Big Data Analytics for SCADA - EWEA€¦ · Big Data Analytics for SCADA 1 Machine Learning Models...
23
DNV GL © 2016 Ungraded 14 April 2016 SAFER, SMARTER, GREENER DNV GL © 2016 Ungraded 14 April 2016 Elizabeth Traiger, Ph.D., M.Sc. ENERGY Big Data Analytics for SCADA 1 Machine Learning Models for Fault Detection and Turbine Performance
Transcript of Big Data Analytics for SCADA - EWEA€¦ · Big Data Analytics for SCADA 1 Machine Learning Models...
DNV GL © 2016
Ungraded
14 April 2016 SAFER, SMARTER, GREENER DNV GL © 2016
Ungraded
14 April 2016
Elizabeth Traiger, Ph.D., M.Sc.
ENERGY
Big Data Analytics for SCADA
1
Machine Learning Models for Fault Detection
and Turbine Performance
DNV GL © 2016
Ungraded
14 April 2016
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
2
DNV GL © 2016
Ungraded
14 April 2016
Big Data in Wind Industry
3
Big Data Volume
Velocity
Varied
Beyond Capabilities of
Traditional Data Processing
DNV GL © 2016
Ungraded
14 April 2016
Big Data in Wind Industry
4
SCADA
Atmospheric Performance
Vibration/ Acceleration
Temperature
Grid
Market
DNV GL © 2016
Ungraded
14 April 2016
Big Data in Wind Industry
5
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
DNV GL © 2016
Ungraded
14 April 2016
Analysis on Large Volume Data Practicalities
6
DNV GL © 2016
Ungraded
14 April 2016
Analysis on Large Volume Data Practicalities
7
DNV GL © 2016
Ungraded
14 April 2016
Analysis on Large Volume Data Practicalities
8
DNV GL © 2016
Ungraded
14 April 2016
Analysis on Large Volume Data Practicalities
9
Unstructured
Wind Speed
Temperature
Yaw Angle
Power
Voltage
…
Wind Speed Yaw Angle
Market Price Temperature
Inspection
Condition
Structured
DNV GL © 2016
Ungraded
14 April 2016
Into to the Black Box – Machine Learning Basics
10
Machine Learning
Pattern Recognition
Separation
Predictive
Generalization
DNV GL © 2016
Ungraded
14 April 2016
Into to the Black Box – Machine Learning Basics
11
Supervised
Classification Regression
Unsupervised
Clustering Dimension Reduction
Training Set Validation Set
DNV GL © 2016
Ungraded
14 April 2016
Into the Black Box – Machine Learning Basics
12
SOURCE: https://s3.amazonaws.com/MLMastery/MachineLearningAlgorithms.png?__s=iph8dvzbonmmouyrjzfq
DNV GL © 2016
Ungraded
14 April 2016
Into to the Black Box – Machine Learning Basics - Supervised
13
Learners
Representation
Evaluation
Optimization
DNV GL © 2016
Ungraded
14 April 2016
Into to the Black Box – Machine Learning Basics - Supervised
14
Source: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
DNV GL © 2016
Ungraded
14 April 2016
Supervised learning example – Gearbox Fault Classification
15
Early Fault
Identified
Total Failure
Time
Conditio
n
DNV GL © 2016
Ungraded
14 April 2016
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
DNV GL © 2016
Ungraded
14 April 2016
Into to the Black Box – Machine Learning Basics - Unsupervised
17
Source: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms
DNV GL © 2016
Ungraded
14 April 2016
Unsupervised learning example – Turbine Performance
18
AD
TI
Wind Speed
TOD TE
WD
Shear Veer
Power
DNV GL © 2016
Ungraded
14 April 2016
Unsupervised learning example – Turbine Performance
19
Random Forest
Dissimilarity
DNV GL © 2016
Ungraded
14 April 2016
Unsupervised learning example – Turbine Performance
WS (AD Corrected)
AD WD
TI TOD TE
20
DNV GL © 2016
Ungraded
14 April 2016
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
21
DNV GL © 2016
Ungraded
14 April 2016
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.
22
DNV GL © 2016
Ungraded
14 April 2016
SAFER, SMARTER, GREENER
www.dnvgl.com
Happy Learning
23
Elizabeth Traiger, Ph.D, M.Sc
