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Pitch system can adjust the blade’s pitch angle, control the aerodynamic torque and capture the wind turbine’s aerodynamic power. It is critical guarantee for the turbine’s safety and stability. However, the turbine’s operating environment is harsh and the long-term component abrasion may cause the system failure. Only by the monitoring signals’ instantaneous value, it is difficult to discovery the system fault. Therefore, this paper fuses multiple time sequence signals and uses the multi-channel attention mechanism to improve the long short-time memory networks (MCA-LSTM) for pitch system fault diagnosis. Firstly, the data of SCADA is preprocessed to construct the time sequence ratio vectors and input into MCA-LSTM. Through the attention mechanism, the time sequences ratio vectors are integrated from different channels in MCA-LSTM. Then the MCA-LSTM establishes the logical relationship between the signals and the fault features. Finally, root-mean-square error and the residual fluctuation threshold are used to identify the abnormal states. The experiments utilized the SCADA data of wind farms. The results show that MCA-LSTM can detect the pitch bearing anomalies and hub faults, which are about ten hours earlier than the wind farm fault recording. So, MCA-LSTM can effectively predict pitch system faults and provides an important basis for the WT fault diagnosis.

When the wind turbine pitch system is in operation, due to the strong coupling of the internal structure of the system, it is difficult to accurately locate the fault only relying on prior knowledge. And when using the data-driven contribution graph method for fault location, due to the influence of the fault variable, the contribution value of the non-fault variable will become larger, which will cause a tailing effect and misdiagnosis. In this paper, a multiblock kernel principal component algorithm (MBKPCA) is proposed. In this algorithm, the variables of the pitch system operation process are divided into several variable blocks based on the historical fault data and perform faults based on the kernel principal component algorithm for each variable block diagnosis. Taking historical data of an area in North China as an actual calculation example, the proposed block kernel principal component analysis algorithm is compared with the traditional kernel principal component analysis algorithm. The results show that the MBKPCA can effectively reduce the “tailing effect” of the fault variable on the non-fault variable when the contribution graph method is used to identify the fault source, and the method can accurately locate the fault source and achieves higher detection accuracy.

This article describes the development and application of a model-based scheme for detecting the early signs of coil failure in solenoid valves. Contrary to other works, the proposed method simply isolates the fault-induced change in the coil resistance from thermal effects and provides a cost-effective solution with no comprehensive equipment demand. The model-based detection method employs a simple thermal model of the solenoid and an extended Kalman filter (EKF) for generating coil current residuals. The EKF utilizes measurements of coil voltage, coil current, ambient, and fluid temperature. The CUmulative SUM of the current residual is found to be sensitive to the early signs of coil failure. An experimental setup is utilized for evaluating the robustness of the detection method in the event of changing ambient temperature, convection, and fluid flow conditions. Results show that the method is able to detect the early signs of coil failure, and robust behavior and high detection probabilities are achieved for realistic fault sizes. The realistic fault sizes and the number of fault instances before failure are determined through extensive accelerated tests, where solenoids are operated at high temperature until failure. [ABSTRACT FROM AUTHOR]

Wind turbine pitch misalignments provoke aerodynamic asymmetries which cause severe damage to the turbine. Hence, it is of interest to develop fault tolerant strategies to cope with pitch misalignments. Fault tolerant strategies require the information regarding the diagnosis and the estimation of the faults. However, most existing works focus only on open-loop misalignment diagnosis and do not provide robust fault estimates. In this work, we present a novel strategy to both estimate and diagnose pitch misalignments. The proposed strategy is developed at a wind farm level and it exploits altogether the information provided by the temporal and spatial relations of the turbines in the farm. Fault estimation is first addressed with a closed-loop switched observer. This observer is robust against disturbances and it adapts to the varying conditions along the wind turbine operation range. Fault diagnosis is then achieved via statistical-based decision mechanisms with adaptive thresholds. Both the observer and the decision mechanisms are designed to guarantee the desired performance. Introducing some restrictions over the number of simultaneous faulty turbines in the farm, the proposed approach is ameliorated via a bank of the aforementioned observers and decision mechanisms. Finally, the strategies are tested using a well-known wind farm benchmark. • A switched observer is proposed to estimate pitch misalignments. • Common engineering performance parameters are utilized in the fault estimator design. • The pitch misalignment estimates are used in a statistical-based fault diagnosis scheme. • Wind farm benchmark simulations validate the approach for low and high wind speeds. [ABSTRACT FROM AUTHOR]

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.

