Поиск по сайту


   
Дополнительные параметры поиска

Результаты поиска ( Отсортировано по релевантности | Сортировать по дате )


Prediction of the power shortage in the electric power system by means of regression analysis by machine learning methods

Boyarkin D.A., Krupenev D.S., Iakubobsky D.V. Prediction of the power shortage in the electric power system by means of regression analysis by machine learning methods // E3S Web of Conferences. Т.114. ID: 03003. 2019. DOI: 10.1051/e3sconf/201911403003 Modern electricity consumers place increasingly high demands on the level of reliability of power supply and, correspondingly, the reliability of electric power systems (EPS). These requirements should be directly addressed in the EPS development...

Теги: decision trees , electric power systems , machine learning , number theory , random number generation , regression analysis , software reliability , support vector machines , electric power systems (eps) , mac
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 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
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
A random forest-based approach for voltage security monitoring in a power system

... 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 an on-line voltage security assessment scheme using periodically updated random forest-based decision trees. We demonstrated the proposed method on the modified 53-bus IEEE power system. Results are presented and discussed. © 2015 IEEE. входит

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


Телефоны

основной    +7(3952) 500-646
приемная    +7(3952) 42-47-00
факс     +7(3952) 42-67-96
Смотреть справочник