Wind Turbine Condition Monitoring Research Articles

Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data

Abstract This paper presents a new methodology – based on cointegration analysis of Supervisory Control And Data Acquisition (SCADA) data – for condition monitoring and fault diagnosis of wind turbines. Analysis of cointegration residuals – obtained from cointegration process of wind turbine data – is used for operational condition monitoring and automated fault and/or abnormal condition detection. The proposed method is validated using the experimental data acquired from a wind turbine drivetrain with a nominal power of 2 MW under varying environmental and operational conditions. A two-stage cointegration-based procedure is performed on six process parameters of the wind turbine, where data trends have nonlinear characteristics. The method is tested using two case studies with known faults. The results demonstrate that the proposed method can effectively analyse nonlinear data trends, continuously monitor the wind turbine and reliably detect abnormal problems.

Fault diagnosis and condition monitoring of wind turbines

SummaryThis paper describes a model‐free method for the fault diagnosis and condition monitoring of rotor systems in wind turbines. Both fault diagnosis and monitoring can be achieved without using a model for the wind turbine, applied controller, or wind profiles. The method is based on measurements from standard sensors on modern wind turbines, including moment sensors and rotor angle sensors. This approach will allow the method to be applied to existing wind turbines without any modifications.The method is based on the detection of asymmetries in the rotor system caused by changes or faults in the rotor system. A multiblade coordinate transformation is used directly on the measured flap‐wise and edge‐wise moments followed by signal modulation. Changes or faults in the rotor system will result in unique signatures in the set of modulation signals. These signatures are described through the amplitudes and phase information of the modulation signals. It is possible to detect and isolate which blade is faulty or has been changed based on these signatures. Furthermore, the faulty component can be isolated, ie, the actuator, sensor or blade, and the type of fault can be determined. The method can be used both on‐ and off‐line.

A health condition model for wind turbine monitoring through neural networks and proportional hazard models

In this article, a parametric model for health condition monitoring of wind turbines is developed. The study is based on the assumption that a wind turbine’s health condition can be modeled through three features: rotor speed, gearbox temperature and generator winding temperature. At first, three neural network models are created to simulate normal behavior of each feature. Deviation signals are then defined and calculated as accumulated time-series of differences between neural network predictions and actual measurements. These cumulative signals carry health condition–related information. Next, through nonlinear regression technique, the signals are used to produce individual models for considered features, which mathematically have the form of proportional hazard models. Finally, they are combined to construct an overall parametric health condition model which partially represents health condition of the wind turbine. In addition, a dynamic threshold for the model is developed to facilitate and add more insight in performance monitoring aspect. The health condition monitoring of wind turbine model has capability of evaluating real-time and overall health condition of a wind turbine which can also be used with regard to maintenance in electricity generation in electric power systems. The model also has flexibility to overcome current challenges such as scalability and adaptability. The model is verified in illustrating changes in real-time and overall health condition with respect to considered anomalies by testing through actual and artificial data.

Ageing assessment of a wind turbine over time by interpreting wind farm SCADA data

Abstract Ageing of a wind turbine and its components is inevitable. It will affect the reliability and power generation of the turbine over time. Therefore, performing the ageing assessment of wind turbines is of significance not only to optimize the operation and maintenance strategy of the wind turbine but also to improve the management of a wind farm. However, in contrast to the significant number of research on wind turbine condition monitoring and reliability analysis, little effort was made before to investigate the ageing led performance degradation issue of wind turbines over time. To fill such a technology gap, four SCADA-based wind turbine ageing assessment criteria are proposed in this paper for measuring the ageing resultant performance degradation of the turbine. With the aid of these four criteria, a reliable information fusion based wind turbine ageing assessment method is developed and verified in the end using the real wind farm SCADA data. It has been shown that the proposed method is effective and reliable in performing the ageing assessment of a wind turbine.

An artificial neural network‐based condition monitoring method for wind turbines, with application to the monitoring of the gearbox

AbstractMajor failures in wind turbines are expensive to repair and cause loss of revenue due to long downtime. Condition‐based maintenance, which provides a possibility to reduce maintenance cost, has been made possible because of the successful application of various condition monitoring systems in wind turbines. New methods to improve the condition monitoring system are continuously being developed. Monitoring based on data stored in the supervisory control and data acquisition (SCADA) system in wind turbines has received attention recently. Artificial neural networks (ANNs) have proved to be a powerful tool for SCADA‐based condition monitoring applications. This paper first gives an overview of the most important publications that discuss the application of ANN for condition monitoring in wind turbines. The knowledge from these publications is utilized and developed further with a focus on two areas: the data preprocessing and the data post‐processing. Methods for filtering of data are presented, which ensure that the ANN models are trained on the data representing the true normal operating conditions of the wind turbine. A method to overcome the errors from the ANN models due to discontinuity in SCADA data is presented. Furthermore, a method utilizing the Mahalanobis distance is presented, which improves the anomaly detection by considering the correlation between ANN model errors and the operating condition. Finally, the proposed method is applied to case studies with failures in wind turbine gearboxes. The results of the application illustrate the advantages and limitations of the proposed method. Copyright © 2017 John Wiley & Sons, Ltd.

