Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

McKinnon, Conor; Carroll, J. Douglas; McDonald, Alasdair; Koukoura, Sofia; Plumley, Charlie

doi:10.3390/en14206601

Cited by 12 publications

(8 citation statements)

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“…McKinnon et al (2021) selected average wind speed, output power, pitch angle and pitch system temperature to detect faults in the hydraulic pitch system. A current signal is then added to detect electrical faults in the pitch system.…”

Section: Application Of Supervisory Control and Data Acquisition Data...mentioning

confidence: 99%

Application of SCADA data in wind turbine fault detection – a review

Yuan

2022

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Purpose With the rapid increase in the number of installed wind turbines (WTs) worldwide, requirements and expenses of maintenance have also increased significantly. The condition monitoring (CM) of WT provides a strong “soft guarantee” for preventive maintenance. The supervisory control and data acquisition (SCADA) system records a huge amount of condition data, which has become an effective means of CM. The main objective of the present study is to summarize the application of SCADA data to fault detection in wind turbines, analyze its advantages and disadvantages and predict the potential of future investigations on the use of SCADA data for fault detection. Design/methodology/approach The authors first review the means of WT CM and summarize the characteristics of CM based on SCADA data. To ensure the quality of SCADA data, data preprocessing methods are analyzed and compared. Then, the failure modes of the key components are discussed and the SCADA data used for fault detection of each component are compared. Moreover, the fault detection methods for WT are classified and a general framework for fault detection is proposed. Finally, the issues in the WT fault detection method based on SCADA data are reviewed. Findings Based on the performed analyses, it is found that although the fault detection accuracy based on SCADA data is relatively poor, it has low capital expenses and low computational cost. More specifically, when there is scarce fault data, the normal SCADA data can be used to detect the fault time. However, the specific fault type cannot be identified in this way. When a large amount of fault data are accumulated in the SCADA system, it can not only detect the occurrence time of the fault but also identify the specific fault type. Originality/value The main contribution of the present study is to summarize the pre-processing methods for SCADA data, the data required for fault detection of key components and the characteristics of the fault detection model. Then we propose a general fault detection framework for wind turbines based on SCADA data, where the maintenance workers can choose the appropriate fault detection method according to different fault detection requirements and data resources. This article is expected to provide guidance for fault detection based on time-series sensor signals and be of interest to researchers, maintenance workers and managers.

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Section: Application Of Supervisory Control and Data Acquisition Data...mentioning

confidence: 99%

Application of SCADA data in wind turbine fault detection – a review

Yuan

2022

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“…Wei et al detected faults in encoders and pitch motors based on an improved RVM neural network algorithm and SCADA data (Wei et al, 2020). McKinnon et al used the isolated forest machine learning method to process SCADA data for pitch system fault predicting (McKinnon et al, 2021). Korkos et al used ANFIS technology to analyze 10-year SCADA data and established a fault detection model for the pitch system (Korkos et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Research on force model and characteristics of large wind turbine pitch system based on SCADA data

Dai

Zhang

et al. 2023

Front. Energy Res.

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In large-scale wind turbines, the force state of the pitch system greatly influences safe operation and service life. This paper provides a novel method to estimate blade pitch load, bearing friction torque, and motor pitch torque. In this method, the force equilibrium equations are established by investigating the force of the pitch system under multiple operating conditions. The multidimensional BIN method is employed to classify the supervisory control and data acquisition (SCADA) data of wind turbines into several intervals. The multidimensional scatter data is processed in a single-valued way. Then, the estimating model of the pitch system forces is established by combining the obtained data and the equilibrium equations. Taking a 2 MW wind turbine as an example, the variation characteristics of blade pitch load, bearing friction torque, and motor pitch torque under multiple operating conditions are analyzed. Some interesting and valuable conclusions are obtained. For example, when the wind speed increases, the blade pitch load increases significantly in the maximum wind energy tracking region, but there is no obvious change in the observed constant power output region. The wind speed and azimuth have little effect on the bearing friction torque. The variation trend of motor pitch torque is consistent with that of blade pitch load in the maximum wind energy tracking region.

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“…In the SCADA data-driven performance assessments, machine learning based approaches were the most investigated. The majority part of researches were focused on the fault diagnosis of critical components of WTs, such as generator [5,12], gearbox [3,5,13], pitch system [14,15,16] and blades [17]. Few studies were focused on the health evaluation of WTs and additionally the health evaluation was mostly based on output power evaluation.…”

Section: Introductionmentioning

confidence: 99%

Health assessment of wind turbines based on output power modelling

Yin

2023

Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022)

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Machine learning based performance assessment of wind turbines using the widely available SCADA data has been receiving growing interest. This work proposes a pragmatic health evaluation approach of wind turbines based on output power modelling. For that, random forests regression models to predict output power are trained respectively by ‘normal’ and ‘under-performance’ samples. The trained two models are used to predict output power of testing data. By comparing the absolute values of the residuals of testing data, the working state is identified at each time point. A health index, which is based on the percentiles of ‘normal’ samples in a defined period, is introduced to evaluate the health condition of the wind turbine. The degradation of the HI can give an early warning of the wind turbine. The proposed approach successfully give an early warning for the potential fault around 6 hours prior the traditional SCADA system.

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Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

Cited by 12 publications

References 50 publications

Application of SCADA data in wind turbine fault detection – a review

Application of SCADA data in wind turbine fault detection – a review

Research on force model and characteristics of large wind turbine pitch system based on SCADA data

Health assessment of wind turbines based on output power modelling

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