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Accurate load forecasting is essential for the efficient and reliable operation of power systems. Traditional models primarily utilize unidirectional data reading, capturing dependencies from past to future. This paper proposes a novel approach that enhances load forecasting accuracy by fine tuning an attention-based model with a bidirectional reading of time-series data. By incorporating both forward and backward temporal dependencies, the model gains a more comprehensive understanding of consumption patterns, leading to improved performance. We present a mathematical framework supporting this approach, demonstrating its potential to reduce forecasting errors and improve robustness. Experimental results on real-world load datasets indicate that our bidirectional model outperforms state-of-the-art conventional unidirectional models, providing a more reliable tool for short and medium-term load forecasting. This research highlights the importance of bidirectional context in time-series forecasting and its practical implications for grid stability, economic efficiency, and resource planning. [ABSTRACT FROM AUTHOR]

The issues of optimizing the operation of micro hydropower plants in conditions of water scarcity, performed by additional connection to the grid of an energy storage system and wind power turbine, as well as optimal load management, are considered. It is assumed that the load of the system is a concentrated autonomous power facility that consumes only active power. The paper presents a rigorous mathematical formulation of the problem, the solution of which corresponds to the minimum cost of an energy storage system and a wind turbine, which allows for uninterrupted supply of electricity to power facilities in conditions of water shortage necessary for the operation of micro hydropower plants (under unfavorable hydrological conditions). The problem is formulated as a nonlinear multi-objective optimization problem to apply metaheuristic stochastic algorithms. At the same time, a significant part of the problem is taken out and framed as a subproblem of linear programming which will make it possible to solve it by a deterministic simplex method that guarantees to find the exact global optimum. This approach will significantly increase the efficiency of solving the entire problem by combining metaheuristic algorithms and taking into account expert knowledge about the problem being solved. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Modern electrical power systems place special demands on the speed and accuracy of transient and steady-state process control. The introduction of renewable energy sources has significantly influenced the amount of inertia and uncertainty of transient processes occurring in energy systems. These changes have led to the need to clarify the existing principles for the implementation of devices for protecting power systems from the loss of small-signal and transient stability. Traditional methods of developing these devices do not provide the required adaptability due to the need to specify a list of accidents to be considered. Therefore, there is a clear need to develop fundamentally new devices for the emergency control of power system modes based on adaptive algorithms. This work proposes to develop emergency control methods based on the use of deep machine learning algorithms and obtained data from synchronized vector measurement devices. This approach makes it possible to ensure adaptability and high performance when choosing control actions. Recurrent neural networks, long short-term memory networks, restricted Boltzmann machines, and self-organizing maps were selected as deep learning algorithms. Testing was performed by using IEEE14, IEEE24, and IEEE39 power system models. Two data samples were considered: with and without data from synchronized vector measurement devices. The highest accuracy of classification of the control actions' value corresponds to the long short-term memory networks algorithm: the value of the accuracy factor was 94.31% without taking into account the data from the synchronized vector measurement devices and 94.45% when considering this data. The obtained results confirm the possibility of using deep learning algorithms to build an adaptive emergency control system for power systems. [ABSTRACT FROM AUTHOR]

Nontechnical losses of electrical energy (NTLEE) have been a persistent issue in both the Russian and global electric power industries since the end of the 20th century. Every year, these losses result in tens of billions of dollars in damages. Promptly identifying unscrupulous consumers can prevent the onset of NTLEE sources, substantially reduce the amount of NTLEE and economic damages to network grids, and generally improve the economic climate. The contemporary advancements in machine learning and artificial intelligence facilitate the identification of NTLEE sources through anomaly detection in energy consumption data. This article aims to analyze the current efficacy of computational methods in locating, detecting, and identifying nontechnical losses and their origins, highlighting the application of neural network technologies. Our research indicates that nearly half of the recent studies on identifying NTLEE sources (41%) employ neural networks. The most utilized tools are convolutional networks and autoencoders, the latter being recognized for their high-speed performance. This paper discusses the main metrics and criteria for assessing the effectiveness of NTLEE identification utilized in training and testing phases. Additionally, it explores the sources of initial data, their composition, and their impact on the outcomes of various algorithms. [ABSTRACT FROM AUTHOR]

