Research on fault detection for three types of wind turbine subsystems using machine learning

2020 by the authors. 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

On fault prediction for wind turbine pitch system using radar chart and support vector machine approach

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

Approximate Optimal Tracking Control for Partially Unknown Nonlinear Systems via an Adaptive Fixed-Time Observer

This paper investigates a novel adaptive fixed-time disturbance observer (AFXDO)-based approximate optimal tracking control architecture for nonlinear systems with partially unknown dynamic drift and perturbation under an adaptive dynamic programming (ADP) scheme. To attenuate the impact of disturbance, a novel AFXDO was designed based on the principle of a fixed-time stable system without prior information of disturbance, making disturbance observer errors converge to zero in a fixed time independent of initial estimation error. Additionally, approximate optimal control is conducted by incorporating the real-time estimation of AFXDO into a critic-only ADP framework to stabilize the dynamics of tracking errors and strike a balance between consumption and performance. In particular, to address the heavy calculation burden and oscillation phenomenon in the traditional actor–critic structure, an improved adaptive update law with a variable learning rate was developed to update the weight for adjusting the optimal cost function and optimal control policy simultaneously, avoiding the initial chattering phenomenon and achieving a prescribed convergence without resorting to dual networks. With the efforts of AFXDO and a weight law with a variable learning rate, the track errors were achieved with fast transient performance and low control consumptions in a fixed time. By revisiting Lyapunov stability, the tracking error and weight estimation error were proven to be uniformly ultimately bounded, and the designed control tended to optimal control. The simulations were carried out on quadrotor tracking to demonstrate the effectiveness of the developed control scheme, which achieves rapid convergence by lower control consumption in 4 s, where the cost function is reduced by 19.13%

Shisa2 regulates the fusion of muscle progenitors

Adult skeletal muscles are comprised of multinuclear muscle cells called myofibers. During skeletal muscle development and regeneration, mononuclear progenitor cells (myoblasts) fuse to form multinuclear myotubes, which mature and become myofibers. The molecular events mediating myoblast fusion are not fully understood. Here we report that Shisa2, an endoplasmic reticulum (ER) localized protein, regulates the fusion of muscle satellite cell-derived primary myoblasts. Shisa2 expression is repressed by Notch signaling, elevated in activated compared to quiescent satellite cells, and further upregulated during myogenic differentiation. Knockdown of Shisa2 inhibits the fusion of myoblasts without affecting proliferation. Conversely, Shisa2 overexpression in proliferating myoblasts inhibits their proliferation but promotes premature fusion. Interestingly, Shisa2-overexpressing nascent myotubes actively recruit myoblasts to fuse with. At the molecular level, Rac1/Cdc42-mediated cytoskeletal F-actin remodeling is required for Shisa2 to promote myoblast fusion. These results provide a novel mechanism through which an ER protein regulates myogenesis

An Experience Transfer Approach for the Initial Data of Iterative Learning Control

Iterative learning control (ILC) requires that the operating conditions of the controlled system must remain unchanged in the repetitive learning process. If the parameters of system change, the former control experience of ILC would not be effective anymore. A new process of iterative learning has to restart, which will exhaust more time and resource. Compared with learning from zero experience, appropriate initial data for the first iteration could reduce the turns of iterations to achieve the target tracking accuracy. When the parameters of a linear system change, its structure and nature are still intrinsically related to the original system. So, if the experience obtained from original ILC could be correspondingly adjusted according to the difference of new and original system, and use the adjusted experience as the initial data in the new iterative learning process, it would reduce the time and save the resource in the new ILC. Based on the idea of experience inheritance and transform, an experience transfer approach for the initial data of ILC is proposed in reference to the relation between the new and original systems. In this paper, via the method of recombining, translational and amplitude adjusting, the experience of former ILC is transferred as the initial control data of new ILC. Simulation shows that the convergence iteration of ILC with experience transfer approach reduces 55–75%, which demonstrates the effectiveness and advantages of the approach proposed in this paper. Both the deviation of the first iteration in ILC and the turns of iterations for achieving desired accuracy are reduced greatly

