Solution of the emergency control of synchronous generator modes based on the local measurements to ensure the dynamic stability

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. © 2022 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology

Recloser-based decentralized control of the grid with distributed generation in the Lahsh district of the Rasht grid in Tajikistan, central Asia

Small-scale power generation based on renewable energy sources is gaining popularity in distribution grids, creating new challenges for power system control. At the same time, remote consumers with their own small-scale generation still have low reliability of power supply and poor power quality, due to the lack of proper technology for grid control when the main power supply is lost. Today, there is a global trend in the transition from a power supply with centralized control to a decentralized one, which has led to the Microgrid concept. A microgrid is an intelligent automated system that can reconfigure by itself, maintain the power balance, and distribute power flows. The main purpose of this paper is to study the method of control using reclosers in the Lahsh district of the Rasht grid in Tajikistan with distributed small generation. Based on modified reclosers, a method of decentralized synchronization and restoration of the grid normal operation after the loss of the main power source was proposed. In order to assess the stable operation of small hydropower plants under disturbances, the transients caused by proactive automatic islanding (PAI) and restoration of the interconnection between the microgrid and the main grid are shown. Rustab software, as one of the multifunctional software applications in the field of power systems transients study, was used for simulation purposes. Based on the simulation results, it can be concluded that under disturbances, the proposed method had a positive effect on the stability of small hydropower plants, which are owned and dispatched by the Rasht grid. Moreover, the proposed method sufficiently ensures the quality of the supplied power and improves the reliability of power supply in the Lahsh district of Tajikistan. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Chinese Academy of Sciences, CAS: XDA20060303National Natural Science Foundation of China, NSFC: 41761144079Y848041Ministry of National Infrastructure, Energy and Water ResourcesFunding: This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road (Grant No. XDA20060303), the International Cooperation Project of the National Natural Science Foundation of China (Grant No. 41761144079), the Xinjiang Tianchi Hundred Talents Program (Grant No. Y848041), and the project of the Research Center of Ecology and Environment in Central Asia (Grant No. Y934031).Acknowledgments: The authors are thankful to the Ministry of Energy and Water Resources of the Republic of Tajikistan and the Rasht electric networks OJSHC “Barqi Tojik” for providing the data for this research work

Comparative Analysis for a Solar Tracking Mechanism of Solar PV in Five Different Climatic Locations in South Indian States: A Techno-Economic Feasibility

As the second most populous country in the world, India’s needs related to electricity production are still growing; thus, the country is seeking renewable energy resources as an alternative to conventional resources. Currently, India’s use of renewable energies ranks as fifth worldwide, with approximately 13.22% of the total amount of energy used in the form of solar energy, which is very nominal. Therefore, in the present study, a large-scale 20 MW solar PV power plant was modelled to access the technological and economic performances using the System Advisor Model (SAM) for the selected locations: Vishakhapatnam (VSKP), Hyderabad (HYD), Madurai (MDU), Thiruvananthapuram (TVC), and Bangalore (SBC), where solar radiation is high for South Indian states. In order to carry this out, three solar tracking mechanisms, i.e., fixed tracking (FT), single-axis tracking (SAT), and double-axis tracking (DAT), are taken into consideration at the selected locations. The results from the assessment of the FT mechanism’s yearly energy production show that 31 GWh were produced at TVC and 33 GWh were produced at VSKP, HYD, MDU, and SBC in the first year of the project, with a capacity factor (CF) from 18.5% to 19.5%. Conversely, the SAT mechanism generated an annual amount of energy, ranging from 38 GWh to 42 GWh, with an increase in the CF ranging from 22% to 23%. Furthermore, the DAT mechanism’s annual energy generated 44 GWh to 46 GWh, with the CF ranging between 25% and 26.5%. However, the recorded levelized cost of energy (LCOE) ranges were between 3.25 ¢/kWh to 4.25 ¢/kWh at the selected locations for all three mechanisms. The sensitivity analysis results also suggest that the FT and SAT mechanisms are not economically feasible because of their negative net present values (NPV) in all five locations, whereas the DAT mechanism generated positive results for all of the locations after 20 years. Furthermore, according to the study, we concluded that HYD was identified as the most feasible location in the South Indian region for installing a large-scale solar PV power project. © 2022 by the authors.Xinjiang Tianchi Hundred Talents Program, (Y848041)National Natural Science Foundation of China, NSFC, (41761144079, 42150410393)Chinese Academy of Sciences, CAS, (2021PC0002, XDA20060303)Ministry of Education and Science of the Russian Federation, Minobrnauka, (FEUZ-2022-0031)K. C. Wong Education Foundation, (GJTD-2020-14)Funding text 1: Strategic Priority Research Program of Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road: XDA20060303International Cooperation Project of the National Natural Science Foundation of China: 41761144079Research Fund for International Scientists of National Natural Science Foundation of China: 42150410393CAS PIFI Fellowship: 2021PC0002K.C. Wong Education Foundation: GJTD-2020-14Xinjiang Tianchi Hundred Talents Program: Y848041.Funding text 2: The 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: Grant number: FEUZ-2022-0031

