Machine Learning Algorithms for Wind Turbine Performance Enhancement
In this study presented at WindEurope Summit, Sebastian Kaus discusses the use of machine learning algorithms to enhance wind turbine performance based on SCADA data. The presentation covers how Senvion utilizes machine learning techniques for monitoring and improving wind turbine yield. Topics include the machine learning approach for performance monitoring, detection of yaw misalignment, and the benefits of self-learning algorithms in wind farm data analysis. The presentation emphasizes the potential of machine learning in optimizing wind turbine performance and highlights the importance of leveraging data for enhanced efficiency and maintenance strategies.
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Presentation Transcript
Machine Learning Algorithms for Wind Turbine Performance Enhancement Optimization of wind turbine performance based on SCADA data Sebastian Kaus, Specialist Wind Farm Performance Monitoring WindEurope Summit Hamburg September 29th2016
Target of presentation How Senvion uses machine learning algorithms for turbine performance monitoring How Senvion machine learning techniques improve the yield of a wind turbine Machine Learning Algorithms for Wind Turbine Performance Enhancement Sebastian Kaus Senvion 29/09/2016 2
Agenda Introduction Machine learning approach for performance monitoring Case study Detection of yaw misaligment Summary Machine Learning Algorithms for Wind Turbine Performance Enhancement Sebastian Kaus Senvion 29/09/2016 3
Agenda Introduction Machine learning approach for performance monitoring Case study Detection of yaw misaligment Summary Machine Learning Algorithms for Wind Turbine Performance Enhancement Sebastian Kaus Senvion 29/09/2016 4
OEM has ideal preconditions for Big Data & Machine Learning Feedback from Service Technical Knowledge Large Fleet Turbine Parameter Set Turbine Master Data SCADA Data Machine Learning Algorithms for Wind Turbine Performance Enhancement Sebastian Kaus Senvion 29/09/2016 5
Agenda Introduction Machine learning approach for performance monitoring Case study Detection of yaw misaligment Summary Machine Learning Algorithms for Wind Turbine Performance Enhancement Sebastian Kaus Senvion 29/09/2016 6
Self learning algorithm improves over time AdvancedMonitoringService Wind farm data Corrective action Calculation of Key Performance Indicators Data Base Evaluation with machine learning algorithm Sensor & Plausibility Analysis KPI for specific turbine anomalies e.g. yaw or pitch misalignment Machine Learning Algorithms for Wind Turbine Performance Enhancement Sebastian Kaus Senvion 29/09/2016 7
Key Performance Indicators sharpen the picture Multiple days Error rate in classification Vane position Power Reference 9331 Time (s) ~-20% Wind Speed 1,68 SCADA SCADA KPI KPI Reduction of noise in data set KPI work like a low pass filter Less computing time to train model Less data points, but more meaningful data KPI s Machine Learning Algorithms for Wind Turbine Performance Enhancement Sebastian Kaus Senvion 29/09/2016 8
Agenda Introduction Machine learning approach for performance monitoring Case study Detection of yaw misaligment Summary Machine Learning Algorithms for Wind Turbine Performance Enhancement Sebastian Kaus Senvion 29/09/2016 9
Case Study 5 Yaw misalignment successfully detected Analysis result Training Set 8 wind farms with aligned and misaligned turbines 1100 KPI used to train model Case conditions Wind farm with 12 turbines Turbine #8 purposly misaligned by 5 2 Week misalignment Input data misaligned turbine 10 Minute Standard SCADA Data Converted into 11 KPI Neural Network Decision Tree +1% AEP (??? -Criteria) Na ve Bayes Random Forrest Analysis Ensemble of selected machine learning algorithms Support Vector Machine Machine Learning Algorithms for Wind Turbine Performance Enhancement Sebastian Kaus Senvion 29/09/2016 10
Agenda Introduction Machine learning approach for performance monitoring Case study Detection of yaw misaligment Summary Machine Learning Algorithms for Wind Turbine Performance Enhancement Sebastian Kaus Senvion 29/09/2016 11
Summary & Conclusion Summary OEM has ideal preconditions for Big Data & Machine Learning Machine learning techniques enable automated complex analysis without installing additional hardware or software Conclusion Use of machine learning algorithms on combination of all available turbine data across the fleet Senvion Advanced Monitoring Service increases the yield of turbines with specific fault analysis and fast resolving due to integrated service Thank you very much! Machine Learning Algorithms for Wind Turbine Performance Enhancement Sebastian Kaus Senvion 29/09/2016 12
