ニューラルネットによる高調波負荷の分類(電力・エネルギー分野におけるニューラルネットワーク応用<特集>)

• 資料種別: 記事
• タイトル: ニューラルネットによる高調波負荷の分類
• 著者・編者: 植松 博; 森 啓之; 都築 旋二
• シリーズタイトル: 電力・エネルギー分野におけるニューラルネットワーク応用<特集>
• 著者標目: 植松 博; 森 啓之; 都築 旋二
• タイトル（掲載誌）: 電気学会論文誌. B, 電力・エネルギー部門誌 = IEEJ transactions on power and energy
• 巻号年月日等（掲載誌）: 111(7) 1991.07
• 掲載巻: 111
• 掲載号: 7
• 掲載ページ: p757～763
• 掲載年月日（W3CDTF）: 1991-07
• ISSN（掲載誌）: 0385-4213
• ISSN-L（掲載誌）: 0385-4213
• 出版事項（掲載誌）: 東京 : 電気学会
• 出版地（国名コード）: JP
• 本文の言語コード: jpn
• NDLC: ZN31
• 対象利用者: 一般
• 所蔵機関: 国立国会図書館
• 請求記号: Z16-794
• 連携機関・データベース: 国立国会図書館 : 国立国会図書館雑誌記事索引; https://ndlsearch.ndl.go.jp
• 書誌ID（NDLBibID）: 3730240; http://id.ndl.go.jp/bib/3730240
• 整理区分コード: 632

• 要約等: This paper presents an artificial neural-net based method for classifying harmonic loads in power distribution systems. The method is used to identify nonlinear relationship between harmonic loads and harmonic currents that vary from time to time. In recent years, nonlinear loads increase due to advanced technologies in power electronics applications. As a result, it is afraid that the harmonic distortion brings about several problems in power transmission and distribution systems. It is necessary to identify the harmonic loads and take an appropriate strategy so that the harmonic distortion is alleviated. However, this identification problem has not been studied so far due to the complex characteristics. The objective of this paper is to identify the nonlinear relationship between harmonic currents and types of harmonic loads as the first stage to detect harmonic sources. In this paper, a three-layered feedforward perceptron is utilized to classify harmonic loads. The neural network is effective for identifying nonlinear problems that have been hard to solve with the conventional methods. The weights between neurons are determined by the backpropagation algorithm. The proposed method has been successfully applied to several sample harmonic loads.
• DOI: 10.1541/ieejpes1990.111.7_757
• オンライン閲覧公開範囲: インターネット公開
• 連携機関・データベース: 科学技術振興機構 : J-STAGE; http://www.jstage.jst.go.jp

• 要約等: This paper presents an artificial neural-net based method for classifying harmonic loads in power distribution systems. The method is used to identify nonlinear relationship between harmonic loads and harmonic currents that vary from time to time. In recent years, nonlinear loads increase due to advanced technologies in power electronics applications. As a result, it is afraid that the harmonic distortion brings about several problems in power transmission and distribution systems. It is necessary to identify the harmonic loads and take an appropriate strategy so that the harmonic distortion is alleviated. However, this identification problem has not been studied so far due to the complex characteristics. The objective of this paper is to identify the nonlinear relationship between harmonic currents and types of harmonic loads as the first stage to detect harmonic sources. In this paper, a three-layered feedforward perceptron is utilized to classify harmonic loads. The neural network is effective for identifying nonlinear problems that have been hard to solve with the conventional methods. The weights between neurons are determined by the backpropagation algorithm. The proposed method has been successfully applied to several sample harmonic loads.
• DOI: 10.1541/ieejpes1990.111.7_757; https://doi.org/10.1541/ieejpes1990.111.7_757
• オンライン閲覧公開範囲: インターネット公開
• 関連情報（URI）: http://id.ndl.go.jp/bib/3730240; https://ndlsearch.ndl.go.jp/books/R000000004-I3730240; https://www.jstage.jst.go.jp/article/ieejpes1990/111/7/111_7_757/_pdf
• 連携機関・データベース: 国立情報学研究所 : CiNii Research; https://cir.nii.ac.jp/
• 提供元機関・データベース: Japan Link Center; https://japanlinkcenter.org/top; 雑誌記事索引データベース; https://ndlsearch.ndl.go.jp; Crossref; https://www.crossref.org; CiNii Articles
• 書誌ID（NDLBibID）: 3730240; http://id.ndl.go.jp/bib/3730240
• NII論文ID: 130006840791