Comparative Study on Tree Classifiers for Application to Condition Monitoring ofWind Turbine Blade through Histogram Features Using Vibration Signals: A Data-Mining Approach

Wind energy is considered as a alternative renewable energy source due to its low operating cost when compared with other sources.The wind turbine is an essential system used to change kinetic energy into electrical energy.Wind turbine blades,in particular,require a competitive condition inspection approach as it is a significant component of the wind turbine system that costs around 20-25 percent of the total turbine cost.The main objective of this study is to differentiate between various blade faults which affect the wind turbine blade under operating conditions using a machine learning approach through histogram features.In this study,blade bend,hub-blade loose connection,blade erosion,pitch angle twist,and blade cracks were simulated on the blade.This problem is formulated as a machine learning problem which consists of three phases,namely feature extraction,feature selection and feature classification.Histogram features are extracted from vibration signals and feature selection was carried out using the J48 decision tree algorithm.Feature classification was performed using 15 tree classifiers.The results of the machine learning classifiers were compared with respect to their accuracy percentage and a better model is suggested for real-time monitoring of a wind turbine blade.

Vibration Signals and Condition Monitoring for Wind Turbines

Rolling element bearings are critical parts of modern wind turbines as they carry the loads of the turning structure and the wind force. The stochastic nature of the wind loads makes it difficult to estimate the useful operational life of the bearings. Condition monitoring of these bearings in a real time environment could be very helpful in estimating their performance and in scheduling maintenance actions when a condition-based maintenance strategy is followed. This procedure can be successfully implemented by using vibration analysis in the time domain or in the frequency domain, giving useful results about the current condition of bearings and the location of potential faults. Permanently located transducers on proper positions on the bearings’ housings can be used in order to collect, process and evaluate real time measurements and provide information about the bearing’s performance. In this work, a test rig is utilized in order to evaluate the performance of rolling bearings. The results of the experimentation are satisfactory and the progress of fatigue failures can be predicted through vibration analysis techniques showing that implementation in real scale may be useful.

PREDICTION OF THE AERODYNAMIC PERFORMANCE OF HORIZONTAL AXIS WIND TURBINES IN CONDITION OF UNIFORM WIND

The classical momentum-blade element theory is improved by using the empirical formula while part of rotor blades enters into the turbulent wake state, and the performance of a horizontal-axis wind turbine (HAWT) at all speed ratios can be predicted. By using an improved version of the so-called secant method, the convergent solutions of the system of two-dimensional equations concerning the induced velocity factors a and a' are guaranteed. Besides, a solving method of multiple solutions for a and a' is proposed and discussed. The method provided in this paper can be used for computing the aerodynamic performance of HAWTs both ofrlow solidity and of high solidity. The calculated results coincide well with the experimental data.

Overview of condition monitoring and operation control of electric power conversion systems in direct-drive wind turbines under faults

Electric power conversion system （EPCS）, which consists of a generator and power converter, is one of the most important subsystems in a direct-drive wind turbine （DD-WT）. However, this component accounts for the most failures （approximately 60% of the total number） in the entire DD-WT system according to statistical data. To improve the reliability of EPCSs and reduce the operation and maintenance cost of DD-WTs, numerous researchers have studied condition monitoring （CM） and fault diagnostics （FD）. Numerous CM and FD techniques, which have respective advantages and disadvantages, have emerged. This paper provides an overview of the CM, FD, and operation control of EPCSs in DD-WTs under faults. After introducing the functional principle and structure of EPCS, this survey discusses the common failures in wind generators and power converters; briefly reviewed CM and FD methods and operation control of these generators and power converters under faults; and discussed the grid voltage faults related to EPCSs in DD-WTs. These theories and their related technical concepts are systematically discussed. Finally, predicted development trends are presented. The paper provides a valuable reference for developing service quality evaluation methods and fault operation control systems to achieve high-performance and high-intelligence DD-WTs.