Wind turbine prognostics and maintenance management based on a hybrid approach of neural networks and a proportional hazards model

This paper proposes an approach for stress condition monitoring and maintenance assessment in wind turbines (WTs) through large amounts of collected data from the supervisory control and data acquisition (SCADA) system. The objectives of the proposed approach are to provide a stress condition model for health monitoring, to assess the WT’s maintenance strategies, and to provide recommendations on current maintenance schemes for future operations of the wind farm. At first, several statistical techniques, namely principal component analysis, Pearson, Spearman and Kendall correlations, mutual information, regressional ReliefF and decision trees are used and compared to assess the data for dimensionality reduction and parameter selection. Next, a normal behavior model is constructed by an artificial neural network which performs condition monitoring analysis. Then, a model based on the mathematical form of a proportional hazards model is developed where it represents the stress condition of the WT. Finally, those two models are jointly employed in order to analyze the overall performance of the WT over the study period. Several cases are analyzed with five-year SCADA data and maintenance information is utilized to develop and validate the proposed approach.

Precise feature extraction from wind turbine condition monitoring signals by using optimised variational mode decomposition

Reliable condition monitoring (CM) highly relies on the correct extraction of fault-related features from CM signals. This equally applies to the CM of wind turbines (WTs). Although influenced by slowly rotating speeds and constantly varying loading, extracting fault characteristics from lengthy, nonlinear, non-stationary WT CM signals is extremely difficult, which makes WT CM one of the most challenge tasks in wind power asset management despites that lots of efforts have been spent. Attributed to the superiorities to empirical mode decomposition and its extension form Hilbert–Huang transform in dealing with nonlinear, non-stationary CM signals, the recently developed variational mode decomposition (VMD) casts a glimmer of light for the solution for this issue. However, the original proposed VMD adopts default values for both number of modes and filter frequency bandwidth. It is not adaptive to the signal being inspected. As a consequence, it would lead to inaccurate feature extraction thus unreliable WT CM result sometimes. For this reason, a precise feature extraction method based on optimised VMD is investigated. The experiments have shown that thanks to the use of the proposed optimisation strategies, the fault-related features buried in WT CM signals have been extracted out successfully.

Using SCADA data for wind turbine condition monitoring – a review

The ever increasing size of wind turbines and the move to build them offshore have accelerated the need for optimised maintenance strategies in order to reduce operating costs. Predictive maintenance requires detailed information on the condition of turbines. Due to the high costs of dedicated condition monitoring systems based on mainly vibration measurements, the use of data from the turbine supervisory control and data acquisition (SCADA) system is appealing. This review discusses recent research using SCADA data for failure detection and condition monitoring (CM), focussing on approaches which have already proved their ability to detect anomalies in data from real turbines. Approaches are categorised as (i) trending, (ii) clustering, (iii) normal behaviour modelling, (iv) damage modelling and (v) assessment of alarms and expert systems. Potential for future research on the use of SCADA data for advanced turbine CM is discussed.

Classification of operating conditions of wind turbines for a class-wise condition monitoring strategy

Relevant classification of the stationary operating conditions of wind turbines (WTs) aids in the selection of an optimal condition monitoring technique. This paper presents a general method that can be used to classify the operating conditions of WTs in terms of rotor speed and power. In this study, the ideal probability density functions (PDFs) of rotor speed and power are calculated using an analytic WT model and a wind speed profile. To estimate the PDFs of rotor speed and power with field data, two methods are employed: (1) empirical PDF-based and (2) Gaussian mixture model (GMM)-based. The individual PDFs estimated by the two methods are used to quantitatively define the range of the stationary WT operating conditions. The proposed methods and the range of stationary operating conditions established by the methods were evaluated using data from an analytical WT model and an actual 2.5 megawatt WT in the field. In addition, the paper presents the evaluation of the performance of the proposed class-wise condition monitoring strategy when used with vibration signals acquired from a two kilowatt WT testbed. In summary, the proposed strategy and methods are promising for effective condition monitoring of WTs.

Oil Debris Signals Enhancement Based On Wavelet Analysis for Wind Turbine Condition Monitoring

Oil debris signals are applied to monitor lubricant oil conditions and fault diagnosis of a wind turbine. However, the signals will be influenced by the vibration of transmission mechanism and background noise, hence false alarms and undetected particles will limit sensors ability in examining fine particles in oil debris. This paper presents an approach to enhance the performance of oil debris signals. The de-noising of signals used a wavelet filtering to remove the vibration of a wind turbine, and then set a threshold to remove the background noise which caused by the system of measurement. The effectiveness of this enhanced measurement system is tested by using simulated and experimental signals.

