scholarly journals Comparison of Electrical and Vibration Analysis Methods for Mechanical Fault Monitoring in Wind Turbine Drivetrains

2019 ◽  
Vol 9 (1) ◽  
pp. 2-7
Author(s):  
P Granjon ◽  
P D Longhitano ◽  
A Singh
Keyword(s):  
Wind Turbine ◽  
Vibration Analysis ◽  
Wind Farm ◽  
Electromechanical System ◽  
Main Bearing ◽  
Monitoring Method ◽  
Fault Monitoring ◽  
Three Phase ◽  
Mechanical Faults ◽  
Electrical Generator

Mechanical faults occurring in drivetrains are traditionally monitored through vibration analysis and, more rarely, by analysing electrical quantities measured on the electromechanical system involved. However, a monitoring method that is able to take into account all of the information contained in three-phase electrical quantities was recently proposed. The goal of this paper is to compare this threephase electrical approach with the usual vibration-based method in terms of its capability to detect mechanical faults in drivetrains. In this context, a 2 MW geared wind turbine operating in an industrial wind farm was equipped with accelerometers near the main bearing and electrical sensors on the stator of the electrical generator for several months. During this period, an important mechanical fault occurred in the main bearing of the system. The evolution of the fault indicators computed by the two previous approaches were compared throughout this period of time. All of the indicators behaved similarly and showed the development of an inner bearing fault in the main bearing. This demonstrated that a mechanical fault occurring in a drivetrain can be monitored and detected by analysing electrical quantities, even if the fault is located some distance from the electrical generator.

scholarly journals Three-Stage Wind Turbine Assessment Method: Condition Monitoring, Failure Prediction, And Health Assessment

2021 ◽  
Author(s):  
Yongsheng Qi ◽  
Tongmei Jing ◽  
Chao Ren ◽  
Xuejin Gao
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Farm ◽  
Health Management ◽  
Health Assessment ◽  
Failure Prediction ◽  
Fuzzy Comprehensive Evaluation ◽  
Assessment Method ◽  
Generalized Regression Neural Network ◽  
Monitoring Method

Abstract To improve the wind turbine shutdown early warning ability, we present a generalized model for wind turbine (WT) prognosis and health management (PHM) based on the data collected from the SCADA system. First, a new condition monitoring method based on kernel entropy component analysis (KECA) was developed for nonlinear data. Then, an aggregate statistic T was designed to express the state change of the monitoring parameters. As the features were submerged because of the diversity and nonlinearity of SCADA data, an enhanced generalized regression neural network (GRNN) method—KECA-GRNN—for failure prediction was developed by adding KECA for feature extraction to improve the predictive performance. Finally, the results of the KECA-GRNN model were visualized by a bubble chart, which made the health assessment results of the WT more intuitive. Similarly, the fusion residual was defined to analyze the health trend of the WT, and the health status of the WT was represented by two visualization methods—bubble chart and fuzzy comprehensive evaluation. Furthermore, they were evaluated using SCADA data that were collected from a wind farm. Observations from the results of the model indicated the ability of the approach to trend and assess turbine degradation before known downtime occurrences.

scholarly journals Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2228 ◽  
Author(s):  
Ángel Encalada-Dávila ◽  
Bryan Puruncajas ◽  
Christian Tutivén ◽  
Yolanda Vidal
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Critical Issue ◽  
Main Bearing ◽  
Bearing Fault ◽  
Bearing Failures ◽  
Relevant Topic ◽  
Fault Prognosis ◽  
And Performance

As stated by the European Academy of Wind Energy (EAWE), the wind industry has identified main bearing failures as a critical issue in terms of increasing wind turbine reliability and availability. This is owing to major repairs with high replacement costs and long downtime periods associated with main bearing failures. Thus, the main bearing fault prognosis has become an economically relevant topic and is a technical challenge. In this work, a data-based methodology for fault prognosis is presented. The main contributions of this work are as follows: (i) Prognosis is achieved by using only supervisory control and data acquisition (SCADA) data, which is already available in all industrial-sized wind turbines; thus, no extra sensors that are designed for a specific purpose need to be installed. (ii) The proposed method only requires healthy data to be collected; thus, it can be applied to any wind farm even when no faulty data has been recorded. (iii) The proposed algorithm works under different and varying operating and environmental conditions. (iv) The validity and performance of the established methodology is demonstrated on a real underproduction wind farm consisting of 12 wind turbines. The obtained results show that advanced prognostic systems based solely on SCADA data can predict failures several months prior to their occurrence and allow wind turbine operators to plan their operations.

Overview of Subsynchronous Oscillation in Grid-connected Wind Farm

2020 ◽  
Vol 13 (7) ◽  
pp. 969-979
Author(s):  
Xu Pei-Zhen ◽  
Lu Yong-Geng ◽  
Cao Xi-Min
Keyword(s):  
Power Systems ◽  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Induction Generator ◽  
Future Research ◽  
Stable Operation ◽  
Subsynchronous Oscillation ◽  
Different Types

Background: Over the past few years, the subsynchronous oscillation (SSO) caused by the grid-connected wind farm had a bad influence on the stable operation of the system and has now become a bottleneck factor restricting the efficient utilization of wind power. How to mitigate and suppress the phenomenon of SSO of wind farms has become the focus of power system research. Methods: This paper first analyzes the SSO of different types of wind turbines, including squirrelcage induction generator based wind turbine (SCIG-WT), permanent magnet synchronous generator- based wind turbine (PMSG-WT), and doubly-fed induction generator based wind turbine (DFIG-WT). Then, the mechanisms of different types of SSO are proposed with the aim to better understand SSO in large-scale wind integrated power systems, and the main analytical methods suitable for studying the SSO of wind farms are summarized. Results: On the basis of results, using additional damping control suppression methods to solve SSO caused by the flexible power transmission devices and the wind turbine converter is recommended. Conclusion: The current development direction of the SSO of large-scale wind farm grid-connected systems is summarized and the current challenges and recommendations for future research and development are discussed.

