A performance and maintenance evaluation framework for wind turbines

In this paper, a data driven framework for performance and maintenance evaluation (PAME) of wind turbines (WT) is proposed. To develop the framework, SCADA data of WTs are adopted and several parameters are carefully selected to create a normal behavior model. This model which is based on Neural Networks estimates operation of WT and aberrations are collected as deviations. Afterwards, in order to capture patterns of deviations, self-organizing map is applied to cluster the deviations. From investigations on deviations and clustering results, a time-discrete finite state space Markov chain is built for mid-term operation and maintenance evaluation. With the purpose of performance and maintenance assessment, two anomaly indexes are defined and mathematically formulated. Moreover, Production Loss Profit is defined for Preventive Maintenance efficiency assessment. By comparing the indexes calculated for 9 WTs, current performance and maintenance strategies can be evaluated, and results demonstrate capability and effectiveness of the proposed framework.

[1]  Yingning Qiu,et al.  Wind turbine SCADA alarm analysis for improving reliability , 2012 .

[2]  Lina Bertling Tjernberg,et al.  Self evolving neural network based algorithm for fault prognosis in wind turbines: A case study , 2014, 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[3]  L. Bertling,et al.  A reliability-centered asset maintenance method for assessing the impact of maintenance in power distribution systems , 2005, IEEE Transactions on Power Systems.

[4]  Mihai Florian,et al.  Wind turbine blade life-time assessment model for preventive planning of operation and maintenance , 2014 .

[5]  José de Jesús Rubio,et al.  Analytic neural network model of a wind turbine , 2015, Soft Comput..

[6]  Katharina Fischer,et al.  Reliability-Centred Asset Maintenance — A step towards enhanced reliability, availability, and profitability of wind power plants , 2010, 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe).

[7]  R. Chavez-Arroyo,et al.  Statistical–dynamical downscaling of wind fields using self-organizing maps , 2015 .

[8]  Christopher Durugbo,et al.  Performance assessment of wind turbine gearboxes using in-service data: Current approaches and future trends , 2015 .

[9]  J V Tu,et al.  Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. , 1996, Journal of clinical epidemiology.

[10]  Lina Bertling,et al.  An optimization framework for opportunistic maintenance of offshore wind power system , 2009, 2009 IEEE Bucharest PowerTech.

[11]  Roy Billinton,et al.  A basic framework for generating system operating health analysis , 1994 .

[12]  John Alexander Steel,et al.  A progressive study into offshore wind farm maintenance optimisation using risk based failure analysis , 2015 .

[13]  Davide Astolfi,et al.  Analyzing wind turbine directional behavior: SCADA data mining techniques for efficiency and power assessment , 2017 .

[14]  Mengyan Nie,et al.  Review of condition monitoring and fault diagnosis technologies for wind turbine gearbox , 2013 .

[15]  Miguel A. Sanz-Bobi,et al.  Failure Risk Indicators for a Maintenance Model Based on Observable Life of Industrial Components With an Application to Wind Turbines , 2013, IEEE Transactions on Reliability.

[16]  Christopher Durugbo,et al.  Effect of preventive maintenance intervals on reliability and maintenance costs of wind turbine gearboxes , 2015 .

[17]  Lina Bertling Tjernberg,et al.  Cost efficient maintenance strategies for wind power systems using LCC , 2014, 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[18]  Jay Lee,et al.  Wind turbine performance assessment using multi-regime modeling approach , 2012 .

[19]  Antoine Tahan,et al.  Wind turbine power curve modelling using artificial neural network , 2016 .

[20]  Xiao Lei,et al.  A generalized model for wind turbine anomaly identification based on SCADA data , 2016 .

[21]  Graeme Wilson.  Quantifying the relationship between wind turbine component failure rates and wind speed , 2015 .

[22]  Miguel A. Sanz-Bobi,et al.  Wind turbine condition assessment based on changes observed in its power curve , 2013, 2013 International Conference on Renewable Energy Research and Applications (ICRERA).

[23]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[24]  Yanjun Yan.  Nacelle orientation based health indicator for wind turbines , 2015, 2015 IEEE Conference on Prognostics and Health Management (PHM).

[25]  John Alexander Steel,et al.  Failure Prognostic Schemes and Database Design of a Software Tool for Efficient Management of Wind Turbine Maintenance , 2015 .

[26]  J. G. Slootweg,et al.  Impacts of Wind Power on Power System Stability , 2012 .

[27]  Tongdan Jin,et al.  Condition based maintenance optimization for wind power generation systems under continuous monitoring , 2011 .

[28]  Marc G. Genton,et al.  A kernel plus method for quantifying wind turbine performance upgrades , 2015 .

[29]  Fausto Pedro García Márquez,et al.  Optimal Maintenance Management of Offshore Wind Farms , 2016 .

[30]  María Isabel Blanco.  The economics of wind energy , 2009 .