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| 講演名 | 2026-03-02 10:10 Advanced Real-Time Anomaly Detection in Power Grid Networks State Estimation Using Graph Signal Processing Techniques. ○SadaTukur・Shogo Muramatsu（Niigata Univ.）  EA2025-79 SIP2025-99 SP2025-32 |
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| 抄録 | （和） | This study proposes Grid-GSP methods for data from the electrical power grid. Modern electrical power grids face unprecedented challenges in maintaining reliability and stability due to increasing complexity, distributed generation, and dynamic load patterns. This paper investigates the application of Graph Signal Processing (GSP) techniques to enhance the reliability and stability of electrical power grids. We formulate the power grid as a weighted graph where nodes represent busses and edges represent transmission lines, with electrical quantities treated as graph signals. This report presents three main contributions: (1) a GSP-based real-time monitoring framework using graph spectral analysis for anomaly detection, (2) a graph filter design approach for stability enhancement, and (3) applications to the Nigerian power grid. Extensive numerical simulations demonstrate that the proposed GSP techniques achieve 97.3% precision in identifying grid disturbances with 83% faster detection times, reduce frequency oscillations by 72% and improve transient stability margins by 88%. These results establish GSP as a powerful paradigm for next-generation power grid management, offering scalability, computational efficiency and theoretical rigor. |
| （英） | This study proposes Grid-GSP methods for data from the electrical power grid. Modern electrical power grids face unprecedented challenges in maintaining reliability and stability due to increasing complexity, distributed generation, and dynamic load patterns. This paper investigates the application of Graph Signal Processing (GSP) techniques to enhance the reliability and stability of electrical power grids. We formulate the power grid as a weighted graph where nodes represent busses and edges represent transmission lines, with electrical quantities treated as graph signals. This report presents three main contributions: (1) a GSP-based real-time monitoring framework using graph spectral analysis for anomaly detection, (2) a graph filter design approach for stability enhancement, and (3) applications to the Nigerian power grid. Extensive numerical simulations demonstrate that the proposed GSP techniques achieve 97.3% precision in identifying grid disturbances with 83% faster detection times, reduce frequency oscillations by 72% and improve transient stability margins by 88%. These results establish GSP as a powerful paradigm for next-generation power grid management, offering scalability, computational efficiency and theoretical rigor. | |
| キーワード | （和） | Graph signal processing / Power grid stability / Phasor measurement unit / Graph spectral analysis / Anomaly detection / Voltage stability / Frequency control / |
| （英） | Graph signal processing / Power grid stability / Phasor measurement unit / Graph spectral analysis / Anomaly detection / Voltage stability / Frequency control / | |
| 文献情報 | 信学技報, vol. 125, no. 370, SIP2025-99, pp. 37-42, 2026年3月. | |
| 資料番号 | SIP2025-99 | |
| 発行日 | 2026-02-23 (EA, SIP, SP) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| 著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します．(許諾番号：10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| PDFダウンロード | EA2025-79 SIP2025-99 SP2025-32 |
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| 研究会情報 | |
| 研究会 | SP EA SIP IPSJ-SLP |
| 開催期間 | 2026-03-02 - 2026-03-04 |
| 開催地（和） | 沖縄県青年会館 |
| 開催地（英） | |
| テーマ（和） | 音声，応用／電気音響，信号処理，一般 |
| テーマ（英） | |
| 講演論文情報の詳細 | |
| 申込み研究会 | SIP |
| 会議コード | 2026-03-SP-EA-SIP-SLP |
| 本文の言語 | 英語 |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | Advanced Real-Time Anomaly Detection in Power Grid Networks State Estimation Using Graph Signal Processing Techniques. |
| サブタイトル（英） | |
| キーワード(1)（和/英） | Graph signal processing / Graph signal processing |
| キーワード(2)（和/英） | Power grid stability / Power grid stability |
| キーワード(3)（和/英） | Phasor measurement unit / Phasor measurement unit |
| キーワード(4)（和/英） | Graph spectral analysis / Graph spectral analysis |
| キーワード(5)（和/英） | Anomaly detection / Anomaly detection |
| キーワード(6)（和/英） | Voltage stability / Voltage stability |
| キーワード(7)（和/英） | Frequency control / Frequency control |
| キーワード(8)（和/英） | / |
| 第1著者 氏名（和/英/ヨミ） | SadaTukur / SadaTukur / |
| 第1著者 所属（和/英） | Niigata University (略称： Niigata Univ.) Niigata University (略称： Niigata Univ.) |
| 第2著者 氏名（和/英/ヨミ） | Shogo Muramatsu / Shogo Muramatsu / |
| 第2著者 所属（和/英） | Niigata University (略称： Niigata Univ.) Niigata University (略称： Niigata Univ.) |
| 講演者 | 第1著者 |
| 発表日時 | 2026-03-02 10:10:00 |
| 発表時間 | 20分 |
| 申込先研究会 | SIP |
| 資料番号 | EA2025-79, SIP2025-99, SP2025-32 |
| 巻番号（vol） | vol.125 |
| 号番号（no） | no.369(EA), no.370(SIP), no.371(SP) |
| ページ範囲 | pp.37-42 |
| ページ数 | 6 |
| 発行日 | 2026-02-23 (EA, SIP, SP) |
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