A good point set-based knowledgebase generation scheme for power system intelligent dynamic security assessment

Yuchen Zhang; Yan Xu; Zhao Yang Dong; Xuekuan Xie; Guo Chen; Chao Lu

doi:10.1049/ic.2015.0228

A good point set-based knowledgebase generation scheme for power system intelligent dynamic security assessment

Yuchen Zhang
, Yan Xu
, Zhao Yang Dong
, Xuekuan Xie
, Guo Chen
, Chao Lu

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

The increased penetration of wind power generation has introduced significant uncertainty and complexity to power system operations, making the real-time dynamic security assessment (DSA) a necessity to protect the system against the risk of blackouts. As a promising strategy for real-time DSA, the intelligent system (IS)-based approach extracts the DSA knowledge from a dynamic security knowledgebase (KB). The quality of KB is the key to the success of such an IS. The conventional Monte Carlo (MC) method requires a large number of sampled cases to cover the operating point (OP) space due to its random sampling mechanism, and therefore is computationally expensive. To generate a KB more efficiently, this paper proposes a Good Point Set (GPS)-based KB generation scheme where the sample distribution is more uniform. To achieve the same level of DSA accuracy, the GPS-based approach requires less number of samples. A case study is conducted on power system with high wind penetration and its result verifies that the GPS method outperforms conventional MC method.

Original language	English
Title of host publication	IET Seminar Digest
Publisher	Institution of Engineering and Technology
Volume	2015
ISBN (Print)	9781785610103, 9781785610523, 9781785610806, 9781785610820, 9781785610943, 9781785611063, 9781785611094, 9781785611148, 9781785611155, 9781785611162, 9781785611179, 9781785611186, 9781785611285, 9781785611315, 9781785611582, 9781785611902, 9781785612190, 9781785612237, 9781849199827, 9781849199841
DOIs	https://doi.org/10.1049/ic.2015.0228
Publication status	Published - 2015
Externally published	Yes
Event	10th International Conference on Advances in Power System Control, Operation and Management, APSCOM 2015 - Hong Kong, China Duration: 8 Nov 2015 → 12 Nov 2015 http://digitallibrary.theiet.org/content/conferences/2015/0008

Conference

Conference	10th International Conference on Advances in Power System Control, Operation and Management, APSCOM 2015
Place	China
City	Hong Kong
Period	8/11/15 → 12/11/15
Internet address	http://digitallibrary.theiet.org/content/conferences/2015/0008

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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10.1049/ic.2015.0228

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@inproceedings{863dec030dce41878b36166c323eb93e,

title = "A good point set-based knowledgebase generation scheme for power system intelligent dynamic security assessment",

abstract = "The increased penetration of wind power generation has introduced significant uncertainty and complexity to power system operations, making the real-time dynamic security assessment (DSA) a necessity to protect the system against the risk of blackouts. As a promising strategy for real-time DSA, the intelligent system (IS)-based approach extracts the DSA knowledge from a dynamic security knowledgebase (KB). The quality of KB is the key to the success of such an IS. The conventional Monte Carlo (MC) method requires a large number of sampled cases to cover the operating point (OP) space due to its random sampling mechanism, and therefore is computationally expensive. To generate a KB more efficiently, this paper proposes a Good Point Set (GPS)-based KB generation scheme where the sample distribution is more uniform. To achieve the same level of DSA accuracy, the GPS-based approach requires less number of samples. A case study is conducted on power system with high wind penetration and its result verifies that the GPS method outperforms conventional MC method.",

author = "Yuchen Zhang and Yan Xu and Dong, \{Zhao Yang\} and Xuekuan Xie and Guo Chen and Chao Lu",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; 10th International Conference on Advances in Power System Control, Operation and Management, APSCOM 2015 ; Conference date: 08-11-2015 Through 12-11-2015",