Expert system application for reactive power compensation in isolated electric power systems

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

Optimal Amount of Information Determination for Power System Steady State Estimation

On the basis of literature sources analysis, the paper provides the rationale for the necessity of considering the limited digital devices capabilities when designing closed digital control systems for the complex electrical power grids. The problem of design is decomposed into two subproblems: design of current state observation vector digital transmission systems and current controlled process state estimation; design of digital systems for optimal control vector calculation, transmission and control actions realization. The paper presents consideration of the former problem, i.e. design of current state observation vector digital transmission systems and current controlled process state estimation: the mathematical model of digital system of information transmission and state estimation considering speed and reliability of technical means of implementation is presented; the functional structure of simulation complex is developed; the paper provides the formulation of the problem of estimating the optimal amount of information about the control object state, resulting in a solution of computational experiments simulating complex. © 2021 The Author(s).The reported study was funded by RFBR, Russia, Sirius University of Science and Technology, JSC Russian Railways and Educational Fund “Talent and success”, project number 20-38-51007

Nested ensemble selection: An effective hybrid feature selection method

It has been shown that while feature selection algorithms are able to distinguish between relevant and irrelevant features, they fail to differentiate between relevant and redundant and correlated features. To address this issue, we propose a highly effective approach, called Nested Ensemble Selection (NES), that is based on a combination of filter and wrapper methods. The proposed feature selection algorithm differs from the existing filter-wrapper hybrid methods in its simplicity and efficiency as well as precision. The new algorithm is able to separate the relevant variables from the irrelevant as well as the redundant and correlated features. Furthermore, we provide a robust heuristic for identifying the optimal number of selected features which remains one of the greatest challenges in feature selection. Numerical experiments on synthetic and real-life data demonstrate the effectiveness of the proposed method. The NES algorithm achieves perfect precision on the synthetic data and near optimal accuracy on the real-life data. The proposed method is compared against several popular algorithms including mRMR, Boruta, genetic, recursive feature elimination, Lasso, and Elastic Net. The results show that NES significantly outperforms the benchmarks algorithms especially on multi-class datasets. © 2023 The Author(s)American University of Sharjah, AUSThe work in this paper was supported by the Open Access Program from the American University of Sharjah

Monthly Runoff Forecasting by Non-Generalizing Machine Learning Model and Feature Space Transformation (Vakhsh River Case Study)

Energy prices and сost of materials for solar and wind power plants have increased over the past year. Therefore, significance increases for the hydropower and long-term (1-10 years) planning generation for the existing hydropower plants, which requires forecasting the average monthly values of the river flow. This task is especially urgent for countries without their own oil-fields and opportunity to invest in the creation of solar or wind power plants. The aim of the research is to decrease the mean absolute forecasting error of the long-term prediction for the Vakhsh River flow (Tajikistan) based on the long-term observations. A study of existing methods for the river runoff forecasting in relation to the object under consideration was carried out, and a new transformation model for the space of the input features was developed. The most significant results are the decrease in the average forecast error in the Vakhsh river flow achieved by the use of the proposed space of polynomial logarithmic features in comparison with other methods, and the need to use at least the 20 year-old observational data for the long-term operation planning of the hydropower plants and cascades of the hydropower plants obtained from the results of computational experiments. The significance of the results lies in the fact that a new approach to the long-term forecasting of river flow has been proposed and verified using the long-term observations. This approach does not require the use of the long-term meteorological forecasts, which are not possible to obtain with high accuracy for all regions. © 2022 Problems of the Regional Energetics. All rights reserved

Deep learning for Covid-19 forecasting: State-of-the-art review.