Development of a low cost test rig for standalone WECS subject to electrical faults

In this paper, a contribution to the development of low-cost wind turbine (WT) test rig for stator fault diagnosis of wind turbine generator is proposed. The test rig is developed using a 2.5kW, 1750 RPM DC motor coupled to a 1.5kW, 1500 RPM self-excited induction generator interfaced with a WT mathematical model in LabVIEW. The performance of the test rig is benchmarked with already proven wind turbine test rigs. In order to detect the stator faults using non-stationary signals in self-excited induction generator, an online fault diagnostic technique of DWT-based multi-resolution analysis is proposed. It has been experimentally proven that for varying wind conditions wavelet decomposition allows good differentiation between faulty and healthy conditions leading to an effective diagnostic procedure for wind turbine condition monitoring.

Online condition monitoring of wind turbines through three-phase electrical signature analysis

In the context of the KAStrion European project, a complete solution was proposed in order to monitor wind turbines. The developed solution comprises both hardware and software parts of the condition monitoring system. In terms of software, modules for vibration analysis and electrical signal analysis have been developed. The current paper presents the electrical analysis solution proposed in the context of this project. The electrical module is able to detect both mechanical and electrical faults in a wind turbine system. The goal of the mechanical fault detection using electrical signals is to confirm the faults also detected by vibration analysis, while the main focus of the module remains the detection of electrical faults. Results showing the performance of mechanical fault detection are presented using electrical signals acquired on the test-bench developed for testing the KAStrion system. Moreover, results regarding the electrical unbalance are presented using signals acquired on a three-phase transformer. The final solution has been implemented on two onshore wind turbines since the end of 2014, and online condition monitoring results are presented at the end of the paper.

A New Fault Diagnosis Algorithm for PMSG Wind Turbine Power Converters under Variable Wind Speed Conditions

Although Permanent Magnet Synchronous Generator (PMSG) wind turbines (WTs) mitigate gearbox impacts, they requires high reliability of generators and converters. Statistical analysis shows that the failure rate of direct-drive PMSG wind turbines’ generators and inverters are high. Intelligent fault diagnosis algorithms to detect inverters faults is a premise for the condition monitoring system aimed at improving wind turbines’ reliability and availability. The influences of random wind speed and diversified control strategies lead to challenges for developing intelligent fault diagnosis algorithms for converters. This paper studies open-circuit fault features of wind turbine converters in variable wind speed situations through systematic simulation and experiment. A new fault diagnosis algorithm named Wind Speed Based Normalized Current Trajectory is proposed and used to accurately detect and locate faulted IGBT in the circuit arms. It is compared to direct current monitoring and current vector trajectory pattern approaches. The results show that the proposed method has advantages in the accuracy of fault diagnosis and has superior anti-noise capability in variable wind speed situations. The impact of the control strategy is also identified. Experimental results demonstrate its applicability on practical WT condition monitoring system which is used to improve wind turbine reliability and reduce their maintenance cost.

Superiorities of variational mode decomposition over empirical mode decomposition particularly in time–frequency feature extraction and wind turbine condition monitoring

Due to constantly varying wind speed, wind turbine (WT) components often operate at variable speeds in order to capture more energy from wind. As a consequence, WT condition monitoring (CM) signals always contain intra-wave features, which are difficult to extract through performing conventional time–frequency analysis (TFA) because none of which is locally adaptive. So far, only empirical mode decomposition (EMD) and its extension forms can extract intra-wave features. However, the EMD and those EMD-based techniques also suffer a number of defects in TFA (e.g. weak robustness of against noise, unidentified ripples, inefficiency in detecting side-band frequencies etc.). The existence of these issues has significantly limited the extensive application of the EMD family techniques to WT CM. Recently, an alternative TFA method, namely variational mode decomposition (VMD), was proposed to overcome all these issues. The purpose of this study is to verify the superiorities of the VMD over the EMD and investigate its potential application to the future WT CM. Experiment has shown that the VMD outperforms the EMD not only in noise robustness but also in multi-component signal decomposition, side-band detection, and intra-wave feature extraction. Thus, it has potential as a promising technique for WT CM.

Reducing sensor complexity for monitoring wind turbine performance using principal component analysis

Availability and reliability are among the priority concerns for deployment of distributed generation (DG) systems, particularly when operating in a harsh environment. Condition monitoring (CM) can meet the requirement but has been challenged by large amounts of data needing to be processed in real time due to the large number of sensors being deployed. This paper proposes an optimal sensor selection method based on principal component analysis (PCA) for condition monitoring of a DG system oriented to wind turbines. The research was motivated by the fact that salient patterns in multivariable datasets can be extracted by PCA in order to identify monitoring parameters that contribute the most to the system variation. The proposed method is able to correlate the particular principal component to the corresponding monitoring variable, and hence facilitate the right sensor selection for the first time for the condition monitoring of wind turbines. The algorithms are examined with simulation data from PSCAD/EMTDC and SCADA data from an operational wind farm in the time, frequency, and instantaneous frequency domains. The results have shown that the proposed technique can reduce the number of monitoring variables whilst still maintaining sufficient information to detect the faults and hence assess the system’s conditions.

Current signature analysis and its application in the condition monitoring of wind turbine for rotor faults

This paper proposes a condition monitoring technique using current signature analysis for diagnosis of Rotor eccentricity fault in Squirrel cage induction generators used in wind power systems. The proposed method measures the current characteristic frequencies of the excitations generated by a Wind turbine generator. The excitations generated by wind turbine generator eccentricity fault are extracted and analyzed under no load, full load and varying wind conditions. Side bands are calculated using FFT approach. Fault frequencies are further analyzed using wavelet-based analysis for steady state and dynamic conditions. Multi-resolution mean power indicator has been used for the quantification of the fault. It has been experimentally proven that for varying wind conditions wavelet decomposition allows good differentiation between faulty and healthy conditions leading to an effectual diagnostic procedure for condition monitoring.

Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines

Data collected from the supervisory control and data acquisition (SCADA) system are used widely in wind farms to obtain operation and performance information about wind turbines. The paper presents a three-way model by means of parallel factor analysis (PARAFAC) for wind turbine fault detection and sensor selection, and evaluates the method with SCADA data obtained from an operational farm. The main characteristic of this new approach is that it can be used to simultaneously explore measurement sample profiles and sensors profiles to avoid discarding potentially relevant information for feature extraction. With K-means clustering method, the measurement data indicating normal, fault and alarm conditions of the wind turbines can be identified, and the sensor array can be optimised for effective condition monitoring.

Wind turbine condition monitoring and fault diagnosis in China

With the advent of a more severe energy crisis and environment contamination, wind energy, as one of the green and renewable energy resources, has attracted more and more attention worldwide [1], [2]. According to statistics issued by the Global Wind Energy Council (GWEC), by the end of 2014, the global installed capacity had reached 360,000 MW, while the installed capacity in China accounted for 114,763 MW, nearly one third of the total, as illustrated in Fig. 1. Meanwhile, it is envisioned in 2050 Blueprint of Wind Power Development in China that by the years 2030 and 2050, the scale of installed capacity will exceed 4 × 105 MW and 106 MW, respectively, so as to meet 8.4% and 17% of the demand for electricity nationwide, enabling wind energy to be one of five main energy resources.

Vibration condition monitoring system for wind turbine bearings based on noise suppression with multi-point data fusion

A noise suppression method for feature frequency extraction that is supplemented with multi-point data fusion was investigated in consideration of issues involving wind turbine vibration signals subject to high noise disturbance. The difficulty of extracting early weak fault features was examined as well. First, a de-noising and feature extraction method that uses EMD-Correlation was developed by adding empirical mode decomposition (EMD) and autocorrelation de-noising to the wavelet package transform under the effects of white noise and short-term disturbance noise in wind turbine vibration signals. Second, an EMD-Correlation analysis model for feature frequency extraction supplemented with multi-point data fusion was established with reference to adaptive resonance theory-2 to highlight the feature frequency of a possible early weak fault. Third, the results obtained with the actual and simulated fault vibration signals of wind turbine bearing faults and the outcomes of comparing the different feature frequency extraction methods show that the proposed method of EMD-Correlation that is supplemented with multi-point data fusion can not only effectively reduce white noise and short-term disturbance noise but can also extract the feature frequency of early weak faults. Finally, prototype hardware and software were developed for a wind turbine condition monitoring system based on the aforementioned fault feature extraction algorithms and tested in an actual wind turbine generation system.

Wind Turbine Fault diagnosis Techniques and Related Algorithms

This paper reviewed different types of faults that might occur in wind turbine systems. Moreover, the symptoms of the common faults and related diagnosis techniques are discussed. Meanwhile, this paper mainly aims to survey the most recent condition and performance monitoring approaches of wind turbines with the primary focus on blade, gearbox, generator, braking system, and rotor. Different methodologies of wind turbine condition monitoring, performance monitoring, and fault diagnosis reported in the literature are discussed This paper reviewed different types of faults that might occur in wind turbine systems. Moreover, the symptoms of the common faults and related diagnosis techniques are discussed. Meanwhile, this paper mainly aims to survey the most recent condition and performance monitoring approaches of wind turbines with the primary focus on blade, gearbox, generator, braking system, and rotor. Different methodologies of wind turbine condition monitoring, performance monitoring, and fault diagnosis reported in the literature are discussed