Health Condition Of Wind Turbine Research Articles

Havacılık Kompozit Malzemelerinde Mikroskobik Teknikler Kullanılarak Yapısal Sağlık İzleme Uygulamaları

Structural Health Monitoring (SHM) is a process that involves the observation and analysis of a system over time using periodically sampled response measurements to monitor changes to the material and geometric properties of engineering structures such as bridges, buildings, and aerospace composite structures. The goal of SHM is to detect changes in the structural behavior or condition that may indicate damage or degradation before a catastrophic failure occurs. SHM involves the implementation of damage detection strategies for structures of high importance. It is commonly used in civil engineering, aerospace engineering, and mechanical engineering applications to ensure the safety and reliability of structures. It improves the safety of aerospace composite structures by detecting damage at an early stage, preventing damage from occurring, improving reliability, and extending the life of the structure. SHM applications enable aircraft to spend less time on the ground and carry more passengers and cargo, thereby reducing operational costs. It can be utilized in various fields such as monitoring the health condition of aircraft tail and wing areas in the aviation industry, preventing damage and deterioration of car parts and components under operating conditions in the automotive sector, monitoring the health condition of bridges and tunnels in the transportation sector, and monitoring the health condition of wind turbines and other structures in the energy sector. Aerospace composite structures can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix cracking, fiber breakage, and voids. This study provides general and useful information on how structural health applications of aviation composites can be supported by microscopic techniques. In order to better understand the subject, an example aircraft composite structural component containing impact damage, which was mentioned above, was examined using microscopic techniques. In this investigation conducted using Stereo and Scanning Electron Microscopes (SEM), the identification of potential damage sources and the assessment of damage severity are explained in detail.

A novel primary frequency regulation strategy of wind farm based on wind turbine health condition

In order to optimize the primary frequency regulation performance of the wind farm and increase the service life of the wind turbines (WTs), this paper presents a novel primary frequency regulation strategy of the wind farm to maintain the system frequency considering each WT's health condition level. The proposed strategy can make WTs participate in frequency regulation according to their health conditions. Thus, the unhealthy WTs can be prevented from further deteriorating by reducing the load. In this paper, condition monitoring data from WTs is applied to estimate their health condition levels. Then, the power support is estimated according to the supplementary loop which includes inertial control and droop control. An adaptive model predictive control (AMPC) algorithm is employed to improve the control accuracy and mitigate the fluctuation caused by frequency variation. The case study is carried out in a two-area wind-diesel power system to validate the effectiveness of the proposed method. The results show that the proposed strategy optimizes the effect of primary frequency regulation while reducing the load on unhealthy WTs.

A Novel Wind Turbine Health Condition Monitoring Method Based on Correlative Features Domain Adaptation

Aimed at the difficulty in fault diagnosis of wind turbine transmission system under variable working conditions, the paper proposes a novel health condition monitoring method based on correlative features domain adaptation. Firstly, the envelope analysis of the collected signals is carried out, and the time–frequency features of the signals are extracted to construct the feature set. The feature sets under the similar working conditions to target are selected as the auxiliary sample sets in source domain through the transferability evaluation. Then, a transformation matrix is found to adapt the marginal and conditional distributions of wind turbine sample data under different working conditions, and its weight is adjusted. While reducing the discrepancy between domains, the class imbalance problem is taken into consideration, so as to improve the accuracy of fault diagnosis under the target working condition. Finally, the classifier is trained with the adjusted source domain and tested in the target domain. Experiments show that the proposed method can effectively improve the accuracy of wind turbine fault diagnosis.