Forecasting electricity consumption is currently one of the most important scientific and practical tasks in the field of electric power industry. The early retrieval of data on expected load profiles makes it possible to choose the optimal operating mode of the system. The resultant forecast accuracy significantly affects the performance of the entire electrical complex and the operating conditions of the electricity market. This can be achieved through using a model of total electricity consumption designed with an acceptable margin of error. This paper proposes a new method for predicting power consumption in all nodes of the power system through the determination of rank coefficients calculated directly for the corresponding voltage level, including node substations, power supply zones, and other parts of the power system. The forecast of the daily load schedule and the construction of a power consumption model was based on the example of nodes in the central power system in Mongolia. An ensemble of decision trees was applied to construct a daily load schedule and rank coefficients were used to simulate consumption in the nodes. Initial data were obtained from daily load schedules, meteorological factors, and calendar features of the central power system, which accounts for the majority of energy consumption and generation in Mongolia. The study period was 2019–2021. The daily load schedules of the power system were constructed using machine learning with a probability of 1.25%. The proposed rank analysis for power system zones increases the forecasting accuracy for each zone and can improve the quality of management and create more favorable conditions for the development of distributed generation. [ABSTRACT FROM AUTHOR]

Solar energy is an unlimited and sustainable energy source that holds great importance during the global shift towards environmentally friendly energy production. However, integrating solar power into electrical grids is challenging due to significant fluctuations in its generation. This research aims to develop a model for predicting solar radiation levels using a hybrid power system in the Gorno-Badakhshan Autonomous Oblast of Tajikistan. This study determined the optimal hyperparameters of a multilayer perceptron neural network to enhance the accuracy of solar radiation forecasting. These hyperparameters included the number of neurons, learning algorithm, learning rate, and activation functions. Since there are numerous combinations of hyperparameters, the neural network training process needed to be repeated multiple times. Therefore, a control algorithm of the learning process was proposed to identify stagnation or the emergence of erroneous correlations during model training. The results reveal that different seasons require different hyperparameter values, emphasizing the need for the meticulous tuning of machine learning models and the creation of multiple models for varying conditions. The absolute percentage error of the achieved mean for one-hour-ahead forecasting ranges from 0.6% to 1.7%, indicating a high accuracy compared to the current state-of-the-art practices in this field. The error for one-day-ahead forecasting is between 2.6% and 7.2%. [ABSTRACT FROM AUTHOR]

The development of modern power systems is directly related to changes in the traditional principles of management, planning, and monitoring of electrical modes. The mass introduction of renewable energy sources and control devices based on power electronics components contributes to changing the nature of the flow of transient and quasi-established electrical modes. In this area, the problem arises of conducting a more accurate and rapid assessment of the parameters of the electrical regime using synchronized vector measurement devices. The paper presents an extensive meta-analysis of the modern applications of phasor measurement units (PMUs) for monitoring, emergency management and protection of power systems. As a result, promising research directions, the advantages and disadvantages of the existing approaches to emergency management, condition assessment, and relay protection based on PMUs are identified. [ABSTRACT FROM AUTHOR]

Large-scale development of small-scale generation, and facilities based on this, with their integration into existing distribution networks, qualitatively change the modes and tasks of network management and transform previously passive electric networks into active ones. Features of parameters and modes of small-scale generation, insufficient observability and manageability in centralized management determine the need to use a decentralized multi-agent control of the modes of such networks. First of all, this applies to emergency management, which includes automatic restoration of the integrity and normal operation of the network. This paper presents a set of specialized methods for emergency management of active power grid modes and the results of a study of their effectiveness on mathematical and physical models that confirm the feasibility of using decentralized emergency management and network recovery management. In particular, this includes: a method of emergency proactively balanced separation of grid energy districts along one of the a priori fixed network cross-sections in the event of disturbances with the transition to island mode, and a method for two-stage restoration of the integrity and normal network mode with decentralized synchronization of active parts on remote network switches. In the case of the decentralized remote synchronization of active parts, it is proposed to use special control of the excitation and speed of generators to create conditions for the successful operation of automatic reclosing devices with synchronization detection. It is essential for emergency management in active networks with small generation to reject the concept of ensuring the reliability of power supply through maintaining the integrity of the network in favor of the concept of an emergency-balanced breakdown of the network into balanced areas with the subsequent automatic restoration of integrity. To conduct research on the physical model, a prototype of distributed system emergency automation has been developed that does not use data transmission tools, which ensures its high cybersecurity and the feasibility of decentralized management. [ABSTRACT FROM AUTHOR]

