Anomaly Detection Using a <scp>SCADA</scp> Feature Extractor and Machine Learning to Detect Lightning Damage on Wind Turbine Blades-Reference-Cited by-同舟云学术

Anomaly Detection Using a SCADA Feature Extractor and Machine Learning to Detect Lightning Damage on Wind Turbine Blades

Published:2022-04-03 Issue:6 Volume:17 Page:945-951
ISSN:1931-4973
Container-title:IEEJ Transactions on Electrical and Electronic Engineering
language:en
Short-container-title:IEEJ Transactions Elec Engng

Author:

Matsui Takuto¹,Yamamoto Kazuo¹,Ogata Jun¹

Affiliation:

1. Electrical and Electronic Engineering (Master's Course) Chubu University 1200 Matsumoto‐Cho Kasugai Aichi Japan

Publisher

Wiley

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/tee.23599

Reference13 articles.

1. “Updraft to offshore wind power” Nikkan kougyou shinbun 2020‐12‐24

2. Ministry of Economy Trade and Industry “New energy power generation facility accident response and structural strength working group” Available online:https://www.meti.go.jp/shingikai/sankoshin/hoan_shohi/denryoku_anzen/newenergy_hatsuden_wg/001.html 2021

3. Ministry of Economy Trade and Industry “Rules to revise part of Interpretation of technical standards for wind power generation facilities” Available online:https://search.e‐gov.go.jp/servlet/PcmFileDownload?seqNo=0000120248 2021

4. Influence of lightning detections and damages on availability of wind turbine generation systems;Kazui H;The Institute of Electrical Engineers of Japan,2019

5. Detection of Lightning Damage on Wind Turbine Blades Using the SCADA System

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2. Latest Trends in Monitoring Technology for Wind Turbine Soundness after Lightning Strikes;IEEJ Transactions on Power and Energy;2024-04-01

3. An Enhanced Interval‐Based Approach for Anomaly Detection in Time Series;IEEJ Transactions on Electrical and Electronic Engineering;2023-03-05