A Study of Intelligent Evaluation of Power System Transient Stability Based on Improved SVM Algorithm
Author: 
Qian WangAuthors Info & Claims
Qian WangAuthors Info & ClaimsPages 230 - 237
Abstract
Transient stability assessment of power system is the precondition of analyzing system transient stability, and it is the basis of effective analysis of power system transient stability. In order to better understand the operating state of the power system, this research builds a pinball loss SVM model based on the traditional SVMA algorithm to evaluate the transient stability of the power system. On this basis, the research takes a group of wind turbines as an example, and analyzes the calculation examples to verify the effectiveness of the pinball loss SVM model. The results show that the time limit of the proposed model is less than 10 seconds, and the average accuracy is about 94%. It can quickly and effectively evaluate the power system transient stability. It is hoped that this study can provide some reference for the evaluation of power system transient stability.
References
[1]
Robak S, Gryszpanowicz K, Piekarz M, et al (2019). Transient stability enhancement by series braking resistor control using local measurements. International journal of electrical power and energy systemse, 112, 272--281.
[2]
Hasan Z S, Hawas M Nl (2019). ENHANCEMENT OF POWER SYSTEM TRANSIENT STABILITY OF A MULTIMACHINE BASED ON INTERACTIONS OF PSS WITH FACTS DEVICES. Journal of Mechanical Engineering Research and Developments, 42(4), 161--167.
[3]
Hu W, Lu Z, Wu S, et al (2019). Real-time transient stability assessment in power system based on improved SVM. ournal of Modern Power System and Clean Energy (English Edition), (001), 26--37.
[4]
Amraee T, Naghdalian S, Kamali S (2019). A Linear Daily Unit Commitment Model to Improve the Transient Stability of Power System. IET Generation, Transmission & Distribution, 13(13), 2877--2888.
[5]
Tajdinian M, Seifi A R, Allahbakhshi M (2018). Sensitivity-based approach for real-time evaluation of transient stability of wind turbines interconnected to power grids. IET Renewable Power Generation, 12(6), 668--679.
[6]
Sharifi E, Mazinan A H (2018). On transient stability of multi-machine power systems through Takagi--Sugeno fuzzy-based sliding mode control approach. Complex & Intelligent Systems, 4(3), 171--179.
[7]
Hasanien H M (2019). Transient Stability Augmentation of a Wave Energy Conversion System Using a Water Cycle Algorithm-Based Multiobjective Optimal Control Strategy. IEEE Transactions on Industrial Informatics, 15(6), 3411--3419.
[8]
Sharifi E, Mazinan A H (2017). On transient stability of multi-machine power systems through Takagi--Sugeno fuzzy-based sliding mode control approach. Complex & Intelligent Systems, (1), 1--9.
[9]
Ledesma P, Calle I A, Castronuovo E D, et al (2017). Multi-contingency TSCOPF based on full-system simulation. IET Generation Transmission & Distribution, 11(1), 64--72.
[10]
Bagde B Y, Umre B S, Dhenuvakonda K R (2018). An efficient transient stability - constrained optimal power flow using biogeography - based algorithm. International Transactions on Electrical Energy Systems, 28(1), 1--15.
[11]
Abe S, Iioka D, Saitoh H (2018). Study on Adjustment Method of Output of Synchronous Generators for Transient Stability Improvement in Power System with Penetration of Renewable Energy Sources. IEEJ Transactions on Power and Energy, 138(10), 815--821.
[12]
L. Nanni, S. Brahnam (2020). Set of Approaches Based on Position Specific Scoring Matrix and Amino Acid Sequence for Primary Category Enzyme Classification. Journal of Artificial Intelligence and Systems, 2, 38--52. https://doi.org/10.33969/AIS.2020.21004.
Index Terms
- A Study of Intelligent Evaluation of Power System Transient Stability Based on Improved SVM Algorithm
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Published In
October 2020
566 pages
ISBN:9781450375535
DOI:10.1145/3421766
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Association for Computing Machinery
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Published: 26 October 2020
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AIAM2020
AIAM2020: 2nd International Conference on Artificial Intelligence and Advanced Manufacture
October 15 - 17, 2020
Manchester, United Kingdom
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AIAM2020 Paper Acceptance Rate 100 of 285 submissions, 35%;
Overall Acceptance Rate 100 of 285 submissions, 35%
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