The article deals with the issues of optimizing the operating modes of 6-10 kV distribution networks constituted by the large-sized complicated meshed systems.In real time, such networks operate with breaking the circuit to eliminate equalizing currents.The goal of optimizing the networks' operating modes is to minimize the active power loss.Determining the optimal breakpoints is a complicated discrete task, for which the method of genetic algorithm becomes the most suitable solution.

Forecasting power consumption is a crucial aspect of managing cities and regions. Accurate forecasts ensure smooth and uninterrupted operation of consumer and industrial units. While the traditional methods of forecasting have been useful, the advent of big data has enabled the use machine learning techniques. In this paper, we discuss the applications of machine learning to power forecasting. We describe the technical details of the existing methods and highlight their strengths and weaknesses. We find that there is no single method that fits all scenarios. The optimal method depends on various factors including the characteristics of the data, the size of the data, and the forecasting horizon.

In the quest for sustainable and efficient energy solutions, hydrogen fuel cells emerge as a beacon of hope, offering a promising pathway towards a greener future. Accurate Identification of the ungiven parameters of proton exchange membrane fuel cell (PEMFC) mathematical models is indispensable for designing, managing, and simulating the practical PEMFC. In order to identify the parameters of PEMFC punctually, this paper presents a modified version of the slime mould algorithm (MSMA). In order to increase capability of the MSMA in the exploitation phase, both locally and globally, the sine-cosine technique has been utilized to boost the search capabilities. To assess the performance of MSMA, MSMA is first utilized to address ten well-known benchmark functions. The obtained results confirm that MSMA outperforms SMA on all benchmark functions. Then, MSMA is employed to solve the optimization problem of different mechanical design problems and also the MSMA provides superior performance over the standard SMA. Finally, the MSMA is used to identify the unknown parameters of four typical PEMFCs: 250W PEMFC, BCS 500W PEMFC, AVISTA SR-12 model, and the Temasek 1 kW PEMFC model. Experimental results boost the supremacy of MSMA in the PEMFC parameters extraction by comparing it with the original SMA and well-known potent optimization techniques. Furthermore, MATLAB/ Simulink is employed for advanced dynamic PEMFC modeling, facilitating a comprehensive assessment of fuel cell parameters. The validation of this dynamic PEMFC model, using MSMA-optimized parameters, establishes its practical utility in system analysis and real-world fuel cell operation, marking a significant advancement in PEMFC technology management and simulation.

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.

В статье показаны возможности оценки технического состояния силовых маслонаполненных трансформаторов на основе данных технической диагностики методом Байеса. Анализ проводится на основе экспертных оценок с помощью функций принадлежности, на основе которых производится расчет вероятностей проявления (не проявления) различных состояний силовых трансформаторов. В настоящей работе предлагается использование математического аппарата, разработанного на основе синтеза нечётких лингвистических переменных и количественных характеристик диагностируемого оборудования. Достоверность предложенного метода подтверждается соответствующими расчётными результатами, демонстрирующими адекватное поведение модели применительно к силовым трансформаторам.

Over the past decades, power companies have been implementing load forecasting to determine trends in the electric power system (EPS); therefore, load forecasting is applied to solve the problems of management and development of power systems. This paper considers the issue of building a model of medium-term forecasting of load graphs for EPS with specific properties, based on the use of ensemble machine learning methods. This paper implements the approach of identification of the most significant features to apply machine learning models for medium-term load forecasting in an isolated power system. A comparative study of the following models was carried out: linear regression, support vector regression (SVR), decision tree regression, random forest (Random Forest), gradient boosting over decision trees (XGBoost), adaptive boosting over decision trees (AdaBoost), AdaBoost over linear regression. Isolation of features from a time series allows for the implementation of simpler and more overfitting-resistant models. All the above makes it possible to increase the reliability of forecasts and expand the use of information technologies in the planning, management, and operation of isolated EPSs. Calculations of the total forecast error have proved that the characteristics of the proposed models are high quality and accurate, and thus they can be used to forecast the real load of a power system.