The article is devoted to solving the problem of managing the mode parameters of an urban electrical network in case of a discrepancy between the actual electrical load and the specific load. Such an issue leads to a deviation of the parameters, in particular, voltage asymmetry in phases due to current asymmetry. To optimize the mode parameters, it is required that the effective value of the electrical load corresponds as much as possible to the values of the specific electrical load. This depends on the following: actual power consumption, external (climatic and meteorological) factors, internal factors (structural design of residential buildings, uneven load when distributed over the phases of three-phase lines and inputs, different number of electrical receivers for consumers), and the provision of consumers with other sources of energy (both gas and heat supply, and hot water supply). To establish the influencing factors on the actual power consumption, it is proposed to generalize the uncertainty accounting coefficient which generalizes both more well-known and less considered factors. Therefore, the authors propose models for determining the electrical loads based on the possibility of assessing the mode parameters of the electrical network by electrical loads. The accuracy of the proposed models is based on the use of the proposed forecasting method considering the actual power consumption and the generalized uncertainty coefficient. Applying the obtained data based on models of electrical loads to the constructed model of a part of a distribution electrical network with real parameters of the electrical network in the MathWorks Simulink environment, the correspondence to the mode parameters of the distribution electrical network is determined. As a result, a device for balancing the voltage depending on the load asymmetry is proposed that is related to the discrepancy between the mode parameters allowing control of the mode parameters by bringing them to acceptable values. [ABSTRACT FROM AUTHOR]

Seasonal fluctuations in electricity consumption, and uneven loading of supply lines reduce not only the energy efficiency of networks, but also contribute to a decrease in the service life of elements of power supply systems. To solve the problem of forecasting power consumption, it is proposed to use the theory of fuzzy sets to assess the effective development of the energy system of the Republic of Tajikistan. According to the statistical data of power consumption for the previous period, a fuzzy logic model with membership functions is proposed, which makes it possible to evaluate consumer satisfaction using the criteria unsatisfactory, satisfactory, conditionally satisfactory, and satisfactory, as well as the efficiency of the consumption mode of compliance using the criteria high, medium, and low, allowing the evaluation of the efficiency plan for the development of the energy system of the Republic of Tajikistan. To obtain and set more accurate data on electricity consumption, calculations were made for the winter period of the year. Based on the proposed calculation model of fuzzy logic, a quantitative component of electricity consumption, the corresponding satisfaction of the consumer, and the efficiency of the regime for nine cities of the Republic of Tajikistan were proposed in the form of diagrams of seasonal electricity consumption. The obtained seasonal power consumption makes it possible to improve the accuracy of estimating power consumption, thereby equalizing the balance of consumption and generation. [ABSTRACT FROM AUTHOR]

At present, diagnostics of the current technical condition of high-voltage power equipment in power systems have become more important. This allows the estimation of the real technical condition of power equipment more accurately with its removal into repair based on the results of the diagnostics. This paper presents the comparative analysis of expert evaluations with the use of the arithmetical mean and median values of expert evaluations. In this case, individual expert opinions, influenced by a level of competence, correspond to each other in a different manner, depending on the applied approach. As the comparison of the consistency of expert opinions is the basis for decision-making, it is recommended to make a decision on the technical condition using median estimations because these estimations are less subjected to distortions from single outliers of judgments. This provides more reliable information for making key decisions. Three approaches are considered in this paper: the method of arithmetical mean estimations, the method of median estimations based on the Kemeny median method, and the analytic hierarchy process of Saaty. The considered methods allow decisions on power equipment operation to be made very quickly; namely, if the power equipment is in an operable state and may remain operated, or it has considerable defects and should be removed from operation for routine maintenance, or it has reached the final technical state and needs to be removed from operation. [ABSTRACT FROM AUTHOR]