A novel wind turbine health condition monitoring method based on composite variational mode entropy and weighted distribution adaptation

Aimed at the problem that the complicated working condition of wind turbine and the lack of sufficient target samples, which makes it difficult to conduct effective health condition monitoring (HCM), a novel method based on composite variational mode entropy (CVME) and weighted distribution adaptation (WDA) is proposed in this paper. A series of mode components are first obtained by performing variational mode decomposition (VMD) on the signals under various working conditions. The mode components are analyzed on multi-scale, and then the fuzzy entropy is extracted at different scales. The extracted CVME is input as a feature vector into WDA. The WDA method can effectively reduce the discrepancy of data distribution between the source and target domains by adjusting the weight of the marginal distribution and the conditional distribution, and solve the problem of class imbalance in domains by a weight matrix. The transferability evaluation is used to select the feature sets under auxiliary working conditions with high similarity to the target feature set as the source samples. Finally, the source and target samples are input into the classifier for training and testing. Compared with traditional fault diagnosis methods, experiment shows that the proposed method has higher accuracy in wind turbine fault diagnosis under variable working conditions.

A novel wind turbine health condition monitoring method based on common features distribution adaptation

Aimed at the difficulty of diagnosing the transmission system of wind turbine under variable working conditions, a novel health condition monitoring method based on common features distribution adaptation is proposed in this article. In the method, envelope analysis is first performed on the collected signals, and then the time-frequency features are extracted to be combined as new input samples. The feature set under the working condition similar to target working condition is selected as the auxiliary sample set in source domain through the evaluation of the transferability. The kernel function is used to map the labeled auxiliary samples and unlabeled target samples to a reproduced kernel Hilbert space, which effectively reduces the data distribution discrepancy between source and target domains. The problem of class imbalance in each domain is taken into account when performing fault recognition, which improves the effect of transfer learning. Finally, the adjusted source domain is used to train the classifier, which is applied to the target domain to get the predicted labels of the test data. Experiment shows that the proposed method has better working performance than traditional fault diagnosis methods.

Condition monitoring of wind turbines based on analysis of temperature-related parameters in supervisory control and data acquisition data

In order to conduct a further in-depth exploration of the role of temperature-related parameters in the condition monitoring of wind turbines, this paper proposes a method to assess the condition of wind turbines by analyzing the supervisory control and data acquisition system temperature-related parameters based on existing research. A prediction model of time-sequence regression is established, based on the key temperature signals of WTs, so as to reflect their health condition in the form of prediction residuals. A kind of health index from the perspective of temperature-related parameters is developed by separating the statistics concerning the conformity of the predicted values of key temperature parameters within a certain time window from the measured values in order to clearly present the implied information on the health condition of wind turbines contained in the model prediction residuals. The case study shows that the trend of health index from the perspective of temperature-related parameters is consistent with the health condition of wind turbines. In some instances, its decline obviously occurs earlier than the maintenance provided to address the stoppage, suggesting that such indexes can effectively reflect some early health problems of the wind turbines to provide a reference for their scientific maintenance.

A new wind turbine health condition monitoring method based on VMD-MPE and feature-based transfer learning

Aimed at the problem that the signal data of wind turbine faulty gearbox is difficult to obtain and the health condition is difficult to diagnose under variable working conditions, a fault diagnosis method based on variational mode decomposition (VMD) multi-scale permutation entropy (MPE) and feature-based transfer learning (FTL) is proposed. According to the vibration signal characteristics of wind turbines, a series of mode components are obtained by transforming the signals under variable conditions. The MPE of the mode components is combined with the signal time domain features as a feature vector to be input into the transfer learning algorithm. The source domain and the target domain data belong to different working conditions, so the traditional machine learning methods are not ideal for fault classification. The method adopted in this paper minimizes the covariance between the source domain and the target domain through a linear transformation matrix, and reduces the difference of data distribution between the source domain and the target domain. Then, the feature vectors of the covariance-aligned source domain and the target domain are input into the support vector machine (SVM) for training and testing. Experiment shows that the proposed covariance alignment (COVAL) of fault features has higher accuracy in rolling bearing multi-state classification under variable working conditions compared with other methods.

