Education, Science, Technology, Innovation and Life
Open Access
Sign In

Cascading Failure Identification System of Transmission Network Based on GA Genetic Algorithm

Download as PDF

DOI: 10.23977/jeeem.2022.050206 | Downloads: 11 | Views: 491

Author(s)

Fuyu Deng 1

Affiliation(s)

1 Sichuan Vocational and Technical College, Suining, Sichuan 629000, China

Corresponding Author

Fuyu Deng

ABSTRACT

Our country's power grids have been interconnected to form a super-large power grid. The scale and complexity of the power grid continue to increase, leading to an increasing risk of large-scale power outages, and the adverse impact of accidents on society and the national economy is also greater. Therefore, studying power system cascading failures and developing control theories and methods for such failures have important theoretical significance and application value. In this paper, the cascading failure identification system of transmission network based on GA genetic algorithm is studied. According to the related system structure, a cascading failure identification system structure based on GA genetic algorithm is proposed, and then the performance of the system is compared and analyzed on the basis. According to the experimental results, the system designed in this paper has only one group of faults with missed selection, and the correct rate of line selection is as high as 90%, while the traditional system has 2 groups of faults with missed and wrong selections, and the correct rate of line selection is 80%. Therefore, the simulation the results show that the accuracy of the system designed in this paper is higher than that of the standard system.

KEYWORDS

Cascading Failure, Power System, Transmission Grid Failure, GA Genetic Algorithm

CITE THIS PAPER

Fuyu Deng, Cascading Failure Identification System of Transmission Network Based on GA Genetic Algorithm. Journal of Electrotechnology, Electrical Engineering and Management (2022) Vol. 5: 40-47. DOI: http://dx.doi.org/10.23977/jeeem.2022.050206.

REFERENCES

[1] Azzolin A, Duenas-Osorio L, Cadini F, et al. Electrical and topological drivers of the cascading failure dynamics in power transmission networks. Reliability Engineering & System Safety, 2018, 175(JUL.):196-206.
[2] Li Y, Teng Y, Xu M, et al. Security and Stability Analysis Method for AC/DC UHV Power System Cascading Failure Based on Improved Risk Assessment. Gaodianya Jishu/High Voltage Engineering, 2018, 44(11):3743-3750.
[3] Chen L, Wang J, Zhou Z, et al. Evaluations of node importance of urban road network based on transmission contribution matrix. Science & Technology Review, 2018, 36(6):105-111.
[4] Zhai S, Chen X, Wei L, et al. Research on identification methods of gas content in transformer insulation oil based on deep transfer network. Journal of Materials Science: Materials in Electronics, 2020, 31(18):15764-15772.
[5] Meng Y. Research and analysis of intelligent English learning system based on improved neural network. Journal of Intelligent and Fuzzy Systems, 2020, 39(2):1-11.
[6] D Lee, Shibahara K, Kobayashi T, et al. A Sparsity Managed Adaptive MIMO Equalization for Few-Mode Fiber Transmission With Various Differential Mode Delays. Journal of Lightwave Technology, 2016, 34(8):1754-1761.
[7] Gupta S, F Kazi, Wagh S, et al. Analysis and prediction of vulnerability in smart power transmission system: A geometrical approach. International Journal of Electrical Power & Energy Systems, 2018, 94:77-87.
[8] Dobson I, Carreras B A, Newman D E, et al. Obtaining statistics of cascading line outages spreading in an electric transmission network from standard utility data. IEEE Transactions on Power Systems, 2016, 31(6):4831-4841.
[9] Gan L, Li G, Zhou M. Coordinated planning of large-scale wind farm integration system and regional transmission network considering static voltage stability constraints. Electric Power Systems Research, 2016, 136(Jul.):298-308.
[10] Bollinger L A, Dijkema G P J. Evaluating infrastructure resilience to extreme weather – the case of the Dutch electricity transmission network. European Journal of Transport and Infrastructure Research, 2016, 16(1):214-239.
[11] Akhter F, Memon A A, Shaikh N N. A Proposed Supergrid Model for National Transmission Network of Pakistan. Mehran University Research Journal of Engineering and Technology, 2017, 36(1):149-158.
[12] Khuntia S R, Tuinema B W, Rueda J L, et al. Time-horizons in the planning and operation of transmission networks: an overview. Iet Generation Transmission & Distribution, 2016, 10(4):841-848.

Downloads: 1969
Visits: 93820

Sponsors, Associates, and Links


All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.