14 April 2016
Elizabeth Traiger, Ph.D., M.Sc.
ENERGY
Big Data Analytics for SCADA
Machine Learning Models for Fault Detection
and Turbine Performance
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Points to Convey
Big Data in Wind Industry
Analysis on Large Volume Data Practicalities
Into to the Black Box – Machine Learning Basics
Supervised Learning – Gearbox Fault Detection
Unsupervised Learning – Random Forest Turbine Performance Classification
General Machine Learning Truths
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Big Data in Wind Industry
Big Data Volume
Velocity
Varied
Beyond Capabilities of
Traditional Data Processing
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Big Data in Wind Industry
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SCADA
Atmospheric Performance
Vibration/
Acceleration
Temperature
Grid
Market
Big Data in Wind Industry
Traditional Data Analysis Methodology
Model Driven Rule Based
Explanatory
Time Averaged Processor
Bound
Big Data / Predictive Analytics
Data Driven Pattern
Based Predictive
Real Time
Distributed
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Analysis on Large Volume Data Practicalities
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Analysis on Large Volume Data Practicalities
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Analysis on Large Volume Data Practicalities
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Analysis on Large Volume Data Practicalities
Unstructured
Wind Speed Temperature
Yaw Angle Power Voltage
…
Wind Speed
Yaw Angle
Market Price Temperature
Inspection Condition
Structured
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Into to the Black Box – Machine Learning Basics
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Machine Learning
Pattern
Recognition
Separation
Predictive
Generalization
Into to the Black Box – Machine Learning Basics
Supervised
Classification Regression
Unsupervised
Clustering Dimension Reduction
Training Set Validation Set
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Into the Black Box – Machine Learning Basics
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SOURCE: https://s3.amazonaws.com/MLMastery/MachineLearningAlgorithms.png?__s=iph8dvzbonmmouyrjzfq
Into to the Black Box – Machine Learning Basics - Supervised
Learners
Representation
Evaluation
Optimization
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Into to the Black Box – Machine Learning Basics - Supervised
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Source: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
Supervised learning example – Gearbox Fault Classification
Early Fault Identified
Total Failure
Time
Condition
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Supervised learning example – Gearbox Fault Classification
Output Input
Generator bearing temp. at T-2
Fault Classification Generator bearing
temp. at T-1
Support Vector Machine
Power output at T Generator speed at T
Wind Speed3
….
Source: By Cyc - Own work, Public Domain, https://commons.wikimedia.org/w/index.php?curid=3566688
Into to the Black Box – Machine Learning Basics - Unsupervised
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Unsupervised learning example – Turbine Performance
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AD TI
Wind Speed
TOD TE
WD Shear
Veer
Power
Unsupervised learning example – Turbine Performance
Random Forest
Dissimilarity
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Unsupervised learning example – Turbine Performance
WS (AD
Corrected) AD WD
TI TOD TE
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General Machine Learning Truths
Data is not enough
High dimension is no longer intuitive
Feature engineering is paramount
More data is better than a smart algorithm
No one model is a best fit
Embrace constant change
Uncertainty about Uncertainty
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Theory References
1. Pedro Domingos. 2012. ‘A few useful things to know about machine learning.’ Commun. ACM 55, 10 (October 2012), 78-87. DOI =
http://dx.doi.org/10.1145/2347736.2347755
2. Hastie, T., Tibshirani, R., and Friedman, J. H., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, New York: Springer, 2011.
3. Brian D. Ripley and N. L. Hjort. Pattern Recognition and Neural Networks.
Cambridge University Press, New York, NY, USA., 1st edition, 1995
4. I. Witten, E. Frank and M. Hall. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Mateo, CA 3rd edition, 2011.
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SAFER, SMARTER, GREENER
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Happy Learning
Elizabeth Traiger, Ph.D, M.Sc [email protected]
