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

N. I. Voropai; N. V. Tomin; D. N. Sidorov; V. G. Kurbatsky; D. A. Panasetsky; A. V. Zhukov; D. N. Efimov; A. B. Osak

doi:10.1134/S0005117918100016

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

N. I. Voropai^*
, N. V. Tomin
, D. N. Sidorov
, V. G. Kurbatsky
, D. A. Panasetsky
, A. V. Zhukov
, D. N. Efimov
, A. B. Osak

^*Corresponding author for this work

Melent'ev Institute of Power Engineering Systems

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	1741-1755
Number of pages	15
Journal	Automation and Remote Control
Volume	79
Issue number	10
DOIs	https://doi.org/10.1134/S0005117918100016
State	Published - 1 Oct 2018
Externally published	Yes

Keywords

L-index
agent modeling
electric power systems
emergency automatics
machine learning
voltage collapse

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10.1134/S0005117918100016

Cite this

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title = "A Suite of Intelligent Tools for Early Detection and Prevention of Blackouts in Power Interconnections",

abstract = "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.",

keywords = "L-index, agent modeling, electric power systems, emergency automatics, machine learning, voltage collapse",

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doi = "10.1134/S0005117918100016",

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AU - Voropai, N. I.

AU - Tomin, N. V.

AU - Sidorov, D. N.

AU - Kurbatsky, V. G.

AU - Panasetsky, D. A.

AU - Zhukov, A. V.

AU - Efimov, D. N.

AU - Osak, A. B.

PY - 2018/10/1

Y1 - 2018/10/1

N2 - 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.

AB - 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.

KW - L-index

KW - agent modeling

KW - electric power systems

KW - emergency automatics

KW - machine learning

KW - voltage collapse

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JO - Automation and Remote Control

JF - Automation and Remote Control

IS - 10

ER -

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

Abstract

Keywords

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