a variety of different data sources. It leads to dramatic degradation of the quality of the initial dataset, due to a substantial number of missing values. The problems of such real-life datasets are considered together with the performed efforts to find a balance between data quality and quantity. A data preprocessing method is proposed as a two-iteration data mining technology with simultaneous visualization of objects’ observability in a form of an image of the dataset represented by a data area diagram. The visualization improves the decision-making quality in the course of the data preprocessing procedure. On the dataset collected by the authors, the two-iteration data preprocessing technology increased the dataset filling degree from 75% to 94%, thus the number of gaps that had to be filled in with the synthetic values was reduced by 2.5 times. The processed dataset was used to build machine-learning models for power transformers’ technical state classification. A comparative analysis of different machine learning models was carried out. The outperforming efficiency of ensembles of decision trees was validated for the fleet of high-voltage power equipment taken under consideration. The resulting classification-quality metric, namely, F1-score, was estimated to be 83%. © 2022 by the authors.Ministry of Education and Science of the Russian Federation, MinobrnaukaThe research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority-2030 Program) is gratefully acknowledged

Повышение точности прогнозирования генерации фотоэлектрических станций на основе алгоритмов k-средних и k-ближайших соседей

Renewable energy sources (RES) are seen as a means of the fuel and energy complex carbon footprint reduction but the stochastic nature of generation complicates RES integration with electric power systems. Therefore, it is necessary to develop and improve methods for forecasting of the power plants generation using the energy of the sun, wind and water flows. One of the ways to improve the accuracy of forecast models is a deep analysis of meteorological conditions as the main factor affecting the power generation. In this paper, a method for adapting of forecast models to the meteorological conditions of photovoltaic stations operation based on machine learning algorithms was proposed and studied. In this case, unsupervised learning is first performed using the k-means method to form clusters. For this, it is also proposed to use studied the feature space dimensionality reduction algorithm to visualize and estimate the clustering accuracy. Then, for each cluster, its own machine learning model was trained for generation forecasting and the k-nearest neighbours algorithm was built to attribute the current conditions at the model operation stage to one of the formed clusters. The study was conducted on hourly meteorological data for the period from 1985 to 2021. A feature of the approach is the clustering of weather conditions on hourly rather than daily intervals. As a result, the mean absolute percentage error of forecasting is reduced significantly, depending on the prediction model used. For the best case, the error in forecasting of a photovoltaic plant generation an hour ahead was 9 %.Возобновляемые источники энергии рассматриваются как средство снижения углеродного следа топливно-энергетического комплекса, при этом стохастический характер генерации осложняет их интеграцию с электроэнергетическими системами. Эта существенная трудность обусловливает необходимость создавать и совершенствовать методы прогнозирования генерации электрических станций, использующих энергию солнца, ветра и водных потоков. Наиболее важным направлением, обеспечивающим повышение точности прогнозных моделей, является глубокий анализ метеорологических условий как главного фактора, влияющего на выработку электроэнергии. В данной работе предложен и исследован метод адаптации прогнозных моделей под метеорологические условия работы фотоэлектрических станций на базе алгоритмов машинного обучения. При этом вначале выполняется обучение без учителя методом k-средних для формирования кластеров. Для этой задачи также предложено и исследовано использование алгоритма понижения размерности пространства признаков для визуализации оценки точности кластеризации. Затем для каждого кластера построена своя модель машинного обучения для формирования прогнозов и алгоритм k-ближайших соседей для отнесения текущих условий на этапе эксплуатации модели к одному из сформированных кластеров. Исследование было проведено на почасовых метеорологических данных за период с 1985 по 2021 г. Одной из особенностей этого подхода является кластеризация метеоусловий на часовых, а не суточных интервалах. В результате средний модуль относительной ошибки прогнозирования существенно снижается в зависимости от используемой модели прогнозирования. Для наилучшего варианта ошибка прогнозирования генерации фотоэлектрической станции на час вперед составила 9 %

Оперативное прогнозирование скорости ветра для автономной энергетической установки тяговой железнодорожной подстанции