Reliable operation of power systems with a significant share of hydropower plants (HPPs) in the energy mix depends in many respects on how accurately the water inflow is forecasted. Taken together, the water inflow prediction and optimal planning of production define energy security, ensure the possibility of protection from floods, and eliminate idle discharges at hydroelectric power plants. The solution of such problems is complicated by lack of reliable information about the water inflow, its being stochastic in nature, a variable electricity consumption pattern, and inaccurate prediction and planning models. Improvement of prediction accuracy is focused on determining the water inventory for planning prospective electricity generation at HPPs taking into account regulation in the medium term. Such regulation is necessary to meet the power system load in the load curve peak and semi-peak parts. The paper considers the problem of constructing a medium-term water inflow prediction model for planning electricity generation for a week ahead taking into account climate changes in isolated operating power systems taking as an example the electric power systems of the Gorno-Badakhshan autonomous oblast in Tajikistan. For taking into account constant climate changes, it is proposed to use an approach based on machine learning methods, which features a self-adaptation capability. Based on the results of accomplished experimental and industry-grade numerical analyses, the expediency of using a model based on an ensemble of regression decision trees has been shown.

Reliable operation of power systems (PS), including those with a significant share of hydropower plants (HPPs) in the energy balance, largely depends on the accuracy of forecasting power generation. The importance of power generation forecasts increases with the development of renewable power generation, which is stochastic by nature. Those kinds of tasks are complicated by the lack of reliable information on metrological data and estimated energy consumption, which is also stochastic. In the medium-term forecasting (MTF) of power generation by HPPs, the seasonality of changes in flow and inflow of water should be taken into account, which significantly affects the reserves and regulatory capabilities of the power system as a whole. This work discusses the problem of constructing a model for MTF of power generation HPP in isolated power systems (IPS), taking into account such atmospheric parameters as air temperature, wind speed and humidity. To address constant climatic changes, this paper suggests implementing machine learning models. The proposed approach is characterized by a high degree of autonomy and learning automation. The paper provides a comparative study of the machine learning models such as polynomial model with Tikhonov’s regularization (LR), k-nearest neighbors (kNN), multilayer perceptron (MLP), ensembles of decision trees, adaptive boosting of linear models (ABLR), etc. Computational experiments have shown that the machine learning approach yields the results of sufficient quality, which allows to use them for forecasting of power generation HPP in isolated power systems under conditions of climate change. The Adaptive Boosting Linear Regression model is the simplest and most reliable machine learning model that has proven itself well in the tasks with a relatively small amount of training samples.

Medium-term forecasting of water inflow is of great importance for small hydroelectric power plants operating in remote power supply areas and having a small reservoir. Improving the forecasting accuracy is aimed at solving the problem of determining the water reserve for the future generation of electricity at hydroelectric power plants, taking into account the regulation in the medium term. Medium-term regulation is necessary to amplify the load in the peak and semi-peak portions of the load curve. The solution to such problems is aggravated by the lack of sufficiently reliable information on water inflow and prospective power consumption, which is of a stochastic nature. In addition, the mid-term planning of electricity generation should consider the seasonality of changes in water inflow, which directly affects the reserves and the possibility of regulation. The paper considers the problem of constructing a model for medium-term forecasting of water inflow for planning electricity generation, taking into account climatic changes in isolated power systems. Taking into account the regularly increasing effect of climate change, the current study proposes using an approach based on machine learning methods, which are distinguished by a high degree of autonomy and automation of learning, that is, the ability to self-adapt. The results showed that the error (RMSE) of the model based on the ensemble of regression decision trees due to constant self-adaptation decreased from 4.5 m3/s to 4.0 m3/s and turned out to be lower than the error of a more complex multilayer recurrent neural network (4.9 m3/s). The research results are intended to improve forecasting reliability in the planning, management, and operation of isolated operating power systems.

The paper describes the distinctive features of the isolated power system of Tajikistan, significant part of which is constituted by the hydropower plants; identifies the main problems of the electric power system of the Republic of Tajikistan in terms of power generation; describes specific features of HPP cascade management . proposes a method of determining the alternative fully drawn down level of the Norak HPP reservoir, taking into account the water level requirements in various water volume conditions from the point of view of power generation increase; estimates the economic efficiency of reducing the deficit of electricity in the power system with view to long-term optimization.

