Data-Driven Model-Based Fault Diagnosis in a Wind Turbine With Actuator Faults

2014 ◽  
Author(s):  
Hamed Badihi ◽  
Javad Soltani Rad ◽  
Youmin Zhang ◽  
Henry Hong
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Dynamic Models ◽  
Full Range ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Data Driven ◽  
Offshore Wind Turbine ◽  
Model Based ◽  
Highly Nonlinear

Wind turbines are renewable energy conversion devices that are being deployed in greater numbers. However, today’s wind turbines are still expensive to operate, and maintain. The reduction of operational and maintenance costs has become a key driver for applying low-cost, condition monitoring and diagnosis systems in wind turbines. Accurate and timely detection, isolation and diagnosis of faults in a wind turbine allow satisfactory accommodation of the faults and, in turn, enhancement of the reliability, availability and productivity of wind turbines. The so–called model-based Fault Detection and Diagnosis (FDD) approaches utilize system model to carry out FDD in real-time. However, wind turbine systems are driven by wind as a stochastic aerodynamic input, and essentially exhibit highly nonlinear dynamics. Accurate modeling of such systems to be suitable for use in FDD applications is a rather difficult task. Therefore, this paper presents a data-driven modeling approach based on artificial intelligence (AI) methods which have excellent capability in describing complex and uncertain systems. In particular, two data-driven dynamic models of wind turbine are developed based on Fuzzy Modeling and Identification (FMI) and Artificial Neural Network (ANN) methods. The developed models represent the normal operating performance of the wind turbine over a full range of operating conditions. Consequently, a model-based FDD scheme is developed and implemented based on each of the individual models. Finally, the FDD performance is evaluated and compared through a series of simulations on a well-known large offshore wind turbine benchmark in the presence of wind turbulences, measurement noises, and different realistic fault scenarios in the generator/converter torque actuator.

scholarly journals Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

2019 ◽  
Vol 9 (4) ◽  
pp. 783 ◽  
Author(s):  
Silvio Simani ◽  
Paolo Castaldi
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Operating Conditions ◽  
Fault Detection And Isolation ◽  
Offshore Wind ◽  
Data Driven ◽  
Offshore Wind Turbine ◽  
Wind Turbine System ◽  
High Degree ◽  
Offshore Plants

Fault diagnosis of wind turbine systems is a challenging process, especially for offshore plants, and the search for solutions motivates the research discussed in this paper. In fact, these systems must have a high degree of reliability and availability to remain functional in specified operating conditions without needing expensive maintenance works. Especially for offshore plants, a clear conflict exists between ensuring a high degree of availability and reducing costly maintenance. Therefore, this paper presents viable fault detection and isolation techniques applied to a wind turbine system. The design of the so-called fault indicator relies on an estimate of the fault using data-driven methods and effective tools for managing partial knowledge of system dynamics, as well as noise and disturbance effects. In particular, the suggested data-driven strategies exploit fuzzy systems and neural networks that are used to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, which approximate dynamic relations with arbitrary accuracy. The designed fault diagnosis schemes were verified and validated using a high-fidelity simulator that describes the normal and faulty behavior of a realistic offshore wind turbine plant. Finally, by accounting for the uncertainty and disturbance in the wind turbine simulator, a hardware-in-the-loop test rig was used to assess the proposed methods for robustness and reliability. These aspects are fundamental when the developed fault diagnosis methods are applied to real offshore wind turbines.

scholarly journals A novel approach to fault detection and diagnosis on wind turbines

2014 ◽  
Vol 16 (6) ◽  
pp. 1029-1037 ◽  
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Fault Detection And Diagnosis ◽  
Offshore Wind Turbine ◽  
Critical Element ◽  
External Temperature ◽  
Structural Problems ◽  
Detection And Diagnosis ◽  
Pulse Echo

<div> <p>The structure of the wind turbines nowadays is a critical element due to their importance from the reliability, availability, safety, and cost points of view. This is more relevant when the offshore wind turbine is considered. This paper introduces a novel design of a Fault Detection and Diagnosis (FDD) model based on ultrasound technique. The FDD model will be able to detect fault/failures via the pulse-echo technique. The pulse-echo is got via piezoelectric transducers that are also employed as sensors. The signal processing is based on two steps. Firstly, a wavelet transform is applied to the measured signals with filtering purposes, in order to enhance the signal to noise ratio. Secondly, a time series modeling approach, as an autoregressive with exogenous input model, is employed for pattern recognition by minimizing the Akaike information criterion. An experimental platform is proposed to test the procedure, where pulse-echo experiments were employed before and after a fault occurred. The results from this paper lead to the identification of an early indication of structural problems induced by internal (material, shape, age, etc.) and external (temperature, humidity, pressure, etc.) factors. The model can anticipate catastrophic faults, reducing the preventive/corrective tasks and costs, etc, and increasing the availability of the wind turbine, and therefore the energy production.</p> </div> <p>&nbsp;</p>

