Optimisation of Data Acquisition in Wind Turbines with Data-Driven Conversion Functions for Sensor Measurements

Abstract Operation and Maintenance (O&M) is an important cost driver of modern wind turbines. Condition monitoring (CM) allows the implementation of predictive O&M strategies helping to reduce costs. In this work a novel approach for wind turbine condition monitoring is proposed focusing on synergistic effects of coexisting sensing technologies. The main objective is to understand the predictability of signals using information from other measurements recorded at different locations of the turbine. The approach is based on a multi-step procedure to pre-process data, train a set of conversion functions and evaluate their performance. A subsequent sensitivity analysis measuring the impact of the input variables on the predicted response reveals hidden relationships between signals. The concept feasibility is tested in a case study using Supervisory Control And Data Acquisition (SCADA) data from an offshore turbine.

[1]  Eric R. Ziegel,et al.  Generalized Linear Models , 2002, Technometrics.

[2]  A Kusiak,et al.  A Data-Driven Approach for Monitoring Blade Pitch Faults in Wind Turbines , 2011, IEEE Transactions on Sustainable Energy.

[3]  Lina Bertling Tjernberg,et al.  An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings , 2015, IEEE Transactions on Smart Grid.

[4]  H. Braam,et al.  Operation and maintenance cost estimator (OMCE) to estimate the future O&M costs of offshore wind farms , 2009 .

[5]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[6]  L.W.M.M. Rademakers,et al.  Operation and maintenance cost estimator (OMCE) to estimate the future Oampersand M costs of offshore wind farms , 2009 .

[7]  Fouad Slaoui-Hasnaoui,et al.  Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges , 2014 .

[8]  A. Davies,et al.  Handbook of Condition Monitoring , 1998 .

[9]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[10]  Robert B. Randall,et al.  Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications , 2011 .

[11]  L. Bertling,et al.  Maintenance Management of Wind Power Systems Using Condition Monitoring Systems—Life Cycle Cost Analysis for Two Case Studies , 2007, IEEE Transactions on Energy Conversion.

[12]  Julio J. Melero,et al.  Wind Turbine Failures - Tackling current Problems in Failure Data Analysis , 2016 .

[13]  B.K.N. Rao,et al.  Handbook of Condition Monitoring , 1996 .

[14]  Lina Bertling,et al.  On the economic benefits of using Condition Monitoring Systems for maintenance management of wind power systems , 2010, 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems.

[15]  Yaoyu Li,et al.  A review of recent advances in wind turbine condition monitoring and fault diagnosis , 2009, 2009 IEEE Power Electronics and Machines in Wind Applications.

[16]  P. McCullagh,et al.  Generalized Linear Models , 1972, Predictive Analytics.

[17]  James Carroll,et al.  Failure rate, repair time and unscheduled O&M cost analysis of offshore wind turbines , 2016 .

[18]  Simon J. Watson,et al.  Using SCADA data for wind turbine condition monitoring – a review , 2017 .

[19]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .