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Data Mining Techniques to classify inter-area oscillations Adamantios Marinakis ABB Corporate Research CH London, 29/11/2013.

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Presentation on theme: "Data Mining Techniques to classify inter-area oscillations Adamantios Marinakis ABB Corporate Research CH London, 29/11/2013."— Presentation transcript:

1 Data Mining Techniques to classify inter-area oscillations Adamantios Marinakis ABB Corporate Research CH London, 29/11/2013

2 Presentation outline 2 Problem statement Data mining Support Vector Machines Evolution Strategies Random Forests Solution – Results Conclusion

3 Presentation outline 3 Problem statement Data mining Support Vector Machines Evolution Strategies Random Forests Solution – Results Conclusion

4 © ABB Group June 3, 2014 | Slide 4 Time stamps GPS Satellite Voltage and current phasors V, I t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t Communication network Wide-Area Monitoring System (WAMS) System Protection Center Visualization of power system dynamics Stability monitoring Stability control and blackout prevention

5 Power Damping Monitoring – PDM Principle 5

6 Swissgrid WAMS Collects measurements from PMUs around Europe 6

7 And then? Do something more than observing… 7 model operating point security status

8 What is an operating point At least, how we define it here 8

9 Overview of the approach Linking WAMS with SCADA data… 9 WAMS PMU measurements time-stamped oscillations damping ratios SCADA system (time-stamped data) generation, load dispatch line power flows FACTS devices status (PSS status) … Train classifier Database input variables output labels Need to time- synchronize them

10 Presentation outline 10 Problem statement Data mining Support Vector Machines Evolution Strategies Random Forests Solution – Results Conclusion

11 What is data mining? Apart from a fancy term 11 It is about analyzing the data

12 Presentation outline 12 Problem statement Data mining Support Vector Machines Evolution Strategies Random Forests Solution – Results Conclusion

13 Support Vector Machines A powerful classification technique 13 a QP

14 Non-separable classes 14 regularization parameter

15 And what about nonlinear patterns in the data? 15 higher dimension Map into a higher dimension feature space

16 The kernel trick 16

17 Most used kernels 17

18 Ouf, now it seems that quite some tuning is required … 18 Proper tuning is essential for good SVM performance

19 Automatic tuning of the SVM hyperparameters A nonlinear, non analytical optimization problem 19

20 Presentation outline 20 Problem statement Data mining Support Vector Machines Evolution Strategies Random Forests Solution – Results Conclusion

21 The basic cycle of the ES algorithm 21 Explore Exploit

22 Mutation: create an offspring out of one parent 22

23 Create offsprings out of one parent 23

24 Self-adaptation of mutation strength 24

25 Population >1 25

26 Another variation operator: Recombination 26 Parents are selected by uniform random distribution (their fitness is NEVER taken into account)

27 Guidelines for successful self-adaptation 27

28 ES-tuned SVM classifier Coming up with the oscillation damping classifier 28

29 Presentation outline 29 Problem statement Data mining Support Vector Machines Evolution Strategies Random Forests Solution – Results Conclusion

30 Random Forests A promising alternative 30 A collection of decision trees Basic Idea of DT: Greedy algorithm to progressively select the cut-attributes Splitting decided according to some node impurity measure typically the Gini index

31 Ensemble classifiers 31 General Idea

32 Random Forests – The algorithm 32 Feature importance insight Massive parallelization potential

33 Presentation outline 33 Problem statement Data mining Support Vector Machines Evolution Strategies Random Forests Solution – Results Conclusion

34 Solution Overview Linking WAMS with SCADA data… 34 WAMS PMU measurements time-stamped oscillations damping ratios SCADA system (time-stamped data) generation, load dispatch line power flows FACTS devices status (PSS status) … Train classifier Database input variables output labels Need for proper feature selection

35 Test system - Modified Nordic32 12978 samples, produced by simulations 35 (based on participation factors from linear model) Generators mostly participating at the 0.4-0.5Hz mode

36 Correspond to different PSS being off 4851 samples 1643 samples (out of 12978) 1271 samples 3580 samples Damping vs. Intertie Cut Correlated, but …

37 ES-SVM classifier 10-fold cross-validation accuracy 37 Input featureskernel mixedradial basispolynomial Onlyintertieflow92.7 92.0 Intertieflow&PSSstatus93.494.092.8 Dispatch95.6 Intertieflow,PSSstatus& syntheticfeatures 98.397.898.2 Dispatch&PSSstatus98.697.898.3 Dispatch,powerflows, PSSstatus&synthetic features 99.298.699.1 95.6

38 Random Forest classifier Out-of-bag accuracy 38 Input featuresAccuracy Dispatch, power flows, PSS status & synthetic features 97.79 PSS, Intertie, Line 18, Line 32 98.54 PSS, Intertie, Gen63, Line 16, Line 32 98.53 PSS, Intertie, Gen63 & 6 line flows 98.59 18 32 Gen63 very efficient feature selection less accurate than SVM 16

39 Presentation outline 39 Problem statement Data mining Support Vector Machines Evolution Strategies Random Forests Solution – Results Conclusion

40 Conclusion … and challenges 40 WAMS-SCADA link turned out to be an interesting idea At least for the inter-area oscillations case SVM achieved higher accuracy proper SVM tuning pays off RFs are not much worse, while allowing for very efficient feature selection Challenges… Check in real data Computational intensiveness Close the loop – Correct operating point based on model

41 Acknowledgment 41 The author gratefully acknowledges the financial support from Marie Curie FP7-IAPP Project: Using real-time measurements for monitoring and management of power transmission dynamics for the smart grid- REAL-SMART, Contract No. PIAP- GA 2009-251304

42 Thank you for your attention! Adamantios Marinakis ABB Corporate Research Switzerland Phone: +41 585867307 Mobile: +41 798766227 adamantios.marinakis@ch.abb.com


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