Adaptation in load shedding under vulnerable operating conditions

Juhwan Jung; Chen Ching Liu; Steven L. Tanimoto; Vijay Vittal

doi:10.1109/TPWRS.2002.805023

Adaptation in load shedding under vulnerable operating conditions

Juhwan Jung, Chen Ching Liu, Steven L. Tanimoto, Vijay Vittal

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

104 Scopus citations

Abstract

The proposed approach to avoiding catastrophic failures in interconnected power systems uses a defensive system based on multiagent system technologies. Due to their dynamic environments, these agents need to automatically adjust their decision criteria. The method of reinforcement learning with temporal differences can provide a suitable basis for the adaptation. An appropriate convergence criterion is derived and an application of the method to load shedding is demonstrated. A dynamic simulation involving a 179-bus power system is used to validate the method.

Original language	English (US)
Pages (from-to)	1199-1205
Number of pages	7
Journal	IEEE Transactions on Power Systems
Volume	17
Issue number	4
DOIs	https://doi.org/10.1109/TPWRS.2002.805023
State	Published - Nov 2002
Externally published	Yes

Keywords

Defense system
Intelligent systems
Multiagent system
Power system vulnerability
Reinforcement learning

ASJC Scopus subject areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering

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10.1109/TPWRS.2002.805023

Cite this

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title = "Adaptation in load shedding under vulnerable operating conditions",

abstract = "The proposed approach to avoiding catastrophic failures in interconnected power systems uses a defensive system based on multiagent system technologies. Due to their dynamic environments, these agents need to automatically adjust their decision criteria. The method of reinforcement learning with temporal differences can provide a suitable basis for the adaptation. An appropriate convergence criterion is derived and an application of the method to load shedding is demonstrated. A dynamic simulation involving a 179-bus power system is used to validate the method.",

keywords = "Defense system, Intelligent systems, Multiagent system, Power system vulnerability, Reinforcement learning",

author = "Juhwan Jung and Liu, {Chen Ching} and Tanimoto, {Steven L.} and Vijay Vittal",

note = "Funding Information: Manuscript received September 20, 2001; revised February 21, 2002. This work was supported by the U.S. Department of Defense and the Electric Power Research Institute through the Complex Interactive Networks/Systems Initiative under contract no. 8333 01.",

year = "2002",

month = nov,

doi = "10.1109/TPWRS.2002.805023",

language = "English (US)",

volume = "17",

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journal = "IEEE Transactions on Power Systems",

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T1 - Adaptation in load shedding under vulnerable operating conditions

AU - Jung, Juhwan

AU - Liu, Chen Ching

AU - Tanimoto, Steven L.

AU - Vittal, Vijay

N1 - Funding Information: Manuscript received September 20, 2001; revised February 21, 2002. This work was supported by the U.S. Department of Defense and the Electric Power Research Institute through the Complex Interactive Networks/Systems Initiative under contract no. 8333 01.

PY - 2002/11

Y1 - 2002/11

N2 - The proposed approach to avoiding catastrophic failures in interconnected power systems uses a defensive system based on multiagent system technologies. Due to their dynamic environments, these agents need to automatically adjust their decision criteria. The method of reinforcement learning with temporal differences can provide a suitable basis for the adaptation. An appropriate convergence criterion is derived and an application of the method to load shedding is demonstrated. A dynamic simulation involving a 179-bus power system is used to validate the method.

AB - The proposed approach to avoiding catastrophic failures in interconnected power systems uses a defensive system based on multiagent system technologies. Due to their dynamic environments, these agents need to automatically adjust their decision criteria. The method of reinforcement learning with temporal differences can provide a suitable basis for the adaptation. An appropriate convergence criterion is derived and an application of the method to load shedding is demonstrated. A dynamic simulation involving a 179-bus power system is used to validate the method.

KW - Defense system

KW - Intelligent systems

KW - Multiagent system

KW - Power system vulnerability

KW - Reinforcement learning

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JO - IEEE Transactions on Power Systems

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Adaptation in load shedding under vulnerable operating conditions

Abstract

Keywords

ASJC Scopus subject areas

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