Wind Turbine Condition Research Articles

Data‐driven technique for interpreting wind turbine condition monitoring signals

Increasing deployment of large wind turbines (WT) offshore and in remote areas requires reliable condition monitoring (CM) techniques to guarantee the high availability of these WTs and economic return. To meet this need, much effort has been expended to improve the capability of analysing the WT CM signals. However, a fully satisfactory technique has not been achieved today. One of the major reasons is that the developed techniques still cannot provide accurate interpretation of the WT CM signals, which are usually non-linear and non-stationary in nature because of the constantly varying loads and non-linear operations of the turbines. To deal with this issue, a new data-driven signal processing technique is developed in this study based on the concepts of intrinsic time-scale decomposition (ITD) and energy operator separation algorithm (EOSA). The advantages of the proposed technique over the traditional data-driven techniques have been demonstrated and validated experimentally. It has been shown that in comparison with the Hilbert–Huang transform the combination of ITD and EOSA provided more accurate and explicit presentations of the instantaneous information of the signals tested. Thus, it provides a much improved offline tool for accurately interpreting WT CM signals.

A Wind Turbine Fault Detection Approach Based on Cluster Analysis and Frequent Pattern Mining

Wind energy has proven its viability by the emergence of countless wind turbines around the world which greatly contribute to the increased electrical generating capacity of wind farm operators. These infrastructures are usually deployed in not easily accessible areas; therefore, maintenance routines should be based on a well-guided decision so as to minimize cost. To aid operators prior to the maintenance process, a condition monitoring system should be able to accurately reflect the actual state of the wind turbine and its major components in order to execute specific preventive measures using as little resources as possible. In this paper, we propose a fault detection approach which combines cluster analysis and frequent pattern mining to accurately reflect the deteriorating condition of a wind turbine and to indicate the components that need attention. Using SCADA data, we extracted operational status patterns and developed a rule repository for monitoring wind turbine systems. Results show that the proposed scheme is able to detect the deteriorating condition of a wind turbine as well as to explicitly identify faulty components.

Dynamic Stall Modeling for the Conditions of Vertical Axis Wind Turbines

Unsteady aerodynamics imposes additional demands on the modeling of vertical axis wind turbines. Large variations in the angles of attack of the blades cause force oscillations, which can lead to the fatigue-associated problems. Therefore, an accurate estimation of the dynamic loads is essential for the vertical axis wind turbines design. Dynamic stall modeling is in focus because it represents complex phenomena associated with the unsteady flow conditions. The purpose of the study is to find a suitable dynamic stall model for the vertical axis wind turbine conditions. Three versions of the Leishman–Beddoes model are explicitly presented. Additional modifications are implemented for the model to work when the angles of attack change sign and have high amplitudes. All the model parameters are presented. The model is assessed against measured data. The conditions for the simulation tests are close to the vertical axis wind turbine operational conditions. A version of the model, originally designed for low Mach numbers, is the most accurate throughout a number of tests.

A SMART Software Package for Maintenance Optimisation of Offshore Wind Turbines

Offshore Wind Turbine (OWT) maintenance costs in between 20 – 35% of the lifetime power generation cost. Many techniques and tools that are being developed to curtail this cost are challenged by the stochastic climatic conditions of offshore location and the wind energy market. A generic and OWT centric software packages that can smartly adapt to the requirement of any offshore wind farm and optimise its maintenance, logistics and spares-holding while giving due consideration to offshore climate and market conditions will enable OWT operators to centralise their operation and maintenance planning and make significant cost reductions. This work aims to introduce the idea of a comprehensive tool that can meet the above objectives, and give examples of data and functions required. The package uses wind turbine condition monitoring data to anticipate component failure and proposes a time and maintenance implementation strategies that is developed as per the requirements of HSE and government regulations for working in the offshore locations and at heights. The software database contains key failure analysis data that will be an invaluable asset for future researchers, turbine manufacturers and operators, that will optimise OWT power generation cost and better understand OWT working. The work also lists some prevalent tools and techniques developed by industries and researchers for the wind industry.

Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 2: Application examples

This paper is part two of a two part series. The originality of part one was the proposal of a novelty approach for wind turbine supervisory control and data acquisition (SCADA) data mining for condition monitoring purposes. The novelty concerned the usage of adaptive neuro-fuzzy interference system (ANFIS) models in this context and the application of a proposed procedure to a wide range of different SCADA signals. The applicability of the set up ANFIS models for anomaly detection was proven by the achieved performance of the models. In combination with the fuzzy interference system (FIS) proposed the prediction errors provide information about the condition of the monitored components.Part two presents application examples illustrating the efficiency of the proposed method. The work is based on continuously measured wind turbine SCADA data from 18 modern type pitch regulated wind turbines of the 2 MW class covering a period of 35 months. Several real life faults and issues in this data are analyzed and evaluated by the condition monitoring system (CMS) and the results presented. It is shown that SCADA data contain crucial information for wind turbine operators worth extracting. Using full signal reconstruction (FSRC) adaptive neuro-fuzzy interference system (ANFIS) normal behavior models (NBM) in combination with fuzzy logic (FL) a setup is developed for data mining of this information. A high degree of automation can be achieved. It is shown that FL rules established with a fault at one turbine can be applied to diagnose similar faults at other turbines automatically via the CMS proposed. A further focus in this paper lies in the process of rule optimization and adoption, allowing the expert to implement the gained knowledge in fault analysis. The fault types diagnosed here are: (1) a hydraulic oil leakage; (2) cooling system filter obstructions; (3) converter fan malfunctions; (4) anemometer offsets and (5) turbine controller malfunctions. Moreover, the graphical user interface (GUI) developed to access, analyze and visualize the data and results is presented.

Key Technology in Wind Turbine Transmission System Condition Monitoring: A Review

Key technology for the transmission system of wind turbine condition monitoring is researched in this paper. It gives a summary overview, including monitoring methods, signal processing methods and state recognition technology, data acquisition and transmission technology, programming languages. Understanding of the progress of the field is to provide technical reference and research inspiration for insiders.

Copula‐based model for wind turbine power curve outlier rejection

ABSTRACTPower curve measurements provide a conventional and effective means of assessing the performance of a wind turbine, both commercially and technically. Increasingly high wind penetration in power systems and offshore accessibility issues make it even more important to monitor the condition and performance of wind turbines based on timely and accurate wind speed and power measurements. Power curve data from Supervisory Control and Data Acquisition (SCADA) system records, however, often contain significant measurement deviations, which are commonly produced as a consequence of wind turbine operational transitions rather than stemming from physical degradation of the plant. Using such raw data for wind turbine condition monitoring purposes is thus likely to lead to high false alarm rates, which would make the actual fault detection unreliable and would potentially add unnecessarily to the costs of maintenance. To this end, this paper proposes a probabilistic method for excluding outliers, developed around a copula‐based joint probability model. This approach has the capability of capturing the complex non‐linear multivariate relationship between parameters, based on their univariate marginal distributions; through the use of a copula, data points that deviate significantly from the consolidated power curve can then be removed depending on this derived joint probability distribution. After filtering the data in this manner, it is shown how the resulting power curves are better defined and less subject to uncertainty, whilst broadly retaining the dominant statistical characteristics. These improved power curves make subsequent condition monitoring more effective in the reliable detection of faults. Copyright © 2013 John Wiley & Sons, Ltd.

S-Transform and its contribution to wind turbine condition monitoring

Condition monitoring (CM) has long been recognised as one of the best methods of reducing the operation and maintenance (O&M) costs of wind turbines (WTs). However, its potential in the wind industry has not been fully exploited. One of the major reasons is due to the lack of an efficient tool to properly process the WT CM signals, which are usually non-stationary in both time and frequency domains owing to the constantly varying operational and loading conditions experienced by WTs. For this reason, S-transform and its potential contribution to WT CM are researched in this paper. Following the discussion of the superiorities of S-transform to the Short-Time Fourier Transform (STFT) and Wavelet Transform, two S-transform based CM techniques are developed, dedicated for use on WTs. One is for tracking the energy variations of those fault-related characteristic frequencies under varying operational conditions (the energy rise of these frequencies usually indicates the presence of a fault); another is for assessing the health condition of WT gears and bearings, which have shown significant reliability issues in both onshore and offshore wind projects. In the paper, both proposed techniques have been verified experimentally, showing that they are valid for detecting both the mechanical and electrical faults occurring in the WT despite its varying operational and loading conditions.

