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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
A random forest-based approach for voltage security monitoring in a power system

Negnevitsky M., Tomin N., Kurbatsky V., Panasetsky D., Zhukov A., Rehtanz C. A random forest-based approach for voltage security monitoring in a power system // 2015 IEEE Eindhoven PowerTech, PowerTech 2015. 1 p. ISBN (print): 9781479976. DOI: 10.1109/PTC.2015.7232460 Voltage collapse is a critical problem that impacts power system operational security. Timely and accurate assessment of voltage security is necessary to detect alarm states in order to prevent a large-scale blackout. This paper presents...

Теги: artificial intelligence , decision trees , electric power system security , learning systems , blackout , critical problems , large-scale blackout , operational security , random forests , security monitoring
Раздел: ИСЭМ СО РАН
Development of software for modelling decentralized intelligent systems for security monitoring and control in power systems

Panasetsky D., Tomin N., Voropai N., Kurbatsky V., Zhukov A., Sidorov D. Development of software for modelling decentralized intelligent systems for security monitoring and control in power systems // Proc. of Int. Conf. IEEE Power Tech, Eindhoven, Netherlands. 2015. 1 p. ISBN (print): 9781479976. DOI: 10.1109/PTC.2015.7232553 With rapidly increasing complexity of power grids in Europe, North America and Asia, liberalization of electricity markets and increasing penetration of renewable energy...

Теги: artificial intelligence , electric power system control , electric power system security , intelligent agents , intelligent systems , learning systems , matlab , monitoring , multi agent systems , power contro
Раздел: ИСЭМ СО РАН


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