Monitoring and Detection of Wind Turbine Vibration with KNN-Algorithm

Maintenance for wind turbines has been transformed using supervised machine learning techniques. This method of automatic and autonomous learning can identify, monitor, and detect electrical and mechanical components of wind turbines and predict, detect, and anticipate their degeneration. Using a machine learning classifier and frequency analysis, we simulate two failure states caused by bearing vibrations. Implementing KNN facilitates efficient monitoring, monitoring, and fault-finding for wind turbines. It is possible to reduce downtime, anticipate breakdowns, and import offshore aspects through these technologies.

Prognostic Condition Monitoring for Wind Turbine Drivetrains via Generator Current Analysis

Maintenance costs account for a significant portion of the total cost of electricity generated by wind turbines.Currently in the wind power industry,maintenance is mainly performed on regular schedules or when significant damage occurs in a wind turbine making it inoperable,instead of being determined by the actual condition of the wind turbine.Among the total maintenance costs,approximately 25%~35%is related to regularly scheduled preventive maintenance and 65%~75%to unscheduled corrective maintenance.To reduce the failure rate and level and maintenance costs and improve the availability,reliability,safety,and lifespans of wind turbines,it is desirable to perform condition-based predictive maintenance for wind turbines,which will require a high-fidelity online prognostic condition monitoring system(CMS)for fault diagnosis and prognosis and remaining useful life(RUL)prediction of wind turbines.Most of the existing wind turbine CMSs are based on vibration monitoring and have no or limited capability in fault prognosis and RUL prediction.Compared to vibration monitoring,the prognostic condition monitoring techniques based on generator current signal analysis proposed recently have significant advantages in terms of cost,hardware complexity,implementation,and reliability.This paper discusses the principles and challenges of using generator current signals for prognostic condition monitoring of wind turbine drivetrains and presents an overview of recent advancements in this area.

A Comparative Study of Bayes Classifiers for Blade Fault Diagnosis in Wind Turbines through Vibration Signals

Renewable energy sources are considered much in energy fields because of thecontemporary energy calamities. Among the important alternatives being considered, windenergy is a durable competitor because of its dependability due to the development of theinnovations, comparative cost effectiveness and great framework. To yield wind energymore proficiently, the structure of wind turbines has turned out to be substantially bigger,creating conservation and renovation works troublesome. Due to various ecologicalconditions, wind turbine blades are subjected to vibration and it leads to failure. If thefailure is not diagnosed early, it will lead to catastrophic damage to the framework. In orderto increase safety observations, to reduce down time, to bring down the recurrence ofunexpected breakdowns and related enormous maintenance, logistic expenditures and tocontribute steady power generation, the wind turbine blade must be monitored now andthen to assure that they are in good condition. In this paper, a three bladed wind turbinewas preferred and using vibration source, the condition of a wind turbine blade is examined.The faults like blade crack, erosion, hub-blade loose connection, pitch angle twist and bladebend faults were considered and these faults are classified using Bayes Net (BN),Discriminative Multinomial Naïve Bayes (DMNB), Naïve Bayes (NB), Simple NaïveBayes (SNB), and Updateable Naïve Bayes (UNB) classifiers. These classifiers arecompared and better classifier is suggested for condition monitoring of wind turbine blades.

SVM-Algorithm for Supervision, Monitoring and Detection Vibration in Wind Turbines

With the implementation of supervised machine learning techniques, wind turbine maintenance has been transformed. A wind turbine’s electrical and mechanical components can be automatically identified, monitored, and detected to predict, detect, and anticipate their degeneration using this method of automatic and autonomous learning. Two different failure states are simulated due to bearing vibrations and compared with machine learning classifier and frequency analysis. A wind turbine can be monitored, monitored, and faulted efficiently by implementing SVM. With these technologies, downtime can be reduced, breakdowns can be anticipated, and aspects can be imported if they are offshore.

Wind-induced instabilities and monitoring of wind turbine

This paper presents real-time monitoring data and analysis results of the non-stationary vibrations of an operational wind turbine. The advanced time-frequency spectrum analysis reveals varied non-stationary vibrations with timevarying frequencies, which are correlated with certain system natural modes characterized by finite element analysis. Under the effects of strong wind load, the wind turbine system exhibits certain resonances due to blade passing excitations. The system also exhibits certain instabilities due to the coupling of the tower bending modes and blade flapwise mode with blade passing excitations under the variation of wind speed. An analytical model is used to elaborate the non-stationary and instability phenomena observed in experimental results. The properties of the nonlinear instabilities are evaluated by using Lyapunov exponent estimation.