Automatic Identification of Closed-Loop Wind Turbine Dynamics via Genetic Programming

2015 ◽  
Author(s):  
William La Cava ◽  
Kourosh Danai ◽  
Matthew Lackner ◽  
Lee Spector ◽  
Paul Fleming ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Dynamic Models ◽  
A Priori ◽  
Operating Conditions ◽  
Automatic Identification ◽  
Horizontal Axis Wind Turbine ◽  
Symbolic Form ◽  
Turbulent Environments ◽  
Wide Range

Wind turbines are nonlinear systems that operate in turbulent environments. As such, their behavior is difficult to characterize accurately across a wide range of operating conditions by physically meaningful models. Customarily, data-based models of wind turbines are defined in ‘black box’ format, lacking in both conciseness and physical intelligibility. To address this deficiency, we identify models of a modern horizontal-axis wind turbine in symbolic form using a recently developed symbolic regression method. The method used relies on evolutionary multi-objective optimization to produce succinct dynamic models from operational data without ‘a priori’ knowledge of the system. We compare the produced models with models derived by other methods for their estimation capacity and evaluate the tradeoff between model intelligibility and accuracy. Several succinct models are found that predict wind turbine behavior as well as or better than more complex alternatives derived by other methods.

scholarly journals General Methodology for the Identification of Reduced Dynamic Models of Barge-Type Floating Wind Turbines

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3902
Author(s):  
Daniel Villoslada ◽  
Matilde Santos ◽  
María Tomás-Rodríguez
Keyword(s):  
Wind Turbines ◽  
Structural Control ◽  
Dynamic Models ◽  
Operating Conditions ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Data Set ◽  
General Methodology ◽  
Floating Offshore Wind Turbines ◽  
Reduced Dynamic

Floating offshore wind turbines (FOWT) are designed to overcome some of the limitations of offshore bottom-fixed ones. The development of computational models to simulate the behavior of the structure and the turbine is key to understanding the wind energy system and demonstrating its feasibility. In this work, a general methodology for the identification of reduced dynamic models of barge-type FOWTs is presented. The method is described together with an example of the development of a dynamic model of a 5 MW floating offshore wind turbine. The novelty of the proposed identification methodology lies in the iterative loop relationship between the identification and validation processes. Diversified data sets are used to select the best-fitting identified parameters by cross evaluation of every set among all validating conditions. The data set is generated for different initial FOWT operating conditions. Indeed, an optimal initial condition for platform pitch was found to be far enough from the system at rest to allow the dynamics to be well characterized but not so far that the unmodeled system nonlinearities were so large that they affected significantly the accuracy of the model. The model has been successfully applied to structural control research to reduce fatigue on a barge-type FOWT.

Dynamic Models for Load Calculation Procedures of Offshore Wind Turbine Support Structures: Overview, Assessment, and Outlook

2015 ◽  
Vol 10 (4) ◽  
Author(s):  
Paul L. C. van der Valk ◽  
Sven N. Voormeeren ◽  
Pauline C. de Valk ◽  
Daniel J. Rixen
Keyword(s):  
Wind Turbine ◽  
Model Reduction ◽  
Wind Turbines ◽  
Dynamic Models ◽  
Wind Farms ◽  
Fatigue Loading ◽  
Offshore Wind ◽  
Accurate Estimation ◽  
Offshore Wind Turbine ◽  
Foundation Design

One of the main mechanisms for driving down the cost of offshore wind energy is to install ever larger wind turbines in larger wind farms. At the same time, these turbines are placed further offshore in deeper waters. As a result, traditional monopile foundations are not always feasible and multimembered foundations, such as jackets and tripods are required. Typically, thousands of load cases need to be simulated for the design and certification of offshore wind turbines (OWTs). As models of such foundations are significantly larger than their monopile counterparts, model reduction is often applied to limit the computational costs. Additionally, the foundation design is generally done by a specialized company, which bases its design on the results of the load simulations. Hence, an accurate estimation of the stresses in load simulation is essential to predict the integrity and the lifetime of different designs. The effect on the load accuracy of both the model reduction as well as the postprocessing method used by foundation designers (FDs) are investigated in this paper. A case study is performed on a jacket-based wind turbine model to verify and quantify the findings. First, it is observed that the effect of the reduced foundation model on the wind turbine loads is negligible. However, both the reduction method and the postprocessing method applied by the FD have a large influence on the fatigue loading in the jacket. It is shown that the popular Guyan reduction results in significant errors on the fatigue damage and that a static postprocessing analysis leads to serious underestimations of the fatigue loads. Finally, an outlook is given into future developments in the field of load calculations for OWT foundation design.

