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Wind speed and power ultra short-term robust forecasting based on Takagi–Sugeno fuzzy model

Liu F., Li R., Dreglea A. Wind speed and power ultra short-term robust forecasting based on Takagi–Sugeno fuzzy model // Energies. Vol.12. No.18. ID: 3551. 2019. DOI: 10.3390/en12183551 Accurate wind power and wind speed forecasting remains a critical challenge in wind power systems management. This paper proposes an ultra short-time forecasting method based on the Takagi–Sugeno (T–S) fuzzy model for wind power and wind speed. The model does not rely on a large amount of historical data and can...

Теги: linearization , machine learning , wind power: wind speed: t–s fuzzy model: forecasting , backpropagation , clustering algorithms , fuzzy clustering , learning systems , least squares approximations , neural networks , support vector machines , wind , wind power , b
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
Voltage/VAR Control and Optimization: AI approach

Tomin N., Kurbatsky V., Panasetsky D., Sidorov D., Zhukov A. Voltage/VAR Control and Optimization: AI approach // IFAC-PapersOnLine. Vol.51. No.28. 2018. P.103-108. DOI: 10.1016/j.ifacol.2018.11.685 Volt-VAr control systems provide the optimal solution with remote automatic or manual control of the capacitor banks and tap positions on the voltage regulators. However, such control possesses inherent characteristics of complexity, nonlinearity, inaccuracy and high requirement for control speed, parts...

Теги: machine learning , multi-agent system , power system , random forest , security , volt-var control , decision trees , intelligent agents , learning systems , value engineering , voltage regulators , inherent characteristics , optimal solutions , random forests , tradit
Раздел: ИСЭМ СО РАН
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 in a multi-agent system for distributed computing management

Bychkov I.V., Feoktistov A.G., Sidorov I.A., Edelev A.V., Gorsky S.A., Kostromin R.O. Machine learning in a multi-agent system for distributed computing management // CEUR Workshop Proceedings. Vol.2212. 2018. P.89-97. http://ceur-ws.org/Vol-2212/paper12.pdf We address the relevant problem of machine learning in a multi-agent system for distributed computing management. We propose a new approach to the agent learning in the system for managing job flows of scalable applications in a heterogeneous...

Теги: learning systems , multi agent systems , nanotechnology , distributed computing environment , energy development , high-performance computing clusters , job classification , parameter adjustments , parameter
Раздел: ИСЭМ СО РАН
Machine learning in electric power systems adequacy assessment using Monte-Carlo method

Boyarkin D.., Krupenev D.S., Iakubovskiy D.., Sidorov D.N. Machine learning in electric power systems adequacy assessment using Monte-Carlo method // Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017. ID: 8109871. P.201-205. ISBN (print): 9781538615966. DOI: 10.1109/SIBIRCON.2017.8109871 This paper deals with the computational efficiency related problem appearing in electric power systems adequacy assessment using Monte-Carlo method...

Теги: adequacy assessment , electric power systems , machine learning , monte carlo method , random forest , support vector machine , artificial intelligence , computational efficiency , decision trees , efficiency , learning systems , problem solving , support vector mach
Раздел: ИСЭМ СО РАН
Random forest based approach for concept drift handling

Zhukov A.V., Sidorov D.N., Foley A.M. Random forest based approach for concept drift handling // Communications in Computer and Information Science. Vol.661. 2017. P.69-77. ISBN (print): 978-3-319-52920-2; 978-3-319-52919-6. DOI: 10.1007/978-3-319-52920-2_7 Concept drift has potential in smart grid analysis because the socio-economic behaviour of consumers is not governed by the laws of physics. Likewise there are also applications in wind power forecasting. In this paper we present decision tree...

Теги: machine learning , decision tree , concept drift , ensemble learning , classification , random forest , classification (of information) , decision trees , image analysis , learning algorithms , learning systems , wind power , aggregation rules , concept drifts , empiri
Раздел: ИСЭМ СО РАН
Identification of pre-emergency states in the electric power system on the basis of machine learning technologies

Kurbatsky V., Tomin N. Identification of pre-emergency states in the electric power system on the basis of machine learning technologies // Proceedings of the World Congress on Intelligent Control and Automation (WCICA). 2016. P.378-383. ISBN (print): 9 781 467 . DOI: 10.1109/WCICA.2016.7578291 The paper proposes a concept of an intelligent system for early detection of pre-emergency state in electric power system as an option of preventive operation and emergency control. The main goal of such...

Теги: artificial intelligence , electric power systems , intelligent control , intelligent systems , learning algorithms , learning systems , construction principle , emergency control , emergency situation , emerge
Раздел: ИСЭМ СО РАН
Machine Learning Techniques for Power System Security Assessment

Tomin N.V., Kurbatsky V.G., Sidorov D.N., Zhukov A.V. Machine Learning Techniques for Power System Security Assessment // IFAC-PapersOnLine. Vol.49. No.27. 2016. P.445-450. DOI: 10.1016/j.ifacol.2016.10.773 Modern electricity grids continue to be vulnerable to large-scale blackouts. As all states leading to large-scale blackouts are unique, there is no algorithm to identify pre-emergency states. Moreover, numerical conventional methods are computationally expensive, which makes it difficult to use...

Теги: artificial intelligence , electric power system security , electric power transmission networks , learning algorithms , learning systems , numerical methods , pattern recognition , smart power grids , blackou
Раздел: ИСЭМ СО РАН


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