Conceptual designs of AI-based systems for local prediction of voltage collapse

K. Yabe; J. Koda; K. Yoshida; K. H. Chiang; P. S. Khedkar; D. J. Leonard; N. W. Miller

doi:10.1109/59.485995

Conceptual designs of AI-based systems for local prediction of voltage collapse

K. Yabe^*, J. Koda, K. Yoshida, K. H. Chiang, P. S. Khedkar, D. J. Leonard, N. W. Miller

^*Corresponding author for this work

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

27 Citations (Scopus)

Abstract

Vulnerability of modern power systems to locally initiated voltage collapse gives rise to a need for methods to measure local voltage security and to predict voltage instability. The paper presents a novel architecture based on a suite of AI technologies and three-dimensional PQV surfaces which provides prediction of local voltage collapse and indices of system voltage security. Robustness and adaptation are demonstrated on difficult and realistic power system simulation models.

Original language	English
Pages (from-to)	137-145
Number of pages	9
Journal	IEEE Transactions on Power Systems
Volume	11
Issue number	1
DOIs	https://doi.org/10.1109/59.485995
Publication status	Published - 1996
Externally published	Yes

Keywords

Artificial intelligence
Fuzzy kaiman filters
Neuro-fuzzy systems
Voltage stability

ASJC Scopus subject areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering

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10.1109/59.485995

Cite this

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abstract = "Vulnerability of modern power systems to locally initiated voltage collapse gives rise to a need for methods to measure local voltage security and to predict voltage instability. The paper presents a novel architecture based on a suite of AI technologies and three-dimensional PQV surfaces which provides prediction of local voltage collapse and indices of system voltage security. Robustness and adaptation are demonstrated on difficult and realistic power system simulation models.",

keywords = "Artificial intelligence, Fuzzy kaiman filters, Neuro-fuzzy systems, Voltage stability",

author = "K. Yabe and J. Koda and K. Yoshida and Chiang, {K. H.} and Khedkar, {P. S.} and Leonard, {D. J.} and Miller, {N. W.}",

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AU - Koda, J.

AU - Yoshida, K.

AU - Chiang, K. H.

AU - Khedkar, P. S.

AU - Leonard, D. J.

AU - Miller, N. W.

PY - 1996

Y1 - 1996

N2 - Vulnerability of modern power systems to locally initiated voltage collapse gives rise to a need for methods to measure local voltage security and to predict voltage instability. The paper presents a novel architecture based on a suite of AI technologies and three-dimensional PQV surfaces which provides prediction of local voltage collapse and indices of system voltage security. Robustness and adaptation are demonstrated on difficult and realistic power system simulation models.

AB - Vulnerability of modern power systems to locally initiated voltage collapse gives rise to a need for methods to measure local voltage security and to predict voltage instability. The paper presents a novel architecture based on a suite of AI technologies and three-dimensional PQV surfaces which provides prediction of local voltage collapse and indices of system voltage security. Robustness and adaptation are demonstrated on difficult and realistic power system simulation models.

KW - Artificial intelligence

KW - Fuzzy kaiman filters

KW - Neuro-fuzzy systems

KW - Voltage stability

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Conceptual designs of AI-based systems for local prediction of voltage collapse

Abstract

Keywords

ASJC Scopus subject areas

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