The vibration analysis of wind turbine blade–cabin–tower coupling system

Renewable energy sources especially wind energy have gained much attention due to the recent energy crisis and the urge to obtain clean energy. To maintain the wind turbine in operation healthily, implementation of condition monitoring system and fault detection system is necessary. In wind turbine condition monitoring, vibration analysis is a common and affective way to apply in the feature extraction and fault diagnosis, especially in the rotation parts. The blade–cabin–tower coupling system is analyzed in this paper. At first, the coordinate system and the kinetic equation are established. The tower natural frequency is calculated based on the coordinate system and the random following wind vibration is analyzed. In the end, the total wind force in blade–cabin–tower coupling system is solved.

Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine

Fault diagnosis for wind turbine transmission systems is an important task for reducing their maintenance cost. However, the non-stationary dynamic operating conditions of wind turbines pose a challenge to fault diagnosis for wind turbine transmission systems. In this paper, a novel fault diagnosis method based on manifold learning and Shannon wavelet support vector machine is proposed for wind turbine transmission systems. Firstly, mixed-domain features are extracted to construct a high-dimensional feature set characterizing the properties of non-stationary vibration signals from wind turbine transmission systems. Moreover, an effective manifold learning algorithm with non-linear dimensionality reduction capability, orthogonal neighborhood preserving embedding (ONPE), is applied to compress the high-dimensional feature set into low-dimensional eigenvectors. Finally, the low-dimensional eigenvectors are inputted into a Shannon wavelet support vector machine (SWSVM) to recognize faults. The performance of the proposed method was proved by successful fault diagnosis application in a wind turbine's gearbox. The application results indicated that the proposed method improved the accuracy of fault diagnosis.

Supervisory control and data acquisition data‐based non‐linear state estimation technique for wind turbine gearbox condition monitoring

Offshore wind energy is catching increasing worldwide interest. However, access and maintenance offshore can be difficult and will be more costly than onshore, and hence, availability is correspondingly lower. As a result, there is a growing interest in wind turbine condition monitoring with condition-based rather than responsive and scheduled maintenance. A non-linear state estimation technique (NSET) model is presented here to model a healthy wind turbine gearbox using stored historical data. These data capture the inter-relationship between the model input and output parameters. The state vectors comprising the data should cover as much as turbine operational range, including the extreme conditions in order to obtain an accurate model performance. A model so constructed can be applied to assess the operational data. Welch's t-test is employed in the fault detection algorithm, together with suitable time series filtering, to identify incipient anomalies in the turbine gearbox before they develop into catastrophic faults. Two case studies based on 10-minute supervisory control and data acquisition data from a commercial wind farm are presented to demonstrate the model's effectiveness. Comparison is made with neural network modelling, and the NSET approach is demonstrated to be superior.

A computational framework for life-cycle management of wind turbines incorporating structural health monitoring

The integration of structural health monitoring into life-cycle management strategies can help facilitating a reliable operation of wind turbines and reducing the life-cycle costs significantly. This article presents a life-cycle management framework for online monitoring and performance assessment of wind turbines, enabling optimum maintenance and inspection planning at minimum associated life-cycle costs. Incorporating continuously updated monitoring data (i.e. structural, environmental, and operational data), the framework allows capturing and understanding the actual wind turbine condition and, hence, reduces uncertainty in structural responses as well as load effects acting on the structure. As will be shown in this article, the framework integrates a variety of heterogeneous hardware and software components, including sensors and data acquisition units, server systems, Internet-enabled user interfaces as well as finite element models for system identification, and a multiagent system for self-detecting sensor malfunctions. To validate its capabilities and to demonstrate its practicability, the framework is deployed for continuous monitoring and life-cycle management of a 500-kW wind turbine. Remote life-cycle analyses of the monitored wind turbine are conducted, and case studies are presented investigating both the structural performance and the operational efficiency of the wind turbine.

