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

^*この研究の対応する著者

研究成果: Article › 査読

29 被引用数 (Scopus)

抄録

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.

本文言語	English
ページ（範囲）	137-145
ページ数	9
ジャーナル	IEEE Transactions on Power Systems
巻	11
号	1
DOI	https://doi.org/10.1109/59.485995
出版ステータス	Published - 1996
外部発表	はい

ASJC Scopus subject areas

エネルギー工学および電力技術
電子工学および電気工学

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10.1109/59.485995

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

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AU - Chiang, K. H.

AU - Khedkar, P. S.

AU - Leonard, D. J.

AU - Miller, N. W.

PY - 1996

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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

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