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

Matsui, Takuto; Yamamoto, Kazuo; Sumi, Satoshi; Triruttanapiruk, Nawakun

doi:10.1109/tpwrd.2020.2992796

Cited by 24 publications

(13 citation statements)

References 1 publication

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“…The features of the anomaly detection model are extracted from the SCADA data based on a previous study [5]. First, the wind speed and rotational speed at which the most noticeable anomaly appeared are extracted as basic features.…”

Section: Feature Extractormentioning

confidence: 99%

“…In this study, the simple moving average (SMA), weighted moving average (WMA), exponentially weighted moving average (EWMA), and sine weighted moving average (SWMA) are used. Each moving average is shown in (2) to (5). Note that x i denotes the i th data.…”

Section: Moving Average Of Wind Speed and Rotational Speedmentioning

confidence: 99%

“…We propose a method that can confirm the soundness of the blade after a lightning strike using supervisory control and data acquisition system data (SCADA data) [5]. This study proposes the use of parameters such as wind speed, the blade's rotational speed, and power, as features of the wind turbine blade anomaly detection model.…”

Section: Introductionmentioning

confidence: 99%

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

Matsui

Yamamoto

Ogata

2022

IEEJ Transactions Elec Engng

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Many wind turbines in Japan are damaged by lightning strikes. In particular, blades that are already damaged by lightning strikes are further damaged by continuous blade rotation. To prevent such additional damage, an emergency stop device that is triggered by lightning detection is required to be installed on wind turbines in winter lightning areas. Normally, the wind turbine restarts after soundness is confirmed by inspection. However, it is often difficult to inspect the turbine visually because of bad weather, and consequently, the wind turbine downtime is prolonged. This type of downtime is a reason for the reduced availability of wind turbines. Therefore, in this study, we consider technologies that would allow quick restart of wind turbines and improve availability, based on understanding the soundness of blades after a lightning strike, using a machine-learning model based on supervisory control and data acquisition system data.

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Section: Feature Extractormentioning

confidence: 99%

Section: Moving Average Of Wind Speed and Rotational Speedmentioning

confidence: 99%

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

Matsui

Yamamoto

Ogata

2022

IEEJ Transactions Elec Engng

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“…It was clarified in previous research [17] that, if a wind turbine blade is damaged by a lightning strike, the outliers will occur frequently and continuously in wind speed rotational speed characteristic, wind speed power characteristic, and wind speed pitch degree characteristis of SCADA data. Based on this result, anomaly detection is performed using the wind speed rotational speed characteristic, which show the most outliers.…”

Section: Scada Data Of Wind Turbine At the Accident Due To Lightning Strikementioning

confidence: 99%

“…However, this method aims to predict failure due to deterioration over time from months to weeks in advance, and it is not applicable to sudden damage such as lightning damage. Therefore, our research group proposed the method that can immediately detect blade anomalies caused by a sudden event such as a lightning strike [17]. Accordingly, it is necessary to continue to consider the optimal anomaly detection model and effective features based on this result.…”

Section: Introductionmentioning

confidence: 99%

Study on Improvement of Lightning Damage Detection Model for Wind Turbine Blade

2021

Self Cite

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There have been many reports of damage to wind turbine blades caused by lightning strikes in Japan. In some of these cases, the blades struck by lightning continue to rotate, causing more serious secondary damage. To prevent such accidents, it is a requirement that a lightning detection system is installed on the wind turbine in areas where winter lightning occurs in Japan. This immediately stops the wind turbine if the system detects a lightning strike. Normally, these wind turbines are restarted after confirming soundness of the blade through visual inspection. However, it is often difficult to confirm the soundness of the blade visually for reasons such as bad weather. This process prolongs the time taken to restart, and it is one of the causes that reduces the availability of the wind turbines. In this research, we constructed a damage detection model for wind turbine blades using machine learning based on SCADA system data and, thereby, considered whether the technology automatically confirms the soundness of wind turbine blades.

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Risk-based maintenance strategy selection for wind turbine composite blades

Lopez

Kolios

2022

Energy Reports

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

Cited by 24 publications

References 1 publication

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

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

Study on Improvement of Lightning Damage Detection Model for Wind Turbine Blade

Risk-based maintenance strategy selection for wind turbine composite blades

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