An Experience Transfer Approach for the Initial Data of Iterative Learning Control

Iterative learning control (ILC) requires that the operating conditions of the controlled system must remain unchanged in the repetitive learning process. If the parameters of system change, the former control experience of ILC would not be effective anymore. A new process of iterative learning has to restart, which will exhaust more time and resource. Compared with learning from zero experience, appropriate initial data for the first iteration could reduce the turns of iterations to achieve the target tracking accuracy. When the parameters of a linear system change, its structure and nature are still intrinsically related to the original system. So, if the experience obtained from original ILC could be correspondingly adjusted according to the difference of new and original system, and use the adjusted experience as the initial data in the new iterative learning process, it would reduce the time and save the resource in the new ILC. Based on the idea of experience inheritance and transform, an experience transfer approach for the initial data of ILC is proposed in reference to the relation between the new and original systems. In this paper, via the method of recombining, translational and amplitude adjusting, the experience of former ILC is transferred as the initial control data of new ILC. Simulation shows that the convergence iteration of ILC with experience transfer approach reduces 55–75%, which demonstrates the effectiveness and advantages of the approach proposed in this paper. Both the deviation of the first iteration in ILC and the turns of iterations for achieving desired accuracy are reduced greatly

Deep learning method for fault detection of wind turbine converter

The converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper presents an approach to wind turbine converter fault detection using convolutional neural network models which are developed by using wind turbine Supervisory Control and Data Acquisition (SCADA) system data. The approach starts with the selection of fault indicator variables, and then the fault indicator variables data are extracted from a wind turbine SCADA system. Using the data, radar charts are generated, and the convolutional neural network models are applied to feature extraction from the radar charts and characteristic analysis of the feature for fault detection. Based on the analysis of the Octave Convolution (OctConv) network structure, an improved AOctConv (Attention Octave Convolution) structure is proposed in this paper, and it is applied to the ResNet50 back-bone network (named as AOC–ResNet50). It is found that the algorithm based on AOC–ResNet50 overcomes the issues of information asymmetry caused by the asymmetry of the sampling method and the damage to the original features in the high and low frequency domains by the OctConv structure. Finally, the AOC–ResNet50 network is employed for fault detection of the wind turbine converter using 10 min SCADA system data. It is verified that the fault detection accuracy using the AOC–ResNet50 network is up to 98.0%, which is higher than the fault detection accuracy using the ResNet50 and Oct–ResNet50 networks. Therefore, the effectiveness of the AOC–ResNet50 network model in wind turbine converter fault detection is identified. The novelty of this paper lies in a novel AOC–ResNet50 network proposed and its effectiveness in wind turbine fault detection. This was verified through a comparative study on wind turbine power converter fault detection with other competitive convolutional neural network models for deep learning

LFM: A Lightweight LCD Algorithm Based on Feature Matching between Similar Key Frames

Loop Closure Detection (LCD) is an important technique to improve the accuracy of Simultaneous Localization and Mapping (SLAM). In this paper, we propose an LCD algorithm based on binary classification for feature matching between similar images with deep learning, which greatly improves the accuracy of LCD algorithm. Meanwhile, a novel lightweight convolutional neural network (CNN) is proposed and applied to the target detection task of key frames. On this basis, the key frames are binary classified according to their labels. Finally, similar frames are input into the improved lightweight feature matching network based on Transformer to judge whether the current position is loop closure. The experimental results show that, compared with the traditional method, LFM-LCD has higher accuracy and recall rate in the LCD task of indoor SLAM while ensuring the number of parameters and calculation amount. The research in this paper provides a new direction for LCD of robotic SLAM, which will be further improved with the development of deep learning

On Fault Prediction for Wind Turbine Pitch System Using Radar Chart and Support Vector Machine Approach

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