Distributed Machine Learning on Dynamic Power System Data Features to Improve Resiliency for the Purpose of Self-Healing
Abstract
:1. Introduction
- Development of a feature selection based event detection algorithm for a multi-machine based grid, that can be sectionalized under duress. The proposed algorithm is prepared for the segmented power system used in this study. Despite not being a generic solution for all types of grids the algorithm introduces novelty in the decision making process.
- In larger systems, the curse of dimensionality poses a bigger threat in applying machine learning algorithms, specially for making decisions. The proposed method, by implementing feature extraction on a reduced data set, addresses those challenges and enables an effective decision making scheme.
2. System Under Consideration
3. The Proposed Supervised Control
3.1. Controlled Islanding
3.2. The Corrective Control
3.3. Power System Events
3.4. Available Observed Features
3.5. Multiclass Classifier
4. Results
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Decisions | Wind Speed (m/s) | Variable Demand (MW) | Contingencies |
---|---|---|---|
1 | 10 | 546 | Loss of Genearator-4. No segmentation is required |
2 | 12 | 609 | Loss of Genearator-9. No segmentation is required |
3 | 8 | 405 | Three phase fault at Bus-16. Two area segmentation |
4 | 12 | 35 | Three phase fault at Bus-19. Two area segmentation |
5 | 8.2 | 662 | Three phase fault at Bus-16 and Bus-25. Three area segmentation |
... | ... | ... | ... |
... | ... | ... | ... |
n | 11.2 | 408 | Three phase fault at Bus-25 and loss of Generator-4. Three area segmentation |
Decisions | Wind Speed (m/s) | Variable Demand (MW) | Snsvt (Sensitivity) | Prmn (Prominence) | FF (Frequency Factor) |
---|---|---|---|---|---|
1 | 12 | 35 | 0.1384 | 34.1 | 197.4 |
2 | 6 | 145 | 0.1333 | 0.659 | 34.48 |
3 | 8 | 405 | 0.1636 | 0.0566 | 7.5878 |
.. | .. | .. | .. | .. | .. |
.. | .. | .. | .. | .. | .. |
n | 11 | 790 | 0.1424 | 0.593 | 25.2 |
Decisions | Wind Speed (m/s) | Variable Demand (MW) | Snsvt (Fault-1) | Snsvt (Fault-2) | Prmn (Prominence) | FF (Frequency Factor) |
---|---|---|---|---|---|---|
1 | 8.2 | 662 | 0.8770 | 1.0195 | 0.4374 | 13.7012 |
2 | 10 | 372 | 0.9349 | 0.9910 | 0.7647 | 178.1212 |
.. | .. | .. | .. | .. | .. | .. |
.. | .. | .. | .. | .. | .. | .. |
n | 11.2 | 408 | 0.8515 | 0.2779 | 0.7099 | 32.4 |
Decisions | Wind Speed (m/s) | Variable Demand (MW) | Snsvt (Sensitivity) | Prmn (Prominence) | FF (Frequency Factor) |
---|---|---|---|---|---|
1 | 9.9965 | 252 | 0.2192 | 0.0008 | 46.7454 |
2 | 10 | 546 | 0.2119 | 0.2541 | 46.8115 |
.. | .. | .. | .. | .. | .. |
.. | .. | .. | .. | .. | .. |
n | 12 | 609 | 0.1547 | 0.1291 | 9.56 |
Generator | Proposed Method | Ensemble of Clusters and Decision Tree | ANN | PCA and Decision Tree |
---|---|---|---|---|
4 | 96.2 | 89.4 | <40 | 83.71 |
5 | 96.4 | 96.4 | <40 | 82.02 |
6 | 94.64 | 94.56 | <40 | 84.2 |
7 | 95.08 | 93.33 | <40 | 65.72 |
8 | 94.32 | 90.17 | <40 | 67.23 |
9 | 95.48 | 92.8 | <40 | 66.49 |
10 | 93.8 | 91.1 | <40 | 89.7 |
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Karim, M.A.; Currie, J.; Lie, T.-T. Distributed Machine Learning on Dynamic Power System Data Features to Improve Resiliency for the Purpose of Self-Healing. Energies 2020, 13, 3494. https://doi.org/10.3390/en13133494
Karim MA, Currie J, Lie T-T. Distributed Machine Learning on Dynamic Power System Data Features to Improve Resiliency for the Purpose of Self-Healing. Energies. 2020; 13(13):3494. https://doi.org/10.3390/en13133494
Chicago/Turabian StyleKarim, Miftah Al, Jonathan Currie, and Tek-Tjing Lie. 2020. "Distributed Machine Learning on Dynamic Power System Data Features to Improve Resiliency for the Purpose of Self-Healing" Energies 13, no. 13: 3494. https://doi.org/10.3390/en13133494