Predicting the variability of wind energy resources at different time scales is extremely important for effective energy management. The need to obtain the most accurate forecast of wind speed due to its high degree of volatility is particularly acute since this can significantly improve the planning of wind energy production, reduce costs and improve the use of resources. In this study, a method for predicting the speed of wind flow in an isolated power system of the Gorno-Badakhshan Autonomous Oblast (GBAO), based on the use of a neural network with a learning process control algorithm, is proposed. Predicting is performed for four seasons of the year, based on hourly retrospective meteorological data of wind speed observations. The obtained wind speed average error forecasting ranged from 20–28% for a day ahead. The prediction results serve as a basis for optimizing the energy consumption of individual generating consumers to minimize their financial and technical costs. In addition, this study takes into account the possibility of exporting electricity to a neighboring country as an additional income line for the isolated GBAO power system during periods of excess energy from hydropower plants (March–September), which is a systematic vision of solving the problem of improving energy efficiency in the conditions of autonomous power supply. [ABSTRACT FROM AUTHOR]

Stochastic renewable sources of energy have been causing changes in the structure and operation of power systems. High penetration of this type of generation results in decreased inertia of a power system, increased active power fluctuations, and a higher probability of false tripping of emergency control devices. Traditional algorithms of emergency control are not adaptable and flexible enough for systems with high penetration of renewables and flexible alternating current transmission systems. Integration and development of phasor measurement units make it possible to create adaptable emergency control systems, which would require minimal pre‐defined data. The purpose of this study is to develop an adaptable algorithm for turbine fast valving control synthesis and transient stability estimation for a generator. The suggested algorithm is based on the equal area criterion in the domain synchronous generator "torque–load angle". The measurements of the generator operation under consideration are used as the input data for the steam turbine fast valving control synthesis. Thus, the algorithm becomes adaptable because no pre‐defined parameters of a power system model are required. The suggested algorithm was tested on the power system model NE39bus using Matlab/Simulink. The efficiency of the suggested algorithm is verified and demonstrated. [ABSTRACT FROM AUTHOR]

Currently, energy saving has become an acute problem all over the world. Due to the rapid development of both the energy and information technology sectors, as well as an increase in the electricity demand, the electric distribution system is facing problems caused by stricter requirements for electricity quality, reliability, efficiency, and sustainability. Therefore, the use of energy-saving technologies, both the improvement of existing ones and the application of new ones, is one of the main reserves for electricity saving in power supply systems. This study is devoted to the evaluation of the operating modes of transformer substations of the 6–10 kV electric distribution network in Dushanbe city, based on the results of control winter measurements and the dependence of the low coefficient of active power in transformer substations. It has been observed that, with a low coefficient of active power in transformer substations, when transmitting electricity from substation busbars to transformer substations of a final consumer via overhead transmission lines (an average length of 5 km), the voltage loss exceeds the maximum permissible values. When transmitting electricity via cable lines, the voltage losses are within the limit of 5%. However, a low active power factor may be the reason for an increase in capacitive currents, followed by the command of single-phase earth fault currents. Due to the low active power factor, the increase in voltage losses and useful power losses leads to the inefficient operating modes of the transformer substations. [ABSTRACT FROM AUTHOR]

The results of testing the algorithms of the adaptive model of a synchronous generator using theoretical and real physical data are presented in this study. The adaptive model of a synchronous machine is an equations system, which describes both the static and transient operation of a generator. Parameters of the adaptive model are found using measurements of a generator's operational parameters. The single-machine model was created in Matlab/Simulink software to test the theoretical data. This single-machine model consists of a synchronous generator, a step-up transformer, and a transmission line. The test model also includes models of the automatic voltage regulator and steam turbine governor. The real electrodynamic model was used to verify the adaptive model of a synchronous machine. It consisted of four synchronous generators, with values of power capacity of 5 kW and 15 kW. The data logger with a sampling rate of 57.8 kHz was developed and installed to measure the operating parameters of each generator. As a result of testing on both models, the following values were estimated: inertia moment, d-axis and q-axis reactance, and load angle. These values were compared with the reference values. The adaptive model of a synchronous machine can be used in systems of emergency control and assessment of generator state. [ABSTRACT FROM AUTHOR]