Wind Turbine Condition and Power Quality Monitoring by the Approach of Fast Individual Harmonic Extraction

Increasing deployment of wind turbines requires efficient condition monitoring to ensure the safety and availability of these machines. Grid code also requests the operator to enhance the monitoring of the quality of the power generated by the wind turbine. Most commercially available wind turbine condition monitoring systems are supported by a number of vibration transducers. They consequently are complex in hardware, expensive in price, but inefficient in computation and particularly lack of power quality monitoring ability. In view of this, an innovative electrical signal analysis-based wind turbine condition and power quality monitoring technique is developed in this paper by the approach of individual harmonic extraction. The proposed technique has been verified in the lab by applying to detecting both the mechanical and electrical faults emulated on a specially designed wind turbine condition monitoring test rig. Experiments show that the proposed technique is not only sensitive to the faults, but alert to the degradation of power quality.

Inverse Force Estimation in a New Lab Simulation Approach for Vibration Prediction of Building-Mounted Small Wind Turbine

The structural vibration generated by building mounted small wind turbine was fully investigated by author. In order to achieve this, the real vibration condition of wind turbine in field was accurately simulated in Lab for research. There are some difficulty during the whole simulation process for further quantification of vibration levels. One of the main concerns was the correct estimation of the excitation or contact force at the interface between vibration source (turbine) and receiver (building or walls). However, the relevant simulation method in Lab is not available so far. In this paper, the direct measurement of force was investigated and found unsuitable. A new simulation method was proposed including solving the problem of power supply (DC electric motor used), automatic control of rotational speed of motor and indirect interface force determination. At last, the new established finite element models (FEM) was validated by experimental data with a good initial agreement.

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.

Wind turbine condition monitoring by the approach of SCADA data analysis

Wind turbines are being increasingly deployed in remote onshore and offshore areas due to the richer wind resource there and the advantages of mitigating the land use and visual impact issues. However, site accessing difficulties and the shortage of proper transportation and installation vehicles/vessels are challenging the operation and maintenance of the giants erected at these remote sites. In addition to the continual pressure on lowering the cost of energy of wind, condition monitoring is being regarded as one of the best solutions for the maintenance issues and therefore is attracting significant interest today. Much effort has been made in developing wind turbine condition monitoring systems and inventing dedicated condition monitoring technologies. However, the high cost and the various capability limitations of available achievements have delayed their extensive use. A cost-effective and reliable wind turbine condition monitoring technique is still sought for today. The purpose of this paper is to develop such a technique through interpreting the SCADA data collected from wind turbines, which have already been collected but have long been ignored due to lack of appropriate data interpretation tools. The major contributions of this paper include: (1) develop an effective method for processing raw SCADA data; (2) propose an alternative condition monitoring technique based on investigating the correlations among relevant SCADA data; and (3) realise the quantitative assessment of the health condition of a turbine under varying operational conditions. Both laboratory and site verification tests have been conducted. It has been shown that the proposed technique not only has a potential powerful capability in detecting incipient wind turbine blade and drive train faults, but also exhibits an amazing ability in tracing their further deterioration.

Application of FastICA Algorithm in Wind Turbine Condition Monitoring

In wind turbine condition monitoring, the sensors often can not be installed to the ideal position. Compare the common signal processing method comprehensively and give the advantage of the fastICA algorithm in the wind turbine condition monitoring. Give the basic principle and mathematical model of the fastICA algorithm, while monitor and analysis the wind turbine state data based on the fastICA algorithm. The results show that this algorithm can separate the vibration characteristics of the tested compenent of the wind turbine from the vibration signals quickly and accurately.

