Pitch System Fault Research Articles

Research on Wind Turbine Fault Detection Based on CNN-LSTM

With the wide application of wind energy as a clean energy source, to cope with the challenge of increasing maintenance difficulty brought about by the development of large-scale wind power equipment, it is crucial to monitor the operating status of wind turbines in real time and accurately identify the specific location of faults. In this study, a CNN-LSTM-based wind motor fault detection model is constructed for four types of typical faults, namely gearbox faults, electrical faults, yaw faults, and pitch faults of wind motors, combining CNN’s advantages of excelling in feature extraction and LSTM’s advantages of dealing with long-time sequence data, to achieve the simultaneous detection of multiple fault types. The accuracy of the CNN-LSTM-based wind turbine fault detection model reaches 90.06%, and optimal results are achieved for the effective discovery of yaw system faults, pitch system faults, and gearbox faults, obtaining 94.09%, 96.46%, and 97.39%, respectively. The CNN-LSTM wind turbine fault detection model proposed in this study improves the fault detection effect, avoids the further deterioration of faults, provides direction for preventive maintenance, reduces downtime loss due to restorative maintenance, and is essential for the sustainable use of wind turbines and maintenance of wind turbine service life, which helps to improve the operation and maintenance level of wind farms.

Hard sample mining-enabled supervised contrastive feature learning for wind turbine pitch system fault diagnosis

Abstract The presence of multiple failure severities in the wind turbine pitch system due to the long-term wear and tear poses challenges in accurately classifying the system’s health condition, thus increasing maintenance costs or damage risks. This paper proposes a novel method based on hard sample mining (HSM)-enabled supervised contrastive learning to address this problem. The proposed method leverages the powerful feature extraction capabilities of supervised contrastive learning to extract discriminative features from highly imbalanced data. Additionally, the method incorporates a cosine similarity-based HSM framework that constructs hard sample pairs within mini-batches during both the representation learning and classifier training phases, thereby improving the diagnostic performance of the model for hard samples. The proposed method achieves macro G-mean of 0.9991 and 0.9971 on two real datasets containing data on wind turbine pitch system cog belt fractures. These results indicate significantly superior fault diagnosis performance compared to existing methods, highlighting its potential for enhancing the reliability and efficiency of fault diagnosis in wind turbine pitch systems.

On the effects of pitch system faults on a wind turbine

In this study, the hydraulic pitch system of the DTU 10 MW reference wind turbine has been modelled, including different component-level faults, and the control algorithms have been modified to influence the effect of these faults on loads, production and robustness. The simulation results show that the performance of the pitch system model is similar to others in the literature, while at the same time simulating the hydraulic supply pressure variations. The results also show the importance of these variations for overall turbine performance. The number of safety events due to low supply pressure increases with the severity of internal hydraulic leaks. This can be mitigated with a control strategy which limits negative pitch rates based on accumulator pressure. The results show that this can preserve up to 100% of power production in the face of very severe pitch system faults, with as little as 0% impact on loads.

Hierarchical spatial–temporal autocorrelation graph neural network for online wind turbine pitch system fault detection

The advancement of low-cost, non-manually labeled big data technologies for fault detection in wind turbines is crucial to guarantee their safe and efficient operation. In this context, Normal Behavior Modelling (NBM) is a prevalent approach. However, existing NBM methods for wind turbine fault detection inadequately utilize complex spatial–temporal correlations within the Supervisory Control and Data Acquisition (SCADA) during the normal behavior prediction stage. To cope with this limitation, this paper propose a hierarchical spatial-temporal autocorrelation graph neural network method for online wind turbine pitch system fault detection. This approach integrates a dynamic graph network with an adaptive structure for spatial correlation learning and a transformer model with autocorrelation attention to identify periodic time-distance correlations. Furthermore, a hierarchical neural network, specifically designed for wind turbine physical structure, enhances local information extraction through seven latent graph neural networks and a probsparse attention mechanism. The proposed method demonstrates significant effectiveness in real-world applications, as evidenced by data from a Shanghai wind farm.

Fault diagnosis of wind turbine pitch system based on LSTM with multi-channel attention mechanism

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.

Influence of mechanical faults in pitch, brake, controller systems on fragility of offshore wind turbine under aero-hydro loadings

Offshore wind turbines (OWTs) have undergone unprecedented development in recent years, and during their service life the control and mechanical systems faults have emerged one of the primary reasons to cause downtime of OWTs. This paper addresses the impact of mechanical faults in control systems on the structural damage risk under wind and wave loadings. Numerical simulations implemented by a combination of finite element simulation software are conducted to simulate nonlinear behaviors of support structural system with and without mechanical faults. Incremental dynamic analyses are carried out for three cases: (1) intact OWTs, (2) OWTs with only pitch system fault, and (3) OWTs with simultaneous faults at pitch and brake systems. The IDA results and structural damage risks of blades and support structural system under different cases are comprehensively analyzed and discussed. The results indicated that the pitch system fault results in a minor increase in the damage risk of OWTs in parked condition, while the presence of both pitch and brake systems fault or controller fault leads to a significant rise in the structural risk and even structural failure due to tower buckling and blade-tower strike.

Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators

Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators

Wind Turbine Pitch System Fault Detection Using ssODM-DSTA

A fault detection method of wind turbine pitch system using semi-supervised optimal margin distribution machine (ssODM) optimized by dynamic state transition algorithm (DSTA) [ssODM-DSTA] was proposed to solve the problem of obtaining the optimal hyperparameters of the fault detection model for the pitch system. This method was adopted to input the three hyperparameters of the ssODM into the dynamic state transition algorithm in the form of a three-dimensional vector to obtain the global optimal hyperparameters of the model, thus improving the performance of the fault detection model. Using a random forest to rank the priority of features of the pitch system fault data, the features with large weight proportions were retained. Then, the Pearson correlation method is used to analyze the degree of correlation among features, filter redundant features, and reduce the scale of features. The dataset was divided into a training dataset and a test dataset to train and test the proposed fault detection model, respectively. The real-time wind turbine pitch system fault data were collected from domestic wind farms to carry out fault detection experiments. The results have shown that the proposed method had a positive fault rate (FPR) and fault negative rate (FNR), compared with other optimization algorithms.

Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF.

It is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved grey wolf algorithm (IGWO-ERF) was proposed to solve the problems mentioned above. First, IGWO-ERF imports the Cosine model to nonlinearize the linearly changing convergence factor α to balance the global exploration and local exploitation capabilities of the algorithm. Then, in the later stage of the algorithm iteration, α wolf generates its mirror wolf based on the lens imaging learning strategy to increase the diversity of the population and prevent local optimum of the population. The electric pitch system fault detection method of the wind turbine generator set sets the generator power of the variable pitch system as the main state parameter. First, it uses the Pearson correlation coefficient method to eliminate the features with low correlation with the electric pitch system generator power. Then, the remaining features are ranked by the importance of the RF features. Finally, the top N features are selected to construct the electric pitch system fault data set. The data set is divided into a training set and a test set. The training set is used to train the proposed fault detection model, and the test set is used for testing. Compared with other parameter optimization algorithms, the proposed method has lower FNR and FPR in the electric pitch system fault detection of the wind turbine generator set.

Wind turbine asymmetrical load reduction with pitch sensor fault compensation

AbstractOffshore wind turbines suffer from asymmetrical loading (blades, tower, etc), leading to enhanced structural fatigue. As well as asymmetrical loading different faults (pitch system faults etc.) can occur simultaneously, causing degradation of load mitigation performance. Individual pitch control (IPC) can achieve rotor asymmetric loads mitigation, but this is accompanied by an enhancement of pitch movements leading to the increased possibility of pitch system faults, which exerts negative effects on the IPC performance. The combined effects of asymmetrical blade and tower bending together with pitch sensor faults are considered as a “co‐design” problem to minimize performance deterioration and enhance wind turbine sustainability. The essential concept is to attempt to account for all the “fault effects” in the rotor and tower systems, which can weaken the load reduction performance through IPC. Pitch sensor faults are compensated by the proposed fault‐tolerant control (FTC) strategy to attenuate the fault effects acting in the control system. The work thus constitutes a combination of IPC‐based load mitigation and FTC acting at the pitch system level. A linear quadratic regulator (LQR)‐based IPC strategy for simultaneous blade and tower loading mitigation is proposed in which the robust fault estimation is achieved using an unknown input observer (UIO), considering four different pitch sensor faults. The analysis of the combined UIO‐based FTC scheme with the LQR‐based IPC is shown to verify the robustness and effectiveness of these two systems acting together and separately.

Wind Turbine Pitch System Condition Monitoring and Fault Detection Based on Optimized Relevance Vector Machine Regression

Condition monitoring and early fault detection of wind turbine faults can reduce maintenance costs and prevent cascaded failures. This article proposes a new Normal Behavior Modeling (NBM) method to predict wind turbine electric pitch system failures using supervisory control and data acquisition (SCADA) information. The proposed method is particularly effective for online monitoring applications at a reasonable computational complexity. Briefly, in the data preprocessing stage of the proposed method, in order to remove interferential information and improve data quality, the operational state codes from turbine programmable logic controller are applied to filter SCADA data. In the modeling process, we designed a NBM method using optimized relevance vector machine (RVM) regression, which is relatively fast and computationally efficient. An adaptive threshold by the probabilistic output of RVM is proposed and used as the rule of anomaly detection. One normal case and three typical fault cases have been studied to demonstrate the feasibility of the proposed method. The performance of the method is assessed using 38 actual pitch system faults compared with two existing methods.

Dynamic response analysis of wind turbines under blade pitch system fault, grid loss, and shutdown events

ABSTRACTThis study focuses on the dynamic responses of land‐based and floating wind turbines under blade pitch system fault and emergency shutdown conditions. The NREL 5 MW turbine is studied. A hydraulic pitch system is considered, and the faults under study are events with a seized blade or a blade running out of control. Emergency shutdown is defined as a fast pitch‐to‐feather maneuver of the blades. Load cases with power production and grid fault with ensuing shutdown are also analysed for comparison. The fault scenarios and the blades' fast pitching activity are simulated using HAWC2 through external Dynamic Link Libraries. On the basis of the time‐domain simulations, the response characteristics of the land‐based and the floating turbines in the four design load cases are compared. The load effects from the fault conditions are compared with the operational cases. Strong system dynamics and resonant responses, such as the tower elastic mode and the yaw resonant response, are elicited during shutdown. If the pitch system has a fault and one blade is hindered from normal pitching, the uneven load distribution of the blades leads to large structural and motion responses. For both turbines, the response maxima vary cyclically with the instantaneous azimuth when the blades start pitching to feather. For the floating wind turbine, the interaction of waves and wind also affects the results. The effect of the pitch rate during shutdown is analysed. The responses of the land‐based turbine in grid loss and shutdown conditions are proportional to the pitch rate, whereas decreased sensitivity is found in the cases with pitch system faults. For the floating turbine, the effect of the pitch rate is small, and reduced pitch and yaw motion extremes are observed as the pitch rate increases. Copyright © 2013 John Wiley & Sons, Ltd.