The study examines the development and testing of algorithms for disturbance inception time estimation in a power system using instantaneous values of current and voltage with a high sampling rate. The algorithms were tested on both modeled and physical data. The error of signal extremum forecast, the error of signal form forecast, and the signal value at the so-called joint point provided the basis for the suggested algorithms. The method of tuning for each algorithm was described. The time delay and accuracy of the algorithms were evaluated with varying tuning parameters. The algorithms were tested on the two-machine model of a power system in Matlab/Simulink. Signals from emergency event recorders installed on real power facilities were used in testing procedures. The results of this study indicated a possible and promising application of the suggested methods in the emergency control of power systems. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, demand-side management (DSM) covers a whole range of technological and policy measures aimed at reducing electricity consumption connected with economic activities. Thus, the development of wind PV and other renewable energy technologies, combined with microgrid technology, offers the remote consumers and prosumers ample opportunities to stabilize long-term costs and increase local energy system security. Apart from that, DSM from microgrid based on renewable sources also has certain social benefits, such as protection of the environment and conservation of natural resources. Due to the advances in photovoltaic material research and solar panel price reduction over the last years, the usage of this alternative energy source in Baltic region countries seems more attractive. The usage of energy storage devices can help use the solar power more efficiently and smarter. This paper deals with the optimization of a proposed solar panel array of a renovated office building's communal lighting in Riga, using storage devices and demand-side management of the produced power, looking into a way to calculate the needed storage capacity on the basis of potential PV system and existing power consumption for communal space lighting system. The proposed approach will become one of the first basic steps in applying DSM to help reduce the communal space's illumination power consumption, in turn helping to reduce the needed PV generating power and energy storage. [ABSTRACT FROM AUTHOR]

Copyright of Power & Electrical Engineering is the property of RTU Publishing House and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Currently, active networks called microgrids are formed on the basis of local power supply systems with a small share of distributed generation. Microgrids operating in an island mode, in some cases, have the ability to transfer electricity excess to an external network leading to a synchronization requirement; thus, the optimization task in terms of the system's synchronization must be considered. This paper proposes a method for obtaining synchronization between microgrids and power systems of limited capacity based on a passive synchronization algorithm, allowing us to connect a microgrid to an external power system with a minimum impact moment on the shaft of the generating equipment. The algorithm application was demonstrated by considering a real-life object in Tajikistan. The simulation was carried out on RastrWin3. The obtained results show that the microgrid generator is connected to an external power system at an angle of 0.3° and a power surge of 29 kW, unlike the classical synchronization algorithm with an angle of 6.8° and a power surge of 154 kW (a reduction of the shock moment by more than five times). The proposed synchronization method allows us to reduce the resource consumption of the generating equipment and increase the reliability and efficiency of the functioning units of the examined power system. [ABSTRACT FROM AUTHOR]

The current stage of world energy development is characterized by the creation of powerful, territorially unified energy systems under free market conditions as well as renewable energy integration. Such transactions challenge networks' configurations and their operating modes. Electric power system (EPS) static stability could be considered one of the primary targets in this regard. The aim of this study was to evaluate the undergoing and expected modernization of the Latvian EPS in terms of static stability in a regional scale-based case study. In order to define static stability, a method based on a bus admittance matrix was proposed. A simplified Latvian EPS model was developed and assessed for the following three modes: past (2017), current (2020), and planned (2025), taking into account the Baltic States' planned development scenarios including large wind farm integration. The evaluation of computation results provided an opportunity to visually inspect changes in the EPS' sensitive elements such as lines and nodes. As a result, positive changes were observed (decrease of several weak points) for the planned mode (2025) as compared to the past mode (2017) under the considered modernization scenario. The detailed analysis and results are presented in the article. [ABSTRACT FROM AUTHOR]

This article aims to present some opportunities for improved solar energy utilization by raising the share of renewables in energy generation in the Visegrád Countries (Poland, Czech Republic, Slovakia, and Hungary). The analysis is based on the status of the renewable energy targets in the member countries and their future possibilities. This paper derives input through a thorough investigation of independent data, government policies, European Commission reports, and other data available online with free access. The analysis is processed by focusing on Hungary, as a country with various possible facets of solar energy demand and supply in the region. The assessment methodology is in the context of a geographical map, technical regression analysis, temperature distribution profiles, and the relative trends of solar potential in Hungary. The country currently has ten solar power plants with more than 10 MWp, and five remarkable plants under 10 MWp capacity spread over Hungary. The analysis on geographical aspects clubbed with technical and solar affecting parameters was carried out to harvest the sustainable potential of solar energy in the region. This study attempts to establish a relationship between the current and future prospects of solar energy in Hungary as a nation, and as part of the Visegrád countries, based on assessment for a sustainable future. [ABSTRACT FROM AUTHOR]