Model-based and Fuzzy Logic Approaches to Condition Monitoring of Operational Wind Turbines

It is common for wind turbines to be installed in remote locations on land or offshore, leading to difficulties in routine inspection and maintenance. Further, wind turbines in these locations are often subject to harsh operating conditions. These challenges mean there is a requirement for a high degree of maintenance. The data generated by monitoring systems can be used to obtain models of wind turbines operating under different conditions, and hence predict output signals based on known inputs. A model-based condition monitoring system can be implemented by comparing output data obtained from operational turbines with those predicted by the models, so as to detect changes that could be due to the presence of faults. This paper discusses several techniques for model-based condition monitoring systems: linear models, artificial neural networks, and state dependent parameter "pseudo" transfer functions.The models are identified using supervisory control and data acquisition(SCADA) data acquired from an operational wind firm. It is found that the multiple-input single-output state dependent parameter method outperforms both multivariate linear and artificial neural network-based approaches. Subsequently, state dependent parameter models are used to develop adaptive thresholds for critical output signals. In order to provide an early warning of a developing fault, it is necessary to interpret the amount by which the threshold is exceeded, together with the period of time over which this occurs. In this regard, a fuzzy logic-based inference system is proposed and demonstrated to be practically feasible.

Physics-based data analysis for wind turbine condition monitoring

This article presents methodologies for improving wind turbine condition monitoring using physics-based data analysis techniques.The unique operating conditions of the wind turbine drivetrain are described,and the complex kinematics of the gearbox is analyzed in detail.The pros and cons of the current wind turbine condition monitoring system(CMS)are evaluated.To improve the wind turbine CMS capability,it is suggested to use linear models with unsteady excitations,instead of using nonlinear and nonstationary process models,when dealing the wind turbine dynamics response model.An analysis is undertaken of the damage excitation mechanisms cause for various components in a gearbox,especially for those associated with lower-speed shafts.Physics(mechanics)-based data analysis methods are presented for different component damage excitation mechanisms.Validation results,using the wind farm and manufacturing floor data,are reported.

A portable wind turbine condition monitoring system and its field applications

This article introduces a portable wind turbine condition monitoring system(CMS)and its applications in wind turbine drivetrain damage detection.The portable CMS based on vibration detection and analysis has a long application history in conventional rotating machineries,but it is not widely used in wind turbines.There are several reasons why it is not used,including the labor-and knowledge-intensive requirements for test setup and result interpretation.There are also reasons specific to wind turbines,such as the structural diversity of drivetrains,the uncertainty of operational conditions,and the complexity of the damage mechanism of different parts that make the conventional vibration-based CMS inefficient and not cost-effective.All these factors affect the wide application of the portable system.The portable wind turbine CMS discussed in this article is integrated using advanced vibration measurement and analysis methodology.Fault detection for the acquired acceleration response and high-speed shaft speed signal is carried out by a suite of data analysis techniques specifically designed for a wind turbine gearbox.Using these techniques,damage detection accuracy for all the components inside a gearbox is improved significantly,especially for those related to medium-and low-speed shafts.The new data processing techniques also are briefly described with the developed methodologies verified by three wind turbines with typical low-speed shaft-related component damages.These damage assessments include the low-and medium-speed planetary stage ring gear,the low-speed planetary stage planet gear and damage to the main bearing.

Condition monitoring of a wind turbine generator using a standalone wind turbine emulator

The intend of this paper is to give a description of the realization of a low-cost wind turbine emulator （WTE） with open source technology from graze required for the condition monitoring to diagnose rotor and stator faults in a wind turbine generator （WTG）. The WTE comprises of a 2.5 kW DC motor coupled with a 1 kW squirrel-cage induction machine. This paper provides a detailed overview of the hardware and software used along with the WTE control strategies such as MPPT and pitch control. The emulator reproduces dynamic characteristics both under step variations and arbitrary variation in the wind speed of a typical wind turbine （WT） of a wind energy conversion system （WECS）. The usefulness of the setup has been benchmarked with previously verified WT test rigs made at the University of Manchester and Durham University in UK. Considering the fact that the rotor blades and electric subassemblies direct drive WTs are most susceptible to damage in practice, generator winding faults and rotor unbalance have been introduced and investigated using the terminal voltage and generated current. This wind turbine emulator （WTE） can be reconfigured or analyzed for condition monitoring without the need for real WTs.

