104
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Prediction of probabilistic transient stability using support vector regression

ORCID Icon
Pages 35-49 | Received 04 Mar 2021, Accepted 27 Jun 2022, Published online: 13 Aug 2022

References

  • Abapour, M., and M. Haghifam. 2012. “Probabilistic Transient Stability Assessment for on-line Applications.” International Journal of Electrical Power & Energy Systems 42 (1, Nov): 627–634. doi:10.1016/j.ijepes.2012.03.025.
  • Agber, J. U., P. E. Odaba, and C. O. Onah. 2015. “Effect of Power System Parameters on Transient Stability Studies.” American Journal of Engineering Research 4 (2): 87–94.
  • Bae, K. Y., H. S. Jang, and D. K. Sung. 2017. “Hourly Solar Irradiance Prediction Based on Support Vector Machine and Its Error Analysis.” IEEE Transactions on Power Systems 32 (2): 935–945.
  • Billinton, R., and P. R. S. Kuruganty. 1979. “Probabilistic Considerations in Transient Stability Assessment.” Canadian Electrical Engineering Journal 4 (2, Apr): 26–30. doi:10.1109/CEEJ.1979.6593672.
  • Billinton, R., and P. R. S. Kuruganty. 1981. “Probabilistic Assessment of Transient Stability in a Practical Multimachine System.” IEEE Transactions on Power Apparatus and Systems PAS-100 (7, Jul): 3634–3641. doi:10.1109/TPAS.1981.316657.
  • Buwei, W., C. Jianfeng, W. Bo, and F. Shuanglei. 2018. “A Solar Power Prediction Using Support Vector Machines Based on multi-source Data Fusion.” International Conference on Power System Technology (POWERCON), Guangzhou, China, 4573–4577.
  • DIgSILENT PowerFactory User Manual, DIgSILENT GmbH, 2018. [Online]. https://www.digsilent.de/en/downloads.html
  • Falehi, A. D. 2017. “Optimal Design of fuzzy-AGC Based on PSO & RCGA to Improve Dynamic Stability of Interconnected Multi Area Power Systems.” International Journal of Automation and Computing 17 (Apr): 599–609. doi:10.1007/s11633-017-1064-0.
  • Falehi, A. D. 2019. “Optimal Fractional Order BELBIC to Ameliorate Small Signal Stability of Interconnected Hybrid Power System.” Environmental Progress & Sustainable Energy 38 (5): 1–18.
  • Falehi, A. D. 2020. “Optimal Robust Disturbance observer-based Sliding Mode Controller Using multi-objective Grasshopper Optimization Algorithm to Enhance Power System Stability.” Journal of Ambient Intelligence and Humanized Computing 11 (11, Feb): 5045–5063. doi:10.1007/s12652-020-01811-8.
  • Fang, J., W. Yao, J. Wen, S. Cheng, Y. Tang, Z. Cheng. 2013. “Probabilistic Assessment of Power System Transient Stability Incorporating SMES.” Physica C: Superconductivity 484: 276–281. doi:10.1016/j.physc.2012.03.068.
  • Faried, S. O., R. Billinton, and S. Aboreshaid. 2009. “Probabilistic Evaluation of Transient Stability of a Wind Farm.” IEEE Transactions on Energy Conversion 24 (3, Sep): 733–739. doi:10.1109/TEC.2009.2016035.
  • Faried, S. O., R. Billinton, and S. Aboreshaid. 2010. “Probabilistic Evaluation of Transient Stability of a Power System Incorporating Wind Farms.” IET Renewable Power Generation 4 (4, Jul): 299–307. doi:10.1049/iet-rpg.2009.0031.
  • Gomez, F. R., A. D. Rajapakse, U. D. Annakkage, and I. T. Fernando. 2011. “Support Vector machine-based Algorithm for post-fault Transient Stability Status Prediction Using Synchronized Measurements.” IEEE Transactions on Power Systems 26 (3, Aug): 1474–1483. doi:10.1109/TPWRS.2010.2082575.
  • Guo, T., and J. V. Milanović. 2016. “Online Identification of Power System Dynamic Signature Using PMU Measurements and Data Mining.” IEEE Transactions on Power Systems 31 (3, May): 1760–1768. doi:10.1109/TPWRS.2015.2453424.
  • Gupta, A., G. Gurrala, and P. S. Sastry. 2019. “An Online Power System Stability Monitoring System Using Convolutional Neural Networks.” IEEE Transactions on Power Systems 34 (2, Mar): 864–872. doi:10.1109/TPWRS.2018.2872505.
  • Han, D., J. Ma, A. Xue, T. Lin, and G. Zhang. 2014. “The Uncertainty and Its Influence of Wind Generated Power on Power System Transient Stability under Different Penetration.” International Conference on Power System Technology, Chengdu, China, 675–680.