Online health assessment of wind turbine based on operational condition recognition

To reduce the operation and maintenance (O&M) costs, the health assessment of wind turbine has received more and more attention in recent years. However, it is difficult to evaluate the health condition of wind turbine due to the complex and non-stationary operation environment. This paper proposes a data-driven approach for online health assessment of wind turbine based on operational condition recognition. First, the operational condition parameters are selected by analyzing the monitoring data of wind turbine. Considering the time-varying of operation environment, the operational conditions are divided into four subspaces utilizing the K-means clustering algorithm. Then, using the historical state parameters data under normal operation, a health benchmark model is constructed in each operational condition space based on Gaussian Mixture Model (GMM). Further, a Softmax model is trained according to the results of operational condition classification, which is used to identify the online operational condition of wind turbine. Moreover, an overall health index (HI) based on Mahalanobis distance is developed to assess the health condition of wind turbine. Finally, the method is verified by the actual supervisory control and data acquisition (SCADA) data of a wind field in northwestern China. The test results show that the proposed approach can track the running state of the wind turbine accurately and play a good role in early fault warning.

Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves

Prognostics and Health Management (PHM) can offer substantial improvements in reliability and availability of the wind turbine asset. Driven by reducing the Operation and Maintenance (O&M) cost of wind turbines, many research efforts have been conducted to realize reliable wind turbine performance degradation assessment. Despite these efforts, it is still challenging to assess the actual degradation trend of wind turbine which will be suitable for prediction analysis. In this study, a novel similarity metric for machine performance curves is proposed and a framework of wind turbine performance assessment methodology is presented. The proposed algorithm evaluates the health condition of wind turbine by performing principal component analysis on the quasi-linear region of the power curve. The proposed methodology has been validated on a dataset collected from a large scale onshore wind turbine for a period of two years. The result exhibits a gradual degradation trend of wind turbine and indicates the ability of proposed approach to trend and assess the turbine degradation before downtime happens. The result from the proposed method also reveals its robustness to wind resolution in the power curve, which still exhibits a very similar degradation trend when the wind resolution of power curve has been down sampled.

Automated on‐line fault prognosis for wind turbine pitch systems using supervisory control and data acquisition

Current wind turbine (WT) studies focus on improving their reliability and reducing the cost of energy, particularly when WTs are operated offshore. A supervisory control and data acquisition (SCADA) system is a standard installation on larger WTs, monitoring all major WT sub-assemblies and providing important information. Ideally, a WT's health condition or state of the components can be deduced through rigorous analysis of SCADA data. Several programmes have been made for that purposes; however, the resulting cost savings are limited because of the data complexity and relatively low number of failures that can be easily detected in early stages. This study proposes a new method for analysing WT SCADA data by using an a priori knowledge-based adaptive neuro-fuzzy inference system with the aim to achieve automated detection of significant pitch faults. The proposed approach has been applied to the pitch data of two different designs of 26 variable pitch, variable speed and 22 variable pitch, fixed speed WTs, with two different types of SCADA system, demonstrating the adaptability of the approach for application to a variety of techniques. Results are evaluated using confusion matrix analysis and a comparison study of the two tests is addressed to draw conclusions.

Dynamic evaluation of wind turbine health condition based on Gaussian mixture model and evidential reasoning

Condition-based maintenance is an effective way to reduce operation and maintenance cost of wind turbine. Highly complex and non-stationary operational conditions of wind turbine pose a challenge to conventional condition monitoring technique. Thus, a systematic multi-parameter health condition evaluation framework that considers the dynamic operational conditions is proposed. After characteristic parameter selection and Gaussian mixture model based multi-regime modeling, evidential reasoning is developed to evaluate the health condition of wind turbine. The proposed approach shows good health condition evaluation performance not only on the parameter level but also on the component and system level. Case studies indicate the effectiveness and potential applications of the proposed method for the wind turbine health condition evaluation.

Dynamic Evaluation of Wind Turbine Health Condition Based on Multi-Source Information Fusion

Due to highly complex and non-stationary operation condition, a dynamic wind turbine health condition evaluation approach based on multi-source information fusion is recommended. The uncertainty and incompletion of evaluation factors are fully considered, both qualitative and quantitative evidences are integrated. A case study is provided to illustrate the implementation process of the dynamic evaluation approach for wind turbine health condition. It is shown that the proposed approach offers a flexible and effective way of wind turbine health condition evaluation and the result can be used as a support for condition based maintenance decision.