The Covid-19 pandemic has galvanized scientists to apply machine learning methods to help combat the crisis. Despite the significant amount of research there exists no comprehensive survey devoted specifically to examining deep learning methods for Covid-19 forecasting. In this paper, we fill the gap in the literature by reviewing and analyzing the current studies that use deep learning for Covid-19 forecasting. In our review, all published papers and preprints, discoverable through Google Scholar, for the period from Apr 1, 2020 to Feb 20, 2022 which describe deep learning approaches to forecasting Covid-19 were considered. Our search identified 152 studies, of which 53 passed the initial quality screening and were included in our survey. We propose a model-based taxonomy to categorize the literature. We describe each model and highlight its performance. Finally, the deficiencies of the existing approaches are identified and the necessary improvements for future research are elucidated. The study provides a gateway for researchers who are interested in forecasting Covid-19 using deep learning. © 2022 Elsevier B.V.Dunarea de Jos” University of GalatiUmm Al-Qura Univer-sityUmm Al-Qura University, UQU, (22UQU4300274DSR01)Deanship of Scientific Research, King Saud UniversityFunding text 1: Deanship of Scientific Research at Umm Al-Qura University supported this work by Grant Code: (22UQU4300274DSR01).Funding text 2: Conceptualization, H.O.T., H.M.H.Z., A.M.A.M., G.A. and S.A.M.I.methodology, F.T.A. and H.O.T.software, D.S.B., A.H.A., H.M.H.Z. and A.E.validation, S.A.M.I., A.M.A.M., D.S.B., D.E.A., W.E., Y.S.R. and A.E.formal analysis, H.M.H.Z., and F.T.A.investigation, H.O.T., W.E., and G.A.resources, F.T.A. and D.S.B.data curation, S.A.M.I., A.H.A. and A.E.writing—original draft preparation, Y.S.R., D.E.A., H.O.T., D.E.A., F.T.A. and A.E.writing—review and editing, H.M.H.Z., S.I, A.M.A.M., A.H.A. and A.E.visualization, W.E. and A.E.supervision, H.M.H.Z., W.E., Y.S.R. and D.S.B.project administration, H.O.T., A.E., Y.S.R. and S.A.M.I.funding acquisition A.E. (The APC was funded by “Dunarea de Jos” University of Galati, Romania). All authors have read and agreed to the published version of the manuscript.Funding text 3: The authors would like to thank the Deanship of Scientific Research at the Umm Al-Qura Univer-sity for supporting this work by grant code (22UQU4300274DSR01). The APC was covered by “Dunarea de Jos” University of Galati, Romania

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

The use of Petri computing networks for optimization of the structure of distribution networks to minimize power losses

The paper suggests a self-organizing multi-component computational algorithm as a solution to the problem of optimizing the structure of distribution electrical networks to minimize the loss of power. The suggested algorithm is consistent with the method of branches and borders and uses the apparatus of the Petri computer networks (PCN) apparatus. The PCN apparatus has a universal computational capability to process symbolic-numeric data, which along with the solution of calculating problems, provides for the structural and logical analysis of the systems and processes under study. The structure of the PCN based algorithm is similar to the studied system, which provides for better visualization and convenience of interpretation, modification, and implementation of this algorithm on one or more computers by paralleling computational processes for better system performance. Computing modules within the general text of the algorithm can be arranged in any given order and solve the problem by organizing themselves in the process of functioning. © 2020The reported research was partly funded by Russian Foundation for Basic Research and the government of the Yamal region of the Russian Federation , grant No. 19-48-890001