An explainable AI framework for robust and transparent data-driven wind turbine power curve models

In recent years,increasingly complex machine learning methods have become state-of-the-art in modelling wind turbine power curves based on operational data.While these methods often exhibit superior performance on test sets,they face criticism due to a perceived lack of transparency and concerns about their robustness in dynamic,non-stationary environments encountered by wind turbines.In this work,we address these issues and present a framework that leverages explainable artificial intelligence methods to gain systematic insights into data-driven power curve models.At its core,we propose a metric to quantify how well a learned model strategy aligns with the underlying physical principles of the problem.This novel tool enables model validation beyond the conventional error metrics in an automated manner.We demonstrate,for instance,its capacity as an indicator for model generalization even when limited data is available.Moreover,it facilitates understanding how decisions made during the machine learning development process,such as data selection,pre-processing,or training parameters,affect learned strategies.As a result,we obtain physically more reasonable models,a prerequisite not only for robustness but also for meaningful insights into turbine operation by domain experts.The latter,we illustrate in the context of wind turbine performance monitoring.In summary,the framework aims to guide researchers and practitioners alike toward a more informed selection and utilization of data-driven wind turbine power curve models.

Transfer learning applications for autoencoder-based anomaly detection in wind turbines

Anomaly detection in wind turbines typically involves using normal behaviour models to detect faults early.Normal behaviour models are often implemented through the use of neural networks,of which autoencoders are particularly popular in this field.However,training autoencoder models for each turbine is time-consuming and resource intensive.Thus,transfer learning becomes essential for wind turbines with limited data or applications with limited computational resources.This study examines how cross-turbine transfer learning can be applied to autoencoder-based anomaly detection.Here,autoencoders are combined with constant thresholds for the reconstruction error to determine if input data contains an anomaly.The models are initially trained on one year’s worth of data from one or more source wind turbines.They are then fine-tuned using small amounts of data from the target wind turbine.Three methods for fine-tuning are investigated:adjusting the entire autoencoder,only the decoder,or only the threshold of the model.The performance of the transfer learning models is compared to baseline models that were trained on one year’s worth of data from the target wind turbine.The results of the tests conducted in this study indicate that models trained on data of multiple wind turbines do not improve the anomaly detection capability compared to models trained on data of one source wind turbine.In addition,modifying the model’s threshold can lead to comparable or even superior performance compared to the baseline,whereas fine-tuning the decoder or autoencoder further enhances the models’performance.

Industrial IoT Based Condition Monitoring for Wind Energy Conversion System

Wind energy has been identified as the second dominating source in the world renewable energy generation after hydropower.Conversion and distribution of wind energy has brought technology revolution by developing the advanced wind energy conversion system(WECS)including multilevel inverters(MLIs).The conventional rectifier produces ripples in their output waveforms while the MLI suffers from voltage balancing issues across the DC-link capacitor.This paper proposes a simplified proportional integral(PI)-based space vector pulse width modulation(SVPWM)to minimize the output waveform ripples,resolve the voltage balancing issue and produce better-quality output waveforms.WECS experiences various types of faults particularly in the DC-link capacitor and switching devices of the power converter.These faults,if not detected and rectified at an early stage,may lead to catastrophic failures to the WECS and continuity of the power supply.This paper proposes a new algorithm embedded in the proposed PI-based SVPWM controller to identify the fault location in the power converter in real time.Since most wind power plants are located in remote areas or offshore,WECS condition monitoring needs to be developed over the internet of things(IoT)to ensure system reliability.In this paper,an industrial IoT algorithm with an associated hardware prototype is proposed to monitor the condition of WECS in the real-time environment.