  • Hsu, Y.-Y., and C. Chung-Liang. 1988. “Probabilistic Transient Stability Studies Using the Conditional Probability Approach.” IEEE Transactions on Power Systems 3 (4, Nov): 1565–1572. doi:10.1109/59.192966.
  • Hu, L., L. Zhang, T. Wang, and K. Li. 2020. “Short-term Load Forecasting Based on Support Vector Regression considering Cooling Load in Summer.” Chinese Control and Decision Conference (CCDC), Hefei, China, 5495–5498.
  • Hua, K., A. Vahidnia, Y. Mishra, and G. Ledwich. 2016. “Efficient Probabilistic Contingency Analysis through a Stability Measure considering Wind Perturbation.” IET Generation, Transmission & Distribution 10 (4): 897–905. doi:10.1049/iet-gtd.2015.0496.
  • Huang, J., L. Guan, Y. Su, H. Yao, M. Guo, and Z. Zhong. 2020. “Recurrent Graph Convolutional network-based multi-task Transient Stability Assessment Framework in Power System.” IEEE Access 8 (Apr): 93283–93296. doi:10.1109/ACCESS.2020.2991263.
  • Hyperparameter Optimization in Regression Learner App. [Online]. https://www.mathworks.com/help/stats/hyperparameter-optimization-in-regression-learner-app.html#mw_c9bef99e-9b64-42c3-a9bf-6ff4295b201c
  • Jayashree, K., and K. S. Swarup. 2010. “A Distributed Computing Environment for Probabilistic Transient Stability Analysis.” 16th National Power Systems Conference, Hyderabad, India, pp. 329–335.
  • Kundur, P., Paserba, J., Ajjarapu, V., Andersson, G., Bose, A., Canizares, C., Hatziargyriou, N., et al. 2004. “Definition and Classification of Power System Stability IEEE/CIGRE Joint Task Force on Stability Terms and Definitions”. IEEE Transactions on Power Systems 19 (3): 1387–1401.
  • Li, D. H., and Y. J. Cao. 2005. “SOFM Based Support Vector Regression Model for Prediction and Its Application in Power System Transient Stability Forecasting.” International Power Engineering Conference, Singapore, pp. 765–770.
  • Li, H., R. Diao, X. Zhang, Xi Lin, X. Lu, D Shi, Z. Wang, and L. Wang. 2019. “An Integrated Online Dynamic Security Assessment System for Improved Situational Awareness and Economic Operation.” IEEE Access 7: 162571–162582. doi:10.1109/ACCESS.2019.2952178.
  • Limei, L., and H. Xuan. 2017. “Study of Electricity Load Forecasting Based on Multiple Kernels Learning and Weighted Support Vector Regression Machine.” 29th Chinese Control and Decision Conference (CCDC) Chongqing, China, pp. 1421–1424.
  • Liu, X., Y. Min, L. Chen, X. Zhang, and C. Feng. 2020. “Data-driven Transient Stability Assessment Based on Kernel Regression and Distance Metric Learning.” Journal of Modern Power Systems and Clean Energy 9(Jun): 1–10.
  • Luenberger, D. G., and Y. Ye. 2008. Linear and Nonlinear Programming. New York: Springer.
  • Moradzadeh, A., and K. Pourhossein. 2019. “Application of Support Vector Machines to Locate Minor Short Circuits in Transformer Windings.” 54th International Universities Power Engineering Conference (UPEC) Bucharest, Romania, pp. 1–6.
  • Murphy, K. P. 2012. Machine Learning: A Probabilistic Perspective. Palo Alto, California, USA: MIT Press. https://books.google.com.pk/books?id=NZP6AQAAQBAJ.
  • Nageem, R., and R. Jayabarathi. 2017. “Predicting the Power Output of a grid-connected Solar Panel Using multi-input Support Vector Regression.” Procedia Computer Science 115: 723–730. doi:10.1016/j.procs.2017.09.143.
  • Papadopoulos, P. N., and J. V. Milanović. 2015. “Impact of Penetration of non-synchronous Generators on Power System Dynamics.” IEEE Eindhoven PowerTech. Eindhoven, Netherlands, pp. 1–6.
  • Papadopoulos, P. N., and J. V. Milanović. 2017. “Probabilistic Framework for Transient Stability Assessment of Power Systems with High Penetration of Renewable Generation.” IEEE Transactions on Power Systems 32 (4, Jul): 3078–3088. doi:10.1109/TPWRS.2016.2630799.
  • Power Systems Test Case Archive. [Online]. http://www.ee.washington.edu/research/pstca/pf14/pg_tca14bus.htm
  • Regression Learner App. [Online]. https://www.mathworks.com/help/stats/regression-learner-app.html
  • Ren, C., Y. Xu, Y. Zhang, and C. Hu. 2018. “A Multiple Randomized learning-based Ensemble Model for Power System Dynamic Security Assessment.” IEEE Power & Energy Society General Meeting, Portland, USA, pp. 1–5,
  • S, W. 2006. “Noble, What is a Support Vector Machine?” Nature Biotechnology 24: 1565–1567. doi:10.1038/nbt1206-1565.
