1. Neural Network Recognition of Frequency Disturbance Recorder Signals Stephen Tang REU Final Presentation July 22, 2014

2. Problem Recognize local differences in grid frequency Difficult because differences are small Approach of Principle Component Analysis (PCA) and Neural Networks (NN) 5-2 Frequency of several units with median removed

3. Why PCA & NN Both tools for extracting important features NN are strong pattern recognizers 5-3

4. PCA 5-4 Orders basis vectors according to variation along their direction ~90% of variation accounted for with first 100 principle components Percent Variation vs Number of Components Kept

5. PCA Results Neural Network Day of Testing SimultaneousNext Day1 Week No PCA92.0784.9672.93 With PCA92.9684.3972.03 5-5 Negligible Difference in Accuracy with PCA

6. Training a Neural Network Provide training vectors Split into 3 categories  Training  Validation  Testing Apply error minimization algorithm on training set 5-6

7. Competitive Function Output Weight Matrix/Bias Input Weight Matrix/Bias The Multi-Layer Perceptron Architecture 5-7 Input Vector

8. MLP over Probabilistic NN PNN does not require training PNN consistently gave almost 100% error PNN involves taking Euclidean distances between vectors 5-8 Distance between Input and Training Activation Decision

9. Hierarchical Clustering Hierarchical Shortest distance (Euclidean) Number of FDRs as cutoff 5-9 Clustering Distribution

10. Over Time Results 5-10 Day of Training Day of Testing - 2013 2014 June 2June 3June 4June 5June 6June 7June 8June 9 June 1June 2 92.0784.9666.4964.3162.3564.3173.9272.93 44.3042.86 June 3-91.2667.3964.6465.1064.1872.2067.32 44.7443.25 June 4--91.9274.0973.3166.1263.2557.48 36.5141.56 June 5---89.1078.3571.6967.7160.34 37.2241.55 June 6----92.0773.9469.0562.20 39.8444.32 June 293.2288.6568.4138.70 Western Interconnection Eastern Interconnection

11. Conclusions PCA safe for use Most effective for simultaneous inputs Accuracy degrades quickly over time 5-11

12. Acknowledgements This work was supported primarily by the ERC Program of the National Science Foundation and DOE under NSF Award Number EEC-1041877. Other US government and industrial sponsors of CURENT research are also gratefully acknowledged. Special Thanks to Yin (Laura) Lei and Dr. Liu Yilu 12

13. Hidden Layer Size Affects training time Accuracy is sensitive  Large: Over-fits training set  Small: Unable to classify 5-13

14. Training Duration Results Test SetTraining Duration (hours) 15102448 Simultaneous 94.7892.2592.6092.0789.21 1 Week60.6568.8869.0472.9374.14 5-14

15. Geographic Location Results 5-15