At present, as the demand for electricity increases in all sectors, there is an urgent need to introduce alternative renewable energy sources into modern energy systems. Renewable energy sources, which consist of solar (photovoltaic, PV), wind and hydro power, are key alternative sources of “green energy’’ energies, but it can also be used to produce “green” hydrogen. Thanks to scientific and technological progress, the cost of photovoltaic solar radiation converters is constantly decreasing at a high rate, which makes it possible to build solar power plants of sufficiently large capacity. In the coming decades, solar energy will become an incentive for the economic development of countries that have the maximum “solar” resource. The Republic of Tajikistan is one of these countries with a high potential for solar energy. The article presents an analysis of the resources and potential of solar energy in the Republic of Tajikistan. The study of electromagnetic transients in networks with photovoltaic solar power plants is performed. The main equations, simulation model and calculations of transients are presented, taking into account changes in voltage on DC buses. An algorithm for controlling the system of automatic control of output parameters is proposed. The analysis of dynamic and static modes in parallel operation of a solar power plant with the grid is carried out. A block diagram and computer model is constructed in the MATLAB package together with Simulink and Power System Blockset.

Current trends in power system development and the issues they pose have increased the urgency of automatic and emergency control alongside transient electromechanical processes. Such control devices place high demands on mode-parameter estimation algorithms in terms of their speed, accuracy, and transient sensitivity. This paper proposes a rapid estimation algorithm for power system parameters to investigate its optimal settings and applicability in both static and dynamic states. The proposed algorithm is based on signal approximation on sliding window using a multiparameter model, which is highly stable and reliable. The signals modeled in MATLAB/Simulink were used as initial data. From the experimental results, it became clear that the rapid estimation algorithm of the power system parameters showed high accuracy in steady-state mode as well as during transients, and its optimal parameters were identified. The developed algorithm can be used in relay protection, automatic and emergency control devices, and synchronous machine state-monitoring systems.

The paper offers a method for developing a universal system of automated design of an optimal structure of autonomous distributed hybrid energy complexes (ADHEC) and a means of regulation of the energy balance therein, i.e. control of the power flows circulating in the said system. In general, the design of the optimal structure of ADHEC includes the following stages (subtasks): data research and creation of a statistical database of electric loads of consumers, of the wind speed in the region under consideration, of the hydroelectric potential of mountain and lowland rivers, and of the solar energy, as well as research and development of a database of converters of wind and water energy into electrical energy. The paper dwells on the task of designing the optimal structure of the distributed hybrid generation system that will ensure the desired level of power generation at a minimal cost and with necessary functional reliability.

The paper offers a method for developing a universal system of automated design of an optimal structure of autonomous distributed hybrid energy complexes (ADHEC) and the means for regulation of the energy balance therein, i.e. control of the power flows circulating in the mentioned system. ADHEC will not only help unload the existing power system, but can also be used to produce "green" hydrogen. In general, the design of the optimal structure of ADHEC includes the following stages (subtasks): data research and creation of a statistical database of electric loads of consumers, the wind speed in the region under consideration, the hydroelectric potential of mountain and lowland rivers, and the solar energy, as well as the research and development of a database of converters of wind and water energy into electrical energy. The paper focused on the task of designing the optimal structure of the distributed hybrid generation system that will ensure the desired level of power generation at a minimal cost and with necessary functional reliability.