year = "2015",

doi = "10.1049/ic.2015.0228",

language = "English",

isbn = "9781785610103",

volume = "2015",

booktitle = "IET Seminar Digest",

publisher = "Institution of Engineering and Technology",

address = "United Kingdom",

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}

Zhang, Y, Xu, Y, Dong, ZY, Xie, X, Chen, G & Lu, C 2015, A good point set-based knowledgebase generation scheme for power system intelligent dynamic security assessment. in IET Seminar Digest. vol. 2015, Institution of Engineering and Technology, 10th International Conference on Advances in Power System Control, Operation and Management, APSCOM 2015, Hong Kong, China, 8/11/15. https://doi.org/10.1049/ic.2015.0228

A good point set-based knowledgebase generation scheme for power system intelligent dynamic security assessment. / Zhang, Yuchen; Xu, Yan; Dong, Zhao Yang et al.
IET Seminar Digest. Vol. 2015 Institution of Engineering and Technology, 2015.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A good point set-based knowledgebase generation scheme for power system intelligent dynamic security assessment

AU - Zhang, Yuchen

AU - Xu, Yan

AU - Dong, Zhao Yang

AU - Xie, Xuekuan

AU - Chen, Guo

AU - Lu, Chao

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2015

Y1 - 2015

N2 - The increased penetration of wind power generation has introduced significant uncertainty and complexity to power system operations, making the real-time dynamic security assessment (DSA) a necessity to protect the system against the risk of blackouts. As a promising strategy for real-time DSA, the intelligent system (IS)-based approach extracts the DSA knowledge from a dynamic security knowledgebase (KB). The quality of KB is the key to the success of such an IS. The conventional Monte Carlo (MC) method requires a large number of sampled cases to cover the operating point (OP) space due to its random sampling mechanism, and therefore is computationally expensive. To generate a KB more efficiently, this paper proposes a Good Point Set (GPS)-based KB generation scheme where the sample distribution is more uniform. To achieve the same level of DSA accuracy, the GPS-based approach requires less number of samples. A case study is conducted on power system with high wind penetration and its result verifies that the GPS method outperforms conventional MC method.

AB - The increased penetration of wind power generation has introduced significant uncertainty and complexity to power system operations, making the real-time dynamic security assessment (DSA) a necessity to protect the system against the risk of blackouts. As a promising strategy for real-time DSA, the intelligent system (IS)-based approach extracts the DSA knowledge from a dynamic security knowledgebase (KB). The quality of KB is the key to the success of such an IS. The conventional Monte Carlo (MC) method requires a large number of sampled cases to cover the operating point (OP) space due to its random sampling mechanism, and therefore is computationally expensive. To generate a KB more efficiently, this paper proposes a Good Point Set (GPS)-based KB generation scheme where the sample distribution is more uniform. To achieve the same level of DSA accuracy, the GPS-based approach requires less number of samples. A case study is conducted on power system with high wind penetration and its result verifies that the GPS method outperforms conventional MC method.

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U2 - 10.1049/ic.2015.0228

DO - 10.1049/ic.2015.0228

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781785610103

SN - 9781785610523

SN - 9781785610806

SN - 9781785610820

SN - 9781785610943

SN - 9781785611063

SN - 9781785611094

SN - 9781785611148

SN - 9781785611155

SN - 9781785611162

SN - 9781785611179

SN - 9781785611186

SN - 9781785611285

SN - 9781785611315

SN - 9781785611582

SN - 9781785611902

SN - 9781785612190

SN - 9781785612237

SN - 9781849199827

SN - 9781849199841

VL - 2015

BT - IET Seminar Digest

PB - Institution of Engineering and Technology

T2 - 10th International Conference on Advances in Power System Control, Operation and Management, APSCOM 2015

Y2 - 8 November 2015 through 12 November 2015

ER -

A good point set-based knowledgebase generation scheme for power system intelligent dynamic security assessment

Abstract

Conference

Bibliographical note

UN SDGs

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