Deep anomaly detection in horizontal axis wind turbines using GraphConvolutional Autoencoders for Multivariate Time series

Wind power is one of the fastest-growing renewable energy sectors instrumental in the ongoing decarbonizationprocess. However, wind turbines are subjected to a wide range of dynamic loads which can cause more frequentfailures and downtime periods, leading to ever-increasing attention to effective Condition Monitoring strategies.In this paper, we propose a novel unsupervised deep anomaly detection framework to detect anomalies in windturbines based on SCADA data. We introduce a promising neural architecture, namely a Graph ConvolutionalAutoencoder for Multivariate Time series, to model the sensor network as a dynamical functional graph. Thisstructure improves the unsupervised learning capabilities of Autoencoders by considering individual sensormeasurements together with the nonlinear correlations existing among signals. On this basis, we developeda deep anomaly detection framework that was validated on 12 failure events occurred during 20 months ofoperation of four wind turbines. The results show that the proposed framework successfully detects anomaliesand anticipates SCADA alarms by outperforming other two recent neural approaches.

数字孪生驱动的风力发电机远程监控系统研究

针对目前风力发电机监控系统可视化程度低、数据无法进行远程实时传输等问题;提出一种数字孪生驱动的风力发电机远程监控系统框架,从6个模块进行研究。通过利用实体建模、模型轻量化、行为规则模型和动态模型构建、远程实时数据传输、Unity3D引擎可视化等技术,开发数字孪生驱动的风力发电机远程监控系统。最后,通过实验验证系统的可行性和有效性,实现了实时动态状态监测、故障报警、人机交互的风力发电机的远程智能化监控。

Vibration-based synchronous sampling and its application in wind-turbine drive-train-condition monitoring

Utilizing shaft-speed information to analyse vibration signals is an important method for fault diagnosis and condition monitoring of rotating machineries,especially for those running at variable speeds.However,in many cases,shaft-speed information is not always available,for a variety of reasons.Fortunately,in most of the measurements,the shaft-speed information is embedded in the vibration response in many different forms,such as in the format of the fundamental shaft-rotation-frequency response and its harmonics,and the gear-meshing-frequency response and its harmonics,etc.Proper signal processing can be used to extract the shaft instantaneous speed from the measured vibration responses.In existing instantaneous shaft-speed-identification methods,a narrow-bandpass filtering technique is used explicitly or implicitly.In a complex gearbox system,such as that used in a wind turbine,the gear-meshing-response component could be modulated by many other shaft speeds,due to the configuration of the gearbox or due to the existence of component damage.As a result,it is very difficult to isolate a single vibration-response component for instantaneous shaft-speed detection.In this paper,an innovative approach is presented.The instantaneous shaft speed is extracted based on maxima tracking from the vibration-response spectrogram.A numerical integration scheme is employed to obtain the shaft instantaneous phase.Digital-domain synchronous resampling is then applied to the vibration data by using the instantaneous phase information.Due to the nature of noise suppression in the numerical integration,the accuracy of synchronous sampling is greatly improved.This proposed approach demonstrates the feasibility and engineering applicability through a controlled laboratory test case and two field wind-turbine cases.More detailed results and conclusions of this research are presented at the end of this paper.

基于谐波消除和谱峭度的风电机组主轴承故障诊断

针对风电机组状态监测中主轴轴承故障特征受到其他传动部件振动谐波干扰且原始振动信号非平稳难以准确提取故障冲击特征频率的问题,提出一种基于谐波干扰消除和谱峭度相结合的分析方法(HR-Fast-Kurtogram)。利用离线角域重采样将非平稳时域信号转换成平稳的角度-阶次信号,在阶次域通过梳状陷波器其去除齿箱啮合成分等干扰阶次,应用基于短时傅里叶变换(STFT)的快速谱峭度图提取峭度峰值所在故障子频带从而提取轴承故障特征。既可避免非周期性冲击的干扰,亦可区分冲击性较小的非轴承幅值调制成分和轴承故障冲击成分。在实际机组轴承故障数据应用中的对比分析结果表明:新方法可提高在秒级短时间尺度提取轴承故障特征和识别故障类型的准确性。