scholarly journals Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6905
Author(s):  
Ling Zhou ◽  
Qiancheng Zhao ◽  
Xian Wang ◽  
Anfeng Zhu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Search Algorithm ◽  
Principal Component ◽  
Comparison Results ◽  
Auto Associative Neural Network

When the state of the wind turbine sensors, especially the anemometer, appears abnormal it will cause unnecessary wind loss and affect the correctness of other parameters of the whole system. It is very important to build a simple and accurate fault diagnosis model. In this paper, the model has been established based on the Random Walk Improved Sparrow Search Algorithm to optimize auto-associative neural network (RWSSA-AANN), and is used for fault diagnosis of wind turbine group anemometers. Using the cluster analysis, six wind turbines are determined to be used as a wind turbine group. The 20,000 sets of normal historical data have been used for training and simulating of the model, and the single and multiple fault states of the anemometer are simulated. Using this model to analyze the wind speed supervisory control and data acquisition system (SCADA) data of six wind turbines in a wind farm from 2013 to 2017, can effectively diagnose the fault state and reconstruct the fault data. A comparison of the results obtained using the model developed in this work has also been made with the corresponding results generated using AANN without optimization and AANN optimized by genetic algorithm. The comparison results indicate that the model has a higher accuracy and detection rate than AANN, genetic algorithm auto-associative neural network (GA-AANN), and principal component analysis (PCA).

scholarly journals Research on Fan Operation Evaluation and Error State Judgment Relying on Improved Neural Network and Intelligent Computing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042005
Author(s):  
Xueyi Liu ◽  
Junhao Dong ◽  
Guangyu Tu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Performance Prediction ◽  
Optimization Method ◽  
Aerodynamic Performance ◽  
Evaluation Index ◽  
Genetic Optimization ◽  
Quantum Genetic Algorithm

Abstract Fan, as the most commonly used mechanical equipment, is widely used. In order to solve the problem of fan bearing fault diagnosis, this paper analyzes the main factors affecting fan spindle speed and power generation in operation. The input and output parameters of the performance prediction model are determined. The performance prediction model of wind turbine is established by using generalized regression neural network, and the smoothing factor of GRNN is optimized by comparing the prediction accuracy of the model. Based on this model, the sliding data window method is used to calculate the residual evaluation index of wind turbine speed and power in real time. When the evaluation index continuously exceeds the pre-set threshold, the abnormal state of wind turbine can be judged. In order to obtain wind turbine blades with better aerodynamic performance, a blade aerodynamic performance optimization method based on quantum heredity is proposed. The B é zier curve control point is used as the design variable to represent the continuous chord length and torsion angle distribution of the blade, the blade shape optimization model aiming at the maximum power is established, and the quantum genetic algorithm is used to optimize the chord length and torsion angle of the blade under different constraints. The optimization results of quantum genetic algorithm and classical genetic algorithm are compared and analyzed. Under the same parameters and boundary conditions, the proposed blade aerodynamic optimization method based on quantum genetic optimization is better than the classical genetic optimization method, and can obtain better blade aerodynamic shape and higher wind energy capture efficiency. This method makes up for the shortcomings of traditional fault diagnosis methods, improves the recognition rate of fault types and the accuracy of fault diagnosis, and the diagnosis effect is good.

Fault Diagnosis of Wind Turbines Based on Improved Neural Network

2014 ◽  
Vol 978 ◽  
pp. 78-83
Author(s):  
Qiang Lan ◽  
Peng Da Zhao ◽  
Man Li Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Feature Vector ◽  
Wind Turbine Gearbox ◽  
Vibration Sensors

The gearbox is an important module of wind turbine. In order to diagnosis the fault of wind turbine gearbox, a method based on the improved neural network is proposed. According to the characteristics of the wind turbine gearbox, several vibration sensors are set in the gearbox, so as to acquire the feature vector of gearbox. After training, the improved neural network is verified with some test samples. The result proved that the method is suitable for fault diagnosis in gearbox of wind turbine.. Keywords: wind turbine gearbox, fault diagnosis, particle swam, neural network.

scholarly journals Wind Turbine Anomaly Identification Based on Improved Deep Belief Network with SCADA Data

