Adaptation in Load Shedding under Vulnerable Operation Conditions

J. Jung; C. C. Liu; S. L. Tanimoto; V. Vittal

doi:10.1109/MPER.2002.4312386

Adaptation in Load Shedding under Vulnerable Operation Conditions

J. Jung, C. C. Liu, S. L. Tanimoto, V. Vittal

Research output: Contribution to journal › Review article › peer-review

3 Scopus citations

Abstract

The proposed approach to avoiding catastrophic failures in interconnected power systems uses a defeusive system based on multiagent system technologies. Because of 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)	54-55
Number of pages	2
Journal	IEEE Power Engineering Review
Volume	22
Issue number	7
DOIs	https://doi.org/10.1109/MPER.2002.4312386
State	Published - Jul 2002
Externally published	Yes

Keywords

Aging
Defense system
Learning
Multiagent systems
Power system dynamics
Power system interconnection
Power system modeling
Power system planning
Power system reliability
Power system simulation
System testing
intelligent systems
multiagent system
power system vulnerability
reinforcement learning

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1109/MPER.2002.4312386

Cite this

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title = "Adaptation in Load Shedding under Vulnerable Operation Conditions",

abstract = "The proposed approach to avoiding catastrophic failures in interconnected power systems uses a defeusive system based on multiagent system technologies. Because of 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 = "Aging, Defense system, Learning, Multiagent systems, Power system dynamics, Power system interconnection, Power system modeling, Power system planning, Power system reliability, Power system simulation, System testing, intelligent systems, multiagent system, power system vulnerability, reinforcement learning",

author = "J. Jung and Liu, {C. C.} and Tanimoto, {S. L.} and V. Vittal",

year = "2002",

month = jul,

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language = "English (US)",

volume = "22",

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journal = "IEEE Power Engineering Review",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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AU - Jung, J.

AU - Liu, C. C.

AU - Tanimoto, S. L.

AU - Vittal, V.

PY - 2002/7

Y1 - 2002/7

N2 - The proposed approach to avoiding catastrophic failures in interconnected power systems uses a defeusive system based on multiagent system technologies. Because of 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 defeusive system based on multiagent system technologies. Because of 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 - Aging

KW - Defense system

KW - Learning

KW - Multiagent systems

KW - Power system dynamics

KW - Power system interconnection

KW - Power system modeling

KW - Power system planning

KW - Power system reliability

KW - Power system simulation

KW - System testing

KW - intelligent systems

KW - multiagent system

KW - power system vulnerability

KW - reinforcement learning

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DO - 10.1109/MPER.2002.4312386

M3 - Review article

AN - SCOPUS:85008047812

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SP - 54

EP - 55

JO - IEEE Power Engineering Review

JF - IEEE Power Engineering Review

IS - 7

ER -

Adaptation in Load Shedding under Vulnerable Operation Conditions

Abstract

Keywords

ASJC Scopus subject areas

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