Tajikistan and Kyrgyzstan are the two countries in Central Asia that have a huge reserve of hydro resources of the region. It is important to recognize the significance of the part played by the micro-hydropower plants (HPP) in the electric power generation in Tajikistan and Kyrgyzstan from the point of view of sustainable economic development. After all, the construction of micro-HPPs in mountainous areas will reliably ensure the development of small and medium-sized enterprises in the field of agriculture and livestock, industry, tourism, improve the social conditions of the population, as well as ensure the production of "green" hydrogen, which will contribute to the development of an environmentally friendly transport system in the regions. Micro HPPs gained recognition as a good alternative to traditional power generation for many developing countries around the world. This study presents a structural model and methodology of choice of a feasible type of micro HPP using the developed algorithm for calculation of hydro turbines' characteristics based on the hydrological characteristics of small and shallow watercourses located in Central Asian countries, such as Kyrgyzstan and Tajikistan. Based on this model, the software "Calculation and choosing the type of hydro turbines for micro HPPs" has been developed. Depending on the load, a consumer can choose one of the suggested types of micro-hydroelectric power plants to meet his requirements. When choosing the type of micro-hydroelectric power station, a consumer should also take into account the factor of the seasonality of the water level, the constancy and speed of the water, and the volume of river water, since in some places the water freezes in winter.

Effective electricity use can be an option which enables to achieve significant economy while generating and transmitting of electricity. One of the most important things is to improve the electricity quality through reactive power correction up to optimum values. The current article presents the solution to compensate the reactive power in the distribution networks, in GornoBadakhshan Autonomous Oblast (GBAO) with the use of the advanced technologies based on the data collection within real time. The article describes the methodology of fuzzy logic application and bio-heuristic algorithms for the suggested solution effectiveness to be determined. Fuzzy logic application to specify the node priority for compensating devices based on the linguistic matrix power loss and voltage gives the possibility to the expert to take appropriate solutions for compensating devices installation location to be determined. The appropriate (correct) determination of the compensating devices installation location in the electric power system ensures the effective regulation of the reactive power with the least economic costs. Optimization problems related to the active power loss minimization are solved as well as the cost minimization with compensating devices to ensure the values tan(φ) not exceeding 0.35 through reducing multi-objective problem to the single-objective one using linear convolution.

In the last few years, a great number of methods for identifying the load model parameters have been proposed. This article discusses the use of statistical approach to estimate the substation equivalent load model parameters for supplying to oil-producing industrial region. The disadvantages of existing statistical approach are the low accuracy obtained for the parameter estimates, especially when using samples size is small. To eliminate this deficiency, the current measurement data archive from SCADA system of electrical parameters for 15 months was collected. For the purpose of verifying the obtained results of statistical processing of SCADA data, a full-scale experiment was carried out in relation to the studied substation. The article describes the statistical method used to process the current SCADA measurement data, the results of archived statistical processing and experimental SCADA data. The electrical load models' parameters received from the experimental studies results are of practical importance.

Extra-high voltage (EHV) lines of 500–750 kV, providing transmission of electricity over long distances and at the same time performing the functions of intersystem communication at the level of the national power system, play an important role not only in normal modes, but also in emergency modes, ensuring the dynamic stability of the power system as a whole. In these lines, the overwhelming proportion of power cuts are caused by single-phase short circuits (90%), a significant part of which, being unstable arc faults, are successfully eliminated in the single-phase auto-reclosing cycle. Also, about 5%–10% of failures can be constituted by two-phase short circuits, which can be eliminated in a two-phase auto-reclosing cycle (TPhAR). The purpose of this paper is to study two-phase auto-reclosing in transposed EHV lines equipped with four-radial shunt reactors (ShR). The paper analyzes the efficiency of using a two-phase auto-reclosing to eliminate two-phase short-circuits in the lines connecting the power systems of Kyrgyzstan and Tajikistan. An algorithm is proposed for calculating the transient recovering voltages (TRV) and secondary arc currents (SAC) in the real transposed line Datka–Khujand–Dushanbe. The obtained results of TRV and SAC, which are within the permissible limits for the Dushanbe–Khujand line section, make it possible to have a dead time of TPhAR of no more than 0.6 s, in order to maintain the dynamic stability of the power system. For lines with a length of about 500 km (Datka–Khujand), equipped with three reactors, a successful TPhAR is impossible due to the appearance of resonant TRV in the circuit. The paper proposes the use of banks of capacitors connected in series in the phases of the ShR for the implementation of a successful TPhAR with the duration of the required pause of about 0.6 s.