Fault detection and isolation for wind turbine electric pitch system

Zhu, Jianguo; Ma, Kuichao; Hajizadeh, Amin; Soltani, Mohsen; Chen, Zhe

doi:10.1109/peds.2017.8289226

Cited by 9 publications

(9 citation statements)

References 10 publications

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“…It could detect at least 37 h ahead of SCADA alarms in 8 different fault cases. Zhu et al [17] use an extended Kalman Filter-based multiple model adaptive estimation system to estimate the states of the system from a turbine model. This was done to detect the specific fault types-to differentiate between an actuator or sensor fault.…”

Section: Pitch System Condition Monitoringmentioning

confidence: 99%

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

et al. 2021

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Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed.

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Section: Pitch System Condition Monitoringmentioning

confidence: 99%

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

et al. 2021

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“…Bi et al [16] proposed a method for alerting pitch failures caused by pitch controller fault and slip ring contamination using the technical parameters of the wind turbines and computer-based simulation study. Zhu [17] described a fault detection method for an observer based on an extended Kalman filter design, which is mainly for detecting pitch actuator and sensor faults. Wu et al [18] developed an observer-based multi-innovation stochastic gradient algorithm (O-MISG) used for diagnosing the pitch system faults.…”

Section: %mentioning

confidence: 99%

Research on Fault Detection for Three Types of Wind Turbine Subsystems Using Machine Learning

et al. 2020

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In wind power generation, one aim of wind turbine control is to maintain it in a safe operational status while achieving cost-effective operation. The purpose of this paper is to investigate new techniques for wind turbine fault detection based on supervisory control and data acquisition (SCADA) system data in order to avoid unscheduled shutdowns. The proposed method starts with analyzing and determining the fault indicators corresponding to a failure mode. Three main system failures including generator failure, converter failure and pitch system failure are studied. First, the indicators data corresponding to each of the three key failures are extracted from the SCADA system, and the radar charts are generated. Secondly, the convolutional neural network with ResNet50 as the backbone network is selected, and the fault model is trained using the radar charts to detect the fault and calculate the detection evaluation indices. Thirdly, the support vector machine classifier is trained using the support vector machine method to achieve fault detection. In order to show the effectiveness of the proposed radar chart-based methods, support vector regression analysis is also employed to build the fault detection model. By analyzing and comparing the fault detection accuracy among these three methods, it is found that the fault detection accuracy by the models developed using the convolutional neural network is obviously higher than the other two methods applied given the same data condition. Therefore, the newly proposed method for wind turbine fault detection is proved to be more effective.

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“…In response to the unbalanced sample categories and complex sample distribution of the operating data of the pitch system of the WT generator system, Tang et al [13] proposed a method for the fault detection of the pitch system of the WT generator system based on the multiclass optimal margin distribution machine. Zhu et al [14] proposed multiple model adaptive estimation model based on an extended Kalman filter to estimate the states of the pitch system. Fang et al [15] proposed a neural network-based condition assessment method for the pitch system based on the characteristic parameters related to the operation status of the pitch system extracted from the SCADA system.…”

Section: Introductionmentioning

confidence: 99%

Operation State of the Wind Turbine Pitch System Based on Fuzzy Comprehensive Evaluation

Zhu¹,

Zhao²,

Gui³

et al. 2023

Energy Engineering

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To ensure wind turbine (WT) safe operations and improve the utilization rate of wind energy, effective evaluation of the operation state of the pitch system is critical. Therefore, a new method was proposed to evaluate the operation state of the pitch system of WT based on fuzzy comprehensive evaluation. Firstly, based on SCADA data, the working state of the pitch system under rated power state and power state of WT were analyzed. Secondly, through the analysis of characteristic parameters and physical mechanism of the pitch system, the consistency principle of characteristic parameters, the stability principle of power under rated state, and the stability principle of blade angle underpowered state were obtained. Next, based on the aforementioned principles, the evaluation indexes were established, and the fuzzy comprehensive evaluation method was used to establish the operation state evaluation model of the pitch system under rated power state and under power state of the WT. Finally, an example was provided to verify the effectiveness of the method. The evaluation model established in this study can be used as a technical reference for the online monitoring of WT pitch systems to ensure the safe and stable operation of WTs.

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Fault detection and isolation for wind turbine electric pitch system

Cited by 9 publications

References 10 publications

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

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

Research on Fault Detection for Three Types of Wind Turbine Subsystems Using Machine Learning

Operation State of the Wind Turbine Pitch System Based on Fuzzy Comprehensive Evaluation

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