scholarly journals An Online Hybrid Model for Temperature Prediction of Wind Turbine Gearbox Components

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3920
Author(s):  
Qiang Zhao ◽  
Kunkun Bao ◽  
Jia Wang ◽  
Yinghua Han ◽  
Jinkuan Wang
Keyword(s):  
Prediction Model ◽  
Wind Turbine ◽  
Error Correction ◽  
Hybrid Model ◽  
Wind Turbines ◽  
Short Term Memory ◽  
Operating Conditions ◽  
Temperature Prediction ◽  
Wind Turbine Gearbox ◽  
Model Based

Condition monitoring can improve the reliability of wind turbines, which can effectively reduce operation and maintenance costs. The temperature prediction model of wind turbine gearbox components is of great significance for monitoring the operation status of the gearbox. However, the complex operating conditions of wind turbines pose grand challenges to predict the temperature of gearbox components. In this study, an online hybrid model based on a long short term memory (LSTM) neural network and adaptive error correction (LSTM-AEC) using simple-variable data is proposed. In the proposed model, a more suitable deep learning approach for time series, LSTM algorithm, is applied to realize the preliminary prediction of temperature, which has a stronger ability to capture the non-stationary and non-linear characteristics of gearbox components temperature series. In order to enhance the performance of the LSTM prediction model, the adaptive error correction model based on the variational mode decomposition (VMD) algorithm is developed, where the VMD algorithm can effectively solve the prediction difficulty issue caused by the non-stationary, high-frequency and chaotic characteristics of error series. To apply the hybrid model to the online prediction process, a real-time rolling data decomposition process based on VMD algorithm is proposed. With aims to validate the effectiveness of the hybrid model proposed in this paper, several traditional models are introduced for comparative analysis. The experimental results show that the hybrid model has better prediction performance than other comparative models.

scholarly journals Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 982 ◽  
Author(s):  
Xin Wu ◽  
Hong Wang ◽  
Guoqian Jiang ◽  
Ping Xie ◽  
Xiaoli Li
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Operating Conditions ◽  
Dynamic Threshold ◽  
Detection Accuracy ◽  
Echo State Network ◽  
Monitoring Method ◽  
Vibration Data ◽  
Static Monitoring ◽  
Threshold Monitoring

Health monitoring of wind turbine gearboxes has gained considerable attention as wind turbines become larger in size and move to more inaccessible locations. To improve the reliability, extend the lifetime of the turbines, and reduce the operation and maintenance cost caused by the gearbox faults, data-driven condition motoring techniques have been widely investigated, where various sensor monitoring data (such as power, temperature, and pressure, etc.) have been modeled and analyzed. However, wind turbines often work in complex and dynamic operating conditions, such as variable speeds and loads, thus the traditional static monitoring method relying on a certain fixed threshold will lead to unsatisfactory monitoring performance, typically high false alarms and missed detections. To address this issue, this paper proposes a reliable monitoring model for wind turbine gearboxes based on echo state network (ESN) modeling and the dynamic threshold scheme, with a focus on supervisory control and data acquisition (SCADA) vibration data. The aim of the proposed approach is to build the turbine normal behavior model only using normal SCADA vibration data, and then to analyze the unseen SCADA vibration data to detect potential faults based on the model residual evaluation and the dynamic threshold setting. To better capture temporal information inherent in monitored sensor data, the echo state network (ESN) is used to model the complex vibration data due to its simple and fast training ability and powerful learning capability. Additionally, a dynamic threshold monitoring scheme with a sliding window technique is designed to determine dynamic control limits to address the issue of the low detection accuracy and poor adaptability caused by the traditional static monitoring methods. The effectiveness of the proposed monitoring method is verified using the collected SCADA vibration data from a wind farm located at Inner Mongolia in China. The results demonstrated that the proposed method can achieve improved detection accuracy and reliability compared with the traditional static threshold monitoring method.

scholarly journals Improving the Strategy of Maintaining Offshore Wind Turbines through Petri Net Modelling

