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

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 an online approach to the assessment and management of dynamic security of electric power systems (EPS) wi...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Automation and remote control Ročník 79; číslo 10; s. 1741 - 1755
Hlavní autori: Voropai, N. I., Tomin, N. V., Sidorov, D. N., Kurbatsky, V. G., Panasetsky, D. A., Zhukov, A. V., Efimov, D. N., Osak, A. B.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Moscow Pleiades Publishing 01.10.2018
Springer Nature B.V
Predmet:
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Computer-Aided Engineering (CAD
Control
Control Problems for the Development of Large-Scale Systems
Electric power systems
Electronic equipment tests
Emergency management
Machine learning
Mathematics
Mathematics and Statistics
Mechanical Engineering
Mechatronics
Protection systems
Robotics
Systems Theory
index
voltage collapse
emergency automatics
machine learning
electric power systems
agent modeling
ISSN:0005-1179, 1608-3032
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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 an online approach to the assessment and management of dynamic security of electric power systems (EPS) with the use of a streaming modification of the random forest algorithm. The suite allows to recognize dangerous modes of complex closed-loop EPS, preventing the risk of emergencies on early stages. We show results of experimental tests on IEEE test systems.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0005-1179
1608-3032
DOI:10.1134/S0005117918100016