풍력발전기 상태 모니터링을 위한 ZigBee 무선 센서노드 및 네트워크

풍력발전기가 경제적, 환경적 요인에 따라 대형화, 해상화 되고 있어 접근이 어렵고, 부품 및 유지보수 비용이 증가하고 있다. 풍력발전기 상태 모니터링을 통하여 고장 요소를 최소화 하고, 고장 시 2차 사고를 예방하여 운영유지 및 보수비용을 낮추고 신뢰성을 증가시켜야 한다. 본 논문에서는 IEC 61400-25-2에서 표준으로 추진하는 풍력발전기 모니터링에 적합한 센서 중 실제 풍력발전기 상태 모니터링에 필요한 온도, 습도, 전압, 전류, 풍향, 풍속 센서를 ZigBee 무선 통신 소자와 결합하여 무선 센서노드를 구성하고 이를 이용한 간단한 네트워크를 통하여 센서 신호를 전송한다. 각 무선 센서노드에서 전송되는 신호는 라우터를 통하여 중앙 모니터링 터미널에 전송한다. 또한 LabVIEW로 신호를 수집 및 처리하고, TCP/IP 통신 변환을 통해 인터넷 환경이면 언제 어디서든지 사용자 및 관리자가 모니터링 할 수 있도록 한다. Because wind turbines are larger and more off-shore construction due to economic and environmental factors, it is more difficult to access the wind turbine as well as the necessary parts and the maintenance costs are increasing. So, we need to minimize fault elements and to prevent a secondary accident at failure through monitoring to reduce maintenance costs and to increase reliability of operation. In this paper we have implemented ZigBee based wireless sensor nodes and network for wind turbine condition monitoring using temperature, humidity, voltage, current, wind direction, and wind speed sensors. ZigBee wireless sensor nodes signals are transmitted to a central monitoring system via routers. Also, the sensor signals are collected and processed using LabVIEW program to monitor the wind turbine conveniently. The administrators and users can monitor the condition of wind turbine at remote site in real time over TCP/IP.

Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description

This paper proposes a system for wind turbine condition monitoring using Adaptive Neuro-Fuzzy Interference Systems (ANFIS). For this purpose: (1) ANFIS normal behavior models for common Supervisory Control And Data Acquisition (SCADA) data are developed in order to detect abnormal behavior of the captured signals and indicate component malfunctions or faults using the prediction error. 33 different standard SCADA signals are used and described, for which 45 normal behavior models are developed. The performance of these models is evaluated in terms of the prediction error standard deviations to show the applicability of ANFIS models for monitoring wind turbine SCADA signals. The computational time needed for model training is compared to Neural Network (NN) models showing the strength of ANFIS in training speed. (2) For automation of fault diagnosis Fuzzy Interference Systems (FIS) are used to analyze the prediction errors for fault patterns. The outputs are both the condition of the component and a possible root cause for the anomaly. The output is generated by the aid of rules that capture the existing expert knowledge linking observed prediction error patterns to specific faults. The work is based on continuously measured wind turbine SCADA data from 18 turbines of the 2 MW class covering a period of 30 months.The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models in this context and the application of the proposed procedure to a wide range of SCADA signals. The applicability of the set up ANFIS models for anomaly detection is proved by the achieved performance of the models. In combination with the FIS the prediction errors can provide information about the condition of the monitored components.In this paper the condition monitoring system is described. Part two will entirely focus on application examples and further efficiency evaluation of the system.

Wind turbine condition monitoring: technical and commercial challenges

ABSTRACTDeployment of larger scale wind turbine systems, particularly offshore, requires more organized operation and maintenance strategies to ensure systems are safe, profitable and cost‐effective. Among existing maintenance strategies, reliability centred maintenance is regarded as best for offshore wind turbines, delivering corrective and proactive (i.e. preventive and predictive) maintenance techniques enabling wind turbines to achieve high availability and low cost of energy. Reliability centred maintenance analysis may demonstrate that an accurate and reliable condition monitoring system is one method to increase availability and decrease the cost of energy from wind. In recent years, efforts have been made to develop efficient and cost‐effective condition monitoring techniques for wind turbines. A number of commercial wind turbine monitoring systems are available in the market, most based on existing techniques from other rotating machine industries. Other wind turbine condition monitoring reviews have been published but have not addressed the technical and commercial challenges, in particular, reliability and value for money. The purpose of this paper is to fill this gap and present the wind industry with a detailed analysis of the current practical challenges with existing wind turbine condition monitoring technology. Copyright © 2012 John Wiley & Sons, Ltd.