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.

Experimental Study for SPAR Type Floating Offshore Wind Turbine With Blade-Pitch Control

2013 ◽  
Author(s):  
Toshiki Chujo ◽  
Yoshimasa Minami ◽  
Tadashi Nimura ◽  
Shigesuke Ishida
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Offshore Wind ◽  
Scale Model ◽  
Offshore Wind Turbine ◽  
Experimental Proof ◽  
Pitch Control ◽  
Model Experiments ◽  
North Eastern ◽  
North Eastern Part

The experimental proof of the floating wind turbine has been started off Goto Islands in Japan. Furthermore, the project of floating wind farm is afoot off Fukushima Prof. in north eastern part of Japan. It is essential for realization of the floating wind farm to comprehend its safety, electric generating property and motion in waves and wind. The scale model experiments are effective to catch the characteristic of floating wind turbines. Authors have mainly carried out scale model experiments with wind turbine models on SPAR buoy type floaters. The wind turbine models have blade-pitch control mechanism and authors focused attention on the effect of blade-pitch control on both the motion of floater and fluctuation of rotor speed. In this paper, the results of scale model experiments are discussed from the aspect of motion of floater and the effect of blade-pitch control.

scholarly journals Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2319
Author(s):  
Hyun-Goo Kim ◽  
Jin-Young Kim
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Free Stream ◽  
Turbine Blades ◽  
Wind Farms ◽  
Lidar Measurement ◽  
Wind Turbine Blades ◽  
Wake Effect

This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year.

scholarly journals Dynamic Modeling of an Offshore Floating Wind Turbine for Application in the Mediterranean Sea

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 248
Author(s):  
Lorenzo Cottura ◽  
Riccardo Caradonna ◽  
Alberto Ghigo ◽  
Riccardo Novo ◽  
Giovanni Bracco ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Mediterranean Sea ◽  
Wind Farm ◽  
Reference Model ◽  
Wide Spectrum ◽  
Wind Farms ◽  
Offshore Wind ◽  
Floating Wind Turbine ◽  
The Mediterranean ◽  
Offshore Floating Wind Turbine

Wind power is emerging as one of the most sustainable and low-cost options for energy production. Far-offshore floating wind turbines are attractive in view of exploiting high wind availability sites while minimizing environmental and landscape impact. In the last few years, some offshore floating wind farms were deployed in Northern Europe for technology validation, with very promising results. At present time, however, no offshore wind farm installations have been developed in the Mediterranean Sea. The aim of this work is to comprehensively model an offshore floating wind turbine and examine the behavior resulting from a wide spectrum of sea and wind states typical of the Mediterranean Sea. The flexible and accessible in-house model developed for this purpose is compared with the reference model FAST v8.16 for verifying its reliability. Then, a simulation campaign is carried out to estimate the wind turbine LCOE (Levelized Cost of Energy). Based on this, the best substructure is chosen and the convenience of the investment is evaluated.

Real-Time Optimization of Wind Farm Energy Capture With Delay Compensated Nested-Loop Extremum Seeking Control

2017 ◽  
Author(s):  
Zhongyou Wu ◽  
Yaoyu Li
Keyword(s):  
Wind Turbine ◽  
Real Time ◽  
Wind Farm ◽  
Optimization Techniques ◽  
Extremum Seeking ◽  
Extremum Seeking Control ◽  
Energy Capture ◽  
Time Optimization ◽  
Real Time Optimization ◽  
Nested Loop

Real-time optimization of wind farm energy capture for below rated wind speed is critical for reducing the levelized cost of energy (LCOE). Performance of model based control and optimization techniques can be significantly limited by the difficulty in obtaining accurate turbine and farm models in field operation, as well as the prohibitive cost for accurate wind measurements. The Nested-Loop Extremum Seeking Control (NLESC), recently proposed as a model free method has demonstrated its great potential in wind farm energy capture optimization. However, a major limitation of previous work is the slow convergence, for which a primary cause is the low dither frequencies used by upwind turbines, primarily due to wake propagation delay through the turbine array. In this study, NLESC is enhanced with the predictor based delay compensation proposed by Oliveira and Krstic [1], which allows the use of higher dither frequencies for upwind turbines. The convergence speed can thus be improved, increasing the energy capture consequently. Simulation study is performed for a cascaded three-turbine array using the SimWindFarm platform. Simulation results show the improved energy capture of the wind turbine array under smooth and turbulent wind conditions, even up to 10% turbulence intensity. The impact of the proposed optimization methods on the fatigue loads of wind turbine structures is also evaluated.

Study on the Dynamic Response for Floating Foundation of Offshore Wind Turbine

2011 ◽  
Author(s):  
Yougang Tang ◽  
Jun Hu ◽  
Liqin Liu
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Dynamic Responses ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Wave Load ◽  
Mooring Lines ◽  
Floating Foundation ◽  
Wind Turbine System ◽  
Sea Area

The wind resources for ocean power generation are mostly distributed in sea areas with the distance of 5–50km from coastline, whose water depth are generally over 20m. To improve ocean power output and economic benefit of offshore wind farm, it is necessary to choose floating foundation for offshore wind turbine. According to the basic data of a 600kW wind turbine with a horizontal shaft, the tower, semi-submersible foundation and mooring system are designed in the 60-meter-deep sea area. Precise finite element models of the floating wind turbine system are established, including mooring lines, floating foundation, tower and wind turbine. Dynamic responses for the floating foundation of offshore wind turbine are investigated under wave load in frequency domain.

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