ランダムフォレストを用いた需要家施設の絶縁監視警報の発報予測手法

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2020/02/01

タイトル(英語): Prediction of Alarm of Insulation Monitoring System on Customer Facility using Random Forest

著者名: 横手　愛（東京理科大学），山口　順之（東京理科大学），加藤　謙晴（関東電気保安協会），鈴木　正美（関東電気保安協会）

著者名(英語): Ai Yokote (Tokyo University of Science), Nobuyuki Yamaguchi (Tokyo University of Science), Kaneharu Kato (Kanto Electrical Safety Services Foundation), Masami Suzuki (Kanto Electrical Safety Services Foundation)

キーワード: 漏れ電流，機械学習，ランダムフォレスト，正解率，決定係数　　leakage current，machine learning，random forest，accuracy，coefficient of determination

要約(英語): In order to cope with the shortage of electrical technicians, it is expected to the improvement of work efficiency by the introduction of advanced technology, such as the Internet of Things (IoT) and Artificial Intelligence (AI). In the safety inspection of electrical facilities, insulation monitoring is expected to systemize correspondence judgment based on data such as measured leakage current.In this study, from the data of the security business core system such as leakage current value measured at the periodic inspection and weather data, we created some models to predict the leakage current measured when the abnormality warning was issued and per customer and the presence or absence of alarms on the next day. The combination of the best explanatory variables makes the model more accurate. Variable importance analysis using Random Forest (RF) was performed to find variables that are important for each objective variable. This analysis shows that the accuracy of the prediction model of the leakage current is the highest when the explanation variable is the data of the security business core system, the weather data, the presence / absence of alarms on the previous day. Other predictive models need further verification. As a result of variable importance analysis, We found out that the leakage current value at the periodic inspection and the time of alarm are important for all purpose variables.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.140 No.2 （2020）　特集：エネルギーデータを対象としたIoT，AI活用技術

本誌掲載ページ: 174-180 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/140/2/140_174/_article/-char/ja/