  • Selvi, B. D. A., and N. Kamaraj. 2009. “Support Vector Regression Machine with Enhanced Feature Selection for Transient Stability Evaluation.” Asia-Pacific Power and Energy Engineering Conference, Wuhan, China, pp. 1–5.
  • Shahriyari, M., H. Khoshkhoo, A. Pouryekta, and V. K. Ramachandaramurthy. 2019. “Fast Prediction of Angle Stability Using Support Vector Machine and Fault Duration Data.” IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) Selangor, Malaysia, 258–263.
  • Shahzad, U. 2021. “Application of Supervised Machine Learning for Prediction of Probabilistic Transient Stability.” Australian Journal of Electrical and Electronics Engineering 19 (1, Dec): 65–78. doi:10.1080/1448837X.2021.2013418.
  • Shahzad, U. 2022. “A Comparative Analysis of Artificial Neural Network and Support Vector Machine for Online Transient Stability Prediction considering Uncertainties.” Australian Journal of Electrical and Electronics Engineering 19 (2): 101–116. doi:10.1080/1448837X.2021.2022999.
  • Shi, L., S. Sun, L. Yao, Y. Ni, and M. Bazargan. 2014. “Effects of Wind Generation Intermittency and Volatility on Power System Transient Stability.” IET Renewable Power Generation 8 (5, Jul): 509–521. doi:10.1049/iet-rpg.2013.0028.
  • Sobbouhi, A. R., and A. Vahedi. 2021. “Transient Stability Prediction of Power System; a Review on Methods, Classification and Considerations.” Electric Power Systems Research 190 (Jan): 1–16. doi:10.1016/j.epsr.2020.106853.
  • Vaahedi, E., W. Li, T. Chia, and H. Dommel. 2000. “Large Scale Probabilistic Transient Stability Assessment Using BC Hydro’s on-line Tool.” IEEE Transactions on Power Systems 15 (2, May): 661–667. doi:10.1109/59.867156.
  • Vapnik, V. 1995. The Nature of Statistical Learning Theory. New York, NY, USA: Springer-Verlag.
  • Wahab, N. I. A., A. Mohamed, and M. Al Dabbagh. 2008. “Transient Stability Assessment of a Large Actual Power System Using Least Squares Support Vector Machine with Enhanced Feature Selection.” Australasian Universities Power Engineering Conference, Sydney, Australia, pp. 1–6.
  • Wu, S., 3 Best Metrics to Evaluate Regression Model? [Online]. https://towardsdatascience.com/what-are-the-best-metrics-to-evaluate-your-regression-model-418ca481755b
  • Yan, X., Y. Liu, Z. Mao, Z. Li, and H. Tan. 2006. “SVM-based Elevator Traffic Flow Prediction.” 6th World Congress on Intelligent Control and Automation, Dalian, China, pp. 8814–8818.
  • Ye, J., and L. Yang. 2019. “Short-term Load Forecasting Using Ensemble Empirical Mode Decomposition and Harmony Search Optimized Support Vector Regression.” 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Xi'an, China, 851–855.
  • You, D., Ke Wang, L. Ye, J. Wu, R. Huang. 2013. “Transient Stability Assessment of Power System Using Support Vector Machine with Generator Combinatorial Trajectories Inputs.” International Journal of Electrical Power & Energy Systems 44 (1): 318–325. doi:10.1016/j.ijepes.2012.07.057.
  • Yousefian, R., R. Bhattarai, and S. Kamalasadan. 2017. “Transient Stability Enhancement of Power Grid with Integrated Wide Area Control of Wind Farms and Synchronous Generators.” IEEE Transactions on Power Systems 32 (6, Nov): 4818–4831. doi:10.1109/TPWRS.2017.2676138.
  • Yuanhang, D., C. Lei, Z. Weiling, and M. Yong. 2015. “Multi-support Vector Machine Power System Transient Stability Assessment Based on Relief Algorithm.” IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) Brisbane, Australia, pp. 1–5.
  • Zhang, T., M. Sun, J. L. Cremer, N. Zhang, G. Strbac, and C. Kang. 2021. “A confidence-aware Machine Learning Framework for Dynamic Security Assessment.” IEEE Transactions on Power Systems 36 (5, Sep): 3907–3920. doi:10.1109/TPWRS.2021.3059197.
  • Zhi, D., N. Lin, and Z. Jian. 2007. “Application of Support Vector Regression Model Based on Phase Space Reconstruction to Power System wide-area Stability Prediction.” International Power Engineering Conference (IPEC 2007) Singapore, pp. 1371–1376.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.