Currently, the prospects of creating hybrid power assemblies using renewable energy sources, including wind energy, and energy storage systems based on hydrogen energy technologies are being considered. To control such an energy storage system, it is necessary to perform operational renewable sources generation forecasting, particularly forecasting of wind power assemblies. Their production depends on the speed and direction of the wind. The article presents the results of solving the problem of operational forecasting of wind speed for a hybrid power assembly project aimed at increasing the capacity of the railway section between Yaya and Izhmorskaya stations (Kemerovo region of the Russian Federation). Hourly data of wind speeds and directions for 15 years have been analyzed, a neural network model has been built, and a compact architecture of a multilayer perceptron has been proposed for short-term forecasting of wind speed and direction for 1 and 6 hours ahead. The model that has been developed allows minimizing the risks of overfitting and loss of forecasting accuracy due to changes in the operating conditions of the model over time. A specific feature of this work is the stability investigation of the model trained on the data of long-term observations to long-term changes, as well as the analysis of the possibilities of improving the accuracy of forecasting due to regular further training of the model on newly available data. The nature of the influence of the size of the training sample and the self-adaptation of the model on the accuracy of forecasting and the stability of its work on the horizon of several years has been established. It is shown that in order to ensure high accuracy and stability of the neural network model of wind speed forecasting, long-term meteorological observations data are required.В настоящее время рассматриваются перспективы создания гибридных энергетических установок с использованием возобновляемых источников энергии, в том числе энергии ветра, и систем накопления энергии на базе технологий водородной энергетики. Для управления такой системой накопления энергии необходимо оперативное прогнозирование генерации от возобновляемых источников, в частности ветровых энергетических установок. Их выработка зависит от скорости и направления ветра. В статье представлены результаты решения задачи оперативного прогнозирования скорости ветра для проекта гибридной энергетической установки, направленной на повышение пропускной способности железнодорожного участка между станциями Яя и Ижморская (Кемеровская область Российской Федерации). Проанализированы почасовые данные скоростей и направлений ветра за 15 лет, построена нейросетевая модель и предложена компактная архитектура многослойного перцептрона для краткосрочного прогнозирования скорости и направления ветра на 1 и 6 ч вперед. Разработанная модель позволяет минимизировать риски переобучения и потери точности прогнозирования из-за изменения условий работы модели со временем. Особенность данной статьи заключается в исследовании устойчивости модели, обученной на данных многолетних наблюдений, к долгосрочным изменениям, а также анализе возможностей повышения точности прогнозирования за счет регулярного дообучения модели на вновь поступающих данных. Установлен характер влияния размера обучающей выборки и самоадаптации модели на точность прогнозирования и устойчивость ее работы на горизонте в несколько лет. Показано, что для обеспечения высокой точности и устойчивости нейросетевой модели прогнозирования скорости ветра необходимы данные многолетних метеорологических наблюдений

Оптимизация топологии сети с ВИЭ-генерацией на основе модифицированного адаптированного генетического алгоритма

The article presents an adaptive genetic algorithm developed by the authors, which makes it possible to optimize the topology of a power network with distributed generation. The optimization was based on bioinspired methods. The objects of the study were a 15-node circuit of a power net-work with photovoltaic stations and a 14-node IEEE augmented circuit with distributed generation sources (three wind farms and two photovoltaic plants). The simulation of the modes of electric power systems was performed using the Pandapower library for the Python programming language, which is in the public domain. Three types of electric load of consumers were considered, reflecting the natures of electricity consumption in the nodes of real electric power systems, the results of numerical studies were presented. The proposed genetic algorithm used two different functions of interbreeding, the function of mutation, selection of the best individuals and mass mutation (complete population renewal). At the end of each iteration of the algorithm operation, statistical dependencies were de-rived that characterized its work: the best (minimal losses) and average adaptability in the population, a list of the best individuals throughout all iterations, etc. The verification was carried out in comparison with the results obtained by a complete search of possible radial configurations of the system, and it showed that the developed genetic algorithm had fast convergence, high accuracy and was able to work correctly with different configurations of electrical circuits, generation and load structures. The algorithm can be used in conjunction with renewable energy sources generation forecasting systems for the day ahead when planning the operating modes of power units in order to minimize the costs of covering electricity losses and improve the quality of electricity supplied.В статье представлен разработанный авторами адаптивный генетический алгоритм, позволяющий оптимизировать топологию электрической сети с распределенной генерацией на основе биоинспирированных методов. Объекты исследования – 15-узловая схема электрической сети с фотоэлектрическими станциями и 14-узловая дополненная схема IEEE с источниками распределенной генерации (три ветровые и две фотоэлектрические станции). Моделирование режимов электроэнергетических систем выполнено с использованием находящейся в открытом доступе библиотеки Pandapower для языка программирования Python. Рассмотрены три типа электрической нагрузки потребителей, отражающие характер потребления электроэнергии в узлах реальных электроэнергетических систем, приведены результаты численных исследований. В предложенном генетическом алгоритме применены две различные функции скрещивания, функции мутации, отбора лучших индивидов и массовой мутации (полного обновления популяции). В конце каждой итерации работы алгоритма выводятся статистические зависимости, характеризующие его работу: лучшая (минимальные потери) и средняя приспособленность в популяции, список лучших индивидов на протяжении всех итераций и т. д. Верификация производилась в сравнении с результатами, полученными методом полного перебора возможных радиальных конфигураций системы, и показала, что разработанный генетический алгоритм обладает быстрой сходимостью, высокой точностью и способен корректно работать при различных конфигурациях схем электрических сетей, структурах генерации и нагрузки. Алгоритм может применяться совместно с системами прогнозирования ВИЭ-генерации на сутки вперед при планировании режимов работы энергообъединений с целью минимизации издержек на покрытие потерь электроэнергии и улучшения качества отпускаемой электроэнергии