In order to reduce operation and maintenance cost and improve fault diagnosis and detection accuracy for wind turbines, a study on advanced methods has been carried out. The purpose of this paper is to present a new method developed using radar chart and support vector machine (SVM) approach for fault diagnosis and prediction of wind turbine pitch system as it usually has a higher failure rate. In the study, the supervisory control and data acquisition (SCADA) system data are utilized as source data for SVM prediction. First of all, the characteristics of the indicator variable data collected by the SCADA system are analyzed, and the radar charts corresponding to the normal and faulty operation of the wind turbine pitch system are constructed using the indicator variable data. Secondly, the SVM method is used to extract the gray-level co-occurrence matrix (GLCM) features and histogram of oriented gradients (HOG) features of the radar charts, and the SVM classifier is trained. Then, the operational status is predicted, the classification effect is evaluated by the confusion matrix, and the prediction evaluation index is calculated. Thirdly, the support vector regression method is used to analyze the SCADA indicator variable data, the input and output of the regression model are determined, and the training prediction model is established, and the prediction accuracy of the test model is analyzed using the test sample data. Finally, the forecasting evaluation indexes obtained by the above two methods are compared. It proves that the proposed method using SVM to analyze the system radar charts has a higher prediction accuracy of 91.24% than the support vector regression method. The prediction accuracy is improved by 8.6%. Hence, it is verified that the new method using a radar chart and SVM approach has superiority over the support vector regression method.

This paper describes the development and application of a signal-based fault detection method for identifying gas leakage in hydraulic accumulators used in wind turbines. The method uses Multiresolution Signal Decomposition (MSD) based on wavelets for feature extraction from a single fluid pressure measurement located close to the accumulator. Gas leakage is shown to create increased variations in this pressure signal. The Root Mean Square (RMS) of the detail coefficient Level 9 from the MSD is found as the most sensitive and robust fault indicator of gas leakage. The method is verified on an experimental setup allowing for the replication of the conditions for accumulators in wind turbines. Robustness is tested in a multi-fault environment where gas and external fluid leakage occurs simultaneously. In total, 24 experiments are performed, which show that the method is sensitive to gas leakage in the desired range and can be isolated from external fluid leakage. Additionally, the robustness to other operating conditions, such as wind speeds between rated and cut-off, turbulence intensity and ambient temperature is evaluated via simulations of a pitch system in a wind turbine using the Fatigue, Aerodynamics, Structures and Turbulence program (FAST). Simulation shows that robustness is affected at low ambient temperatures, however, detection is permitted in the range of 22-60 °C. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In order to reduce operation and maintenance cost and improve fault diagnosis and detection accuracy for wind turbines, a study on advanced methods has been carried out. The purpose of this paper is to present a new method developed using radar chart and support vector machine (SVM) approach for fault diagnosis and prediction of wind turbine pitch system as it usually has a higher failure rate. In the study, the supervisory control and data acquisition (SCADA) system data are utilized as source data for SVM prediction. First of all, the characteristics of the indicator variable data collected by the SCADA system are analyzed, and the radar charts corresponding to the normal and faulty operation of the wind turbine pitch system are constructed using the indicator variable data. Secondly, the SVM method is used to extract the gray-level co-occurrence matrix (GLCM) features and histogram of oriented gradients (HOG) features of the radar charts, and the SVM classifier is trained. Then, the operational status is predicted, the classification effect is evaluated by the confusion matrix, and the prediction evaluation index is calculated. Thirdly, the support vector regression method is used to analyze the SCADA indicator variable data, the input and output of the regression model are determined, and the training prediction model is established, and the prediction accuracy of the test model is analyzed using the test sample data. Finally, the forecasting evaluation indexes obtained by the above two methods are compared. It proves that the proposed method using SVM to analyze the system radar charts has a higher prediction accuracy of 91.24% than the support vector regression method. The prediction accuracy is improved by 8.6%. Hence, it is verified that the new method using a radar chart and SVM approach has superiority over the support vector regression method. [ABSTRACT FROM AUTHOR]