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A Suite of Intelligent Tools for Early Detection and Prevention of Blackouts in Power Interconnections

Voropai N.I., Tomin N.V., Sidorov D.N., Kurbatsky V.G., Panasetsky D.A., Zhukov A.V., Efimov D.N., Osak A.B. A Suite of Intelligent Tools for Early Detection and Prevention of Blackouts in Power Interconnections // Automation and Remote Control. Vol.79. No.10. 2018. P.1741-1755. DOI: 10.1134/S0005117918100016 We propose a suite of intelligent tools based on the integration of methods of agent modeling and machine learning for the improvement of protection systems and emergency automatics. We propose...

Теги: agent modeling , electric power systems , emergency automatics , l-index , machine learning , voltage collapse , artificial intelligence , decision trees , electric power system interconnection , learning systems , online systems , software agents , agent model , elec
Раздел: ИСЭМ СО РАН
Machine learning for energy systems

Sidorov D., Liu F., Sun Y. Machine learning for energy systems // Energies. Vol.13. No.18. ID: 4708. 2020. DOI: 10.3390/en13184708 [No abstract available] входит Статья в журнале

Теги: artificial intelligence , cyber-physical systems , energy management system , energy storage , energy systems , forecasting , industrial mathematics , intelligent control , inverse problems , load leveling , of
Раздел: ИСЭМ СО РАН
Taxonomy research of artificial intelligence for deterministic solar power forecasting

Wang H., Liu Y., Zhou B., Li C., Cao G., Voropai N., Barakhtenko E. Taxonomy research of artificial intelligence for deterministic solar power forecasting // Energy Conversion and Management. Vol.214. ID: 112909. 2020. DOI: 10.1016/j.enconman.2020.112909 With the world-wide deployment of solar energy for a sustainable and renewable future,...

Теги: artificial intelligence , photovoltaic power generation , solar power forecast , taxonomy , deep learning , solar energy , stochastic systems , taxonomies , application scenario , electrical energy systems , energy prediction , future research directions , nonlinear
Раздел: ИСЭМ СО РАН
Проблемы развития цифровой энергетики в России

Воропай Н.И., Губко М.В., Ковалев С.П., Массель Л.В., Новиков Д.А., Райков А.Н., Сендеров С.М., Стенников В.А. Проблемы развития цифровой энергетики в России // Проблемы управления. №1. 2019. C.2-14. DOI: 10.25728/pu.2019.1.1 Отмечено, что в условиях исчерпания потенциала экстенсивной эксплуатации сырьевых ресурсов цифровая трансформация является для России «окном» больших возможностей. Показано, что в таких условиях возрастает необходимость цифровизации энергетических систем с учетом усложнения...

Теги: ¶искусственный интеллект , компьютерное моделирование , прорывное развитие , цифровые технологии , цифровая энергетика , энергетическая безопасность , artificial intelligence , computer modelling , breakthrough development , digital technologies , digital energy , e
Раздел: ИСЭМ СО РАН
The development of a joint modelling framework for operational flexibility in power systems

Voropai N., Rehtanz C., Kippelt S., Tomin N., Haeger U., Efimov D., Kurbatsky V., Kolosok I. The development of a joint modelling framework for operational flexibility in power systems // 2019 16th Conference on Electrical Machines, Drives and Power Systems, ELMA 2019 - Proceedings. ID: 8771685. 2019. ISBN (print): 9781728114132. DOI: 10.1109/ELMA.2019.8771685 The TU Dortmund University (Germany) and the Energy Systems Institute of the Russian Academy of Sciences (Russia) launched a joint research...

Теги: artificial intelligence , electric power system , flexibility , machine learning , power system security , electric machinery , electric power systems , learning systems , different layers , energy systems , modelling framework , operational flexibility , russian aca
Раздел: ИСЭМ СО РАН
Intelligent control and protection of power systems in the Russian cities

Voropai N., Kurbatsky V., Tomin N., Efimov D., Kolosok I. Intelligent control and protection of power systems in the Russian cities // SMARTGREENS 2019 - Proceedings of the 8th International Conference on Smart Cities and Green ICT Systems. 2019. P.19-29. ISBN (print): 9789897583735. A distinctive feature of the energy system development in Russian megalopolises is the need for a comprehensive approach to the problem of making the network intelligent. The paper presents the following contributions:...

Теги: artificial intelligence , control , power grid , protection , russia , smart cities , control engineering , electric power system protection , electric power transmission networks , green computing , intelligent agents , learning systems , multi agent systems , power
Раздел: ИСЭМ СО РАН
Non-cascade speed and current control of IPMSM for electrical vehicles based on model predictive control

Gao F., Liu F., Sidorov D.N. Non-cascade speed and current control of IPMSM for electrical vehicles based on model predictive control // ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications. 2018. In this paper, the speed tracking problem of the interior permanent magnet synchronous machines(IPMSM) of electric vehicle is studied. A non-cascade speed...

Теги: interior permanent magnet synchronous motor , model predictive control , non-cascade , speed and current control , artificial intelligence , cascade control systems , controllers , electric current control , permanent magnets , predictive control systems , speed , c
Раздел: ИСЭМ СО РАН
Situation calculus application in tasks of intelligent decision-making support

Massel L., Kuzmin V. Situation calculus application in tasks of intelligent decision-making support // RPC 2018 - Proceedings of the 3rd Russian-Pacific Conference on Computer Technology and Applications. ID: 8482131. 2018. ISBN (print): 9781538675311. DOI: 10.1109/RPC.2018.8482131 The basic concepts of situational calculus are considered. The architecture of the intelligent decision support system (Situation polygon) for strategy development in the energy sector is presented. It is based on the...

Теги: contingency management , decision-making , situation calculus , situation polygon , situational management , artificial intelligence , calculations , decision support systems , geometry , semantics , basic concepts , intelligent decision making , intelligent decision
Раздел: ИСЭМ СО РАН
Ontology-based decision support system for forecasting of energy infrastructure development

Kopaygorodsky A. Ontology-based decision support system for forecasting of energy infrastructure development // RPC 2018 - Proceedings of the 3rd Russian-Pacific Conference on Computer Technology and Applications. ID: 8482172. 2018. ISBN (print): 9781538675311. DOI: 10.1109/RPC.2018.8482172 This article reports the approach and software tools for decision support in forecasting the energy infrastructure development. Support of expert activity is based on using the ontological hybrid approach to...

Теги: energy infrastructure development forecasting , expert decision support , knowledge management , ontology , artificial intelligence , computer software , forecasting , decision supports , energy infrastructures , expert knowledge , hybrid approach , intelligent info
Раздел: ИСЭМ СО РАН
Machine learning algorithms application to road defects classification

Nguyen T.H., Nguyen T.L., Sidorov D.N., Dreglea A.I. Machine learning algorithms application to road defects classification // Intelligent Decision Technologies. Vol.12. №1. 2018. P.59-66. DOI: 10.3233/IDT-170323 The novel approach for automatic detection and classification of road defects is proposed based on shape and texture features analysis. The system includes three main steps: defects position detection, feature contour extraction followed by classification of defects. The proposed approach...

Теги: a random forest algorithm , boosting algorithm , graph-cuts method , markov random fields , pavement condition , road defects , adaptive boosting , artificial intelligence , decision trees , defects , feature extraction , graphic methods , image segmentation , learnin
Раздел: ИСЭМ СО РАН


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