2021 ◽  
Vol 11 (2) ◽  
pp. 574
Author(s):  
Rundong Yan ◽  
Sarah Dunnett
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Petri Net ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Maintenance Costs ◽  
Offshore Wind Turbines ◽  
Major Repair ◽  
Wind Turbine System ◽  
Periodic Maintenance

In order to improve the operation and maintenance (O&M) of offshore wind turbines, a new Petri net (PN)-based offshore wind turbine maintenance model is developed in this paper to simulate the O&M activities in an offshore wind farm. With the aid of the PN model developed, three new potential wind turbine maintenance strategies are studied. They are (1) carrying out periodic maintenance of the wind turbine components at different frequencies according to their specific reliability features; (2) conducting a full inspection of the entire wind turbine system following a major repair; and (3) equipping the wind turbine with a condition monitoring system (CMS) that has powerful fault detection capability. From the research results, it is found that periodic maintenance is essential, but in order to ensure that the turbine is operated economically, this maintenance needs to be carried out at an optimal frequency. Conducting a full inspection of the entire wind turbine system following a major repair enables efficient utilisation of the maintenance resources. If periodic maintenance is performed infrequently, this measure leads to less unexpected shutdowns, lower downtime, and lower maintenance costs. It has been shown that to install the wind turbine with a CMS is helpful to relieve the burden of periodic maintenance. Moreover, the higher the quality of the CMS, the more the downtime and maintenance costs can be reduced. However, the cost of the CMS needs to be considered, as a high cost may make the operation of the offshore wind turbine uneconomical.

scholarly journals Impact of Typhoons on Floating Offshore Wind Turbines: A Case Study of Typhoon Mangkhut

2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Jiawen Li ◽  
Jingyu Bian ◽  
Yuxiang Ma ◽  
Yichen Jiang
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Safety Evaluation ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Motion Response ◽  
Offshore Wind Turbines ◽  
Floating Offshore Wind Turbines ◽  
Coupling Motion

A typhoon is a restrictive factor in the development of floating wind power in China. However, the influences of multistage typhoon wind and waves on offshore wind turbines have not yet been studied. Based on Typhoon Mangkhut, in this study, the characteristics of the motion response and structural loads of an offshore wind turbine are investigated during the travel process. For this purpose, a framework is established and verified for investigating the typhoon-induced effects of offshore wind turbines, including a multistage typhoon wave field and a coupled dynamic model of offshore wind turbines. On this basis, the motion response and structural loads of different stages are calculated and analyzed systematically. The results show that the maximum response does not exactly correspond to the maximum wave or wind stage. Considering only the maximum wave height or wind speed may underestimate the motion response during the traveling process of the typhoon, which has problems in guiding the anti-typhoon design of offshore wind turbines. In addition, the coupling motion between the floating foundation and turbine should be considered in the safety evaluation of the floating offshore wind turbine under typhoon conditions.

scholarly journals Physical Modelling of Offshore Wind Turbine Foundations for TRL (Technology Readiness Level) Studies

2021 ◽  
Vol 9 (6) ◽  
pp. 589
Author(s):  
Subhamoy Bhattacharya ◽  
Domenico Lombardi ◽  
Sadra Amani ◽  
Muhammad Aleem ◽  
Ganga Prakhya ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Prior Experience ◽  
Physical Modelling ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Foundation Design ◽  
Offshore Wind Turbines ◽  
Sea Bed ◽  
Geological Conditions

Offshore wind turbines are a complex, dynamically sensitive structure due to their irregular mass and stiffness distribution, and complexity of the loading conditions they need to withstand. There are other challenges in particular locations such as typhoons, hurricanes, earthquakes, sea-bed currents, and tsunami. Because offshore wind turbines have stringent Serviceability Limit State (SLS) requirements and need to be installed in variable and often complex ground conditions, their foundation design is challenging. Foundation design must be robust due to the enormous cost of retrofitting in a challenging environment should any problem occur during the design lifetime. Traditionally, engineers use conventional types of foundation systems, such as shallow gravity-based foundations (GBF), suction caissons, or slender piles or monopiles, based on prior experience with designing such foundations for the oil and gas industry. For offshore wind turbines, however, new types of foundations are being considered for which neither prior experience nor guidelines exist. One of the major challenges is to develop a method to de-risk the life cycle of offshore wind turbines in diverse metocean and geological conditions. The paper, therefore, has the following aims: (a) provide an overview of the complexities and the common SLS performance requirements for offshore wind turbine; (b) discuss the use of physical modelling for verification and validation of innovative design concepts, taking into account all possible angles to de-risk the project; and (c) provide examples of applications in scaled model tests.

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