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiafei Long ◽  
Shengqing Li ◽  
Xiwen Wu ◽  
Zhao Jin
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Power ◽  
Wind Turbines ◽  
Domain Knowledge ◽  
Wind Farm ◽  
Prediction Models ◽  
Moving Average ◽  
Pearson Correlation ◽  
Whale Optimization

This article presents a novel fault diagnosis algorithm based on the whale optimization algorithm (WOA)-deep belief networks (DBN) for wind turbines (WTs) using the data collected from the supervisory control and data acquisition (SCADA) system. Through the domain knowledge and Pearson correlation, the input parameters of the prediction models are selected. Three different types of prediction models, namely, the wind turbine, the wind power gearbox, and the wind power generator, are used to predict the health condition of the WT equipment. In this article, the prediction accuracy of the models built with these SCADA sample data is discussed. In order to implement fault monitoring and abnormal state determination of the wind power equipment, the exponential weighted moving average (EWMA) threshold is used to monitor the trend of reconstruction errors. The proposed method is used for 2 MW wind turbines with doubly fed induction generators in a real-world wind farm, and experimental results show that the proposed method is effective in the fault diagnosis of wind turbines.

scholarly journals SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

2021 ◽  
Vol 11 (8) ◽  
pp. 3307
Author(s):  
Francesco Castellani ◽  
Davide Astolfi ◽  
Francesco Natili
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Principal Component ◽  
Support Vector ◽  
General Context ◽  
Test Case ◽  
Electric Generator ◽  
Before And After ◽  
Electrical Faults

The electric generator is estimated to be among the top three contributors to the failure rates and downtime of wind turbines. For this reason, in the general context of increasing interest towards effective wind turbine condition monitoring techniques, fault diagnosis of electric generators is particularly important. The objective of this study is contributing to the techniques for wind turbine generator fault diagnosis through a supervisory control and data acquisition (SCADA) analysis method. The work is organized as a real-world test-case discussion, involving electric damage to the generator of a Vestas V52 wind turbine sited in southern Italy. SCADA data before and after the generator damage have been analyzed for the target wind turbine and for reference healthy wind turbines from the same site. By doing this, it has been possible to formulate a normal behavior model, based on principal component analysis and support vector regression, for the power and for the voltages and currents of the wind turbine. It is shown that the incipience of the fault can be individuated as a change in the behavior of the residuals between model estimates and measurements. This phenomenon was clearly visible approximately two weeks before the fault. Considering the fast evolution of electrical damage, this result is promising as regards the perspectives of exploiting SCADA data for individuating electric damage with an advance that can be useful for applications in wind energy practice.

scholarly journals Wind Turbine Yaw Control Optimization and Its Impact on Performance

Machines ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 41 ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Francesco Natili
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Principal Component Regression ◽  
Principal Component ◽  
Energy Conversion Efficiency ◽  
Test Case ◽  
Yaw Control ◽  
Control Optimization ◽  
Technology Improvement

The optimization of wind energy conversion efficiency has been recently boosting the technology improvement and the scientific comprehension of wind turbines. In this context, the yawing behavior of wind turbines has become a key topic: the yaw control can actually be exploited for optimization at the level of single wind turbine and of wind farm (for example, through active control of wakes). On these grounds, this work is devoted to the study of the yaw control optimization on a 2 MW wind turbine. The upgrade is estimated by analysing the difference between the measured post-upgrade power and a data driven model of the power according to the pre-upgrade behavior. Particular attention has therefore been devoted to the formulation of a reliable model for the pre-upgrade power of the wind turbine of interest, as a function of the operation variables of all the nearby wind turbines in the wind farm: the high correlation between the possible covariates of the model indicates that Principal Component Regression (PCR) is an adequate choice. Using this method, the obtained result for the selected test case is that the yaw control optimization provides a 1% of annual energy production improvement. This result indicates that wind turbine control optimization can non-negligibly improve the efficiency of wind turbine technology.

scholarly journals Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 975
Author(s):  
Yancai Xiao ◽  
Jinyu Xue ◽  
Mengdi Li ◽  
Wei Yang
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Gear Tooth ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Empirical Wavelet Transform ◽  
Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

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