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.

[1]  Wang Dong-feng,et al.  Research on Early Fault Diagnostic Method of Wind Turbines , 2013 .

[2]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[3]  Paul Fleming,et al.  Use of SCADA Data for Failure Detection in Wind Turbines , 2011 .

[4]  Cecelia Marie Venuk.  Failure modes and effects analysis (FMEA). , 2012 .

[5]  Wenxian Yang,et al.  Cost-Effective Condition Monitoring for Wind Turbines , 2010, IEEE Transactions on Industrial Electronics.

[6]  Peter Tavner,et al.  Reliability analysis for wind turbines , 2007 .

[7]  Jia Xu,et al.  A New Condition Monitoring Method for Wind Turbines Based on Power Curve Model , 2016, AST 2016.

[8]  Wen Zhong Shen,et al.  Prediction of the wind turbine performance by using BEM with airfoil data extracted from CFD , 2014 .

[9]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches , 2015, IEEE Transactions on Industrial Electronics.

[10]  Deshun Liu,et al.  Research on power coefficient of wind turbines based on SCADA data , 2016 .

[11]  Zhiwei Gao,et al.  Takagi-Sugeno fuzzy modelling and robust fault reconstruction for wind turbine systems , 2016, 2016 IEEE 14th International Conference on Industrial Informatics (INDIN).

[12]  Michele Messina,et al.  Fluid dynamics wind turbine design: Critical analysis, optimization and application of BEM theory , 2007 .

[13]  L. Bertling,et al.  Reliability-Centered Maintenance for Wind Turbines Based on Statistical Analysis and Practical Experience , 2012, IEEE Transactions on Energy Conversion.

[14]  Sofiane Achiche,et al.  Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description , 2013, Appl. Soft Comput..

[15]  Anca Daniela Hansen,et al.  Modelling and control of variable-speed multi-pole permanent magnet synchronous generator wind turbine , 2008 .

[16]  Andrew Kusiak,et al.  The prediction and diagnosis of wind turbine faults , 2011 .

[17]  Yanyang Zi,et al.  Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals , 2016 .

[18]  F. Valenciaga,et al.  Multiple-input–multiple-output high-order sliding mode control for a permanent magnet synchronous generator wind-based system with grid support capabilities , 2015 .

[19]  Yingning Qiu,et al.  Monitoring wind turbine gearboxes , 2013 .

[20]  King Ngi Ngan,et al.  Unsupervised Salient Object Segmentation Based on Kernel Density Estimation and Two-Phase Graph Cut , 2012, IEEE Transactions on Multimedia.

[21]  Lina Bertling,et al.  An Approach for Condition-Based Maintenance Optimization Applied to Wind Turbine Blades , 2010, IEEE Transactions on Sustainable Energy.

[22]  Mohamed Benbouzid,et al.  A Brief Status on Condition Monitoring and Fault Diagnosis in Wind Energy Conversion Systems , 2009 .

[23]  Jianhui Wang,et al.  Time Adaptive Conditional Kernel Density Estimation for Wind Power Forecasting , 2012, IEEE Transactions on Sustainable Energy.

[24]  AchicheSofiane,et al.  Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1 , 2013 .

[25]  Yingning Qiu,et al.  Use of SCADA and CMS signals for failure detection and diagnosis of a wind turbine gearbox , 2011 .

[26]  David Infield,et al.  Online wind turbine fault detection through automated SCADA data analysis , 2009 .

[27]  Wenxian Yang,et al.  Condition Monitoring of the Power Output of Wind Turbine Generators Using Wavelets , 2010, IEEE Transactions on Energy Conversion.

[28]  Iain Staffell,et al.  How does wind farm performance decline with age , 2014 .

[29]  Ciro A. Rodríguez,et al.  An Improved BEM Model for the Power Curve Prediction of Stall-regulated Wind Turbines , 2005 .

[30]  Wenxian Yang,et al.  Wind turbine condition monitoring by the approach of SCADA data analysis , 2013 .

[31]  Ronald Ortner,et al.  Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning , 2015, ICML.

[32]  Yingning Qiu,et al.  Wind turbine condition monitoring: technical and commercial challenges , 2014 .

[33]  Peter Tavner,et al.  Failure Modes and Effects Analysis (FMEA) for wind turbines. , 2010 .

[34]  Xin Long,et al.  Aerodynamic loads calculation and analysis for large scale wind turbine based on combining BEM modified theory with dynamic stall model , 2011 .

[35]  J-C Dai,et al.  Modelling and analysis of direct-driven permanent magnet synchronous generator wind turbine based on wind-rotor neural network model , 2012 .

[36]  Meik Schlechtingen,et al.  Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 2: Application examples , 2014, Appl. Soft Comput..