Evaluating the potential of cascading failure occurrence due to line outage in power systems by information theory method and radial base functions

abedi, morteza; aghamohammadi, mohammadreza; ameli, mohammadtaghi

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Iranian Electric Industry Journal of Quality and Productivity

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Volume 10, Issue 2 (7-2021)

ieijqp 2021, 10(2): 14-27

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Evaluating the potential of cascading failure occurrence due to line outage in power systems by information theory method and radial base functions

Morteza Abedi

, Mohammadreza Aghamohammadi ^*¹

, Mohammadtaghi Ameli

Abstract: (2759 Views)

In power systems, a connected topology is created to increase reliability and economic performance. Increasing dimensions of power systems on the one hand and the need to continuously monitor power systems for secure operation on the other challenge the evaluation of power system security. In such conditions, due to economic conditions and considering the fast growth of consumers in a power system and the need to supply them, power systems are operated in the proximity of their allowed operation limit. Since a power system includes a large number of transmission lines, the location of the lines in the power system and the number of lines compared to other devices like transformer and generator make the lines more vulnerable to events and potent to the outage. Therefore, events like a sudden loss of one or more transmission lines might violate operation constraints of the network and threaten the stability margins of the power system, resulting in the occurrence of cascading failures. The most important concern to prevent cascading failure resulting from line outages is, thus, to determine the potential of cascading failure resulting from line outages using power system control center (PSCC) information. Since PSCC can only access the operating variables of the power system, a method based on operating variables should be presented to determine the potential of cascading failure. Considering the large number of operating variables in a power system and the limitations of measurement and communication devices, it is impossible to use all variables to estimate the cascading failure potential of a specific line outage. Therefore, dominant operating variables (DOVs) should be identified to determine the potential of cascading failure resulting from line outage. This study presents a method based on mutual information theory and boundary equation to evaluate the potential of cascading failure. In the proposed method, the mutual information theory method is used to identify DOVs. Since identifying the DOVs of a power system alone is not sufficient for evaluating the potential of line outage-induced cascading failure, an index is also required to evaluate the potential of cascading failure. A mathematical equation is, therefore, developed as the boundary equation to evaluate the potential of cascading failure using the least-squares error (LSE) method based on the radial basis function (RBF). As such, the PSCC employs the identified DOVs and the boundary equation of each line to evaluate the potential of cascading failure at each operating point of the power system online before line outage. The proposed method is implemented on a standard 39-bus power system and a standard 118-bus power system, and desired results are obtained.

Keywords: Cascading failure, mutual information theory, dominant operating variables, Radial Base Function, boundary equation

Full-Text [PDF 1217 kb] (776 Downloads)

Type of Study: Research |
Received: 2020/05/30 | Accepted: 2021/05/5 | Published: 2021/07/1

References

1. Henry.S, Pompee.J, Devatine.L, Bulot.M, Bell.K (2004)."New TrendsFor The Assessment of Power System Security Under Uncertainty." IEEE Power Systems Conference and Exposition 3:1380-1385.

2. Ajendra Dwivedi (2011). "Vulnerability analysis and fault location in power systems using complex network theory," PhD thesis, College of Science, Engineering and Health RMIT University.

3. Zhou Liun(2013). "Multi-Agent System Based Special Protection and Emergency Control Scheme against Cascading Events in Power System." PhD thesis, The Faculty of Engineering, Science and Medicine, Aalborg University.

4. H.H Alhelou, and et al (2019). "A Survey on Power System Blackout and Cascading Events: Research Motivations and Challenges. "Energies, 12, 682. [DOI:10.3390/en12040682]

5. A Abedi, L Gaudard, F Romerio (2019). "Review of major approaches to analyze vulnerability in power system Reliability." Engineering & System Safety, Elsevier, 183:153-172. [DOI:10.1016/j.ress.2018.11.019]

6. R.Casimir, E.Boutleux, G.Clerc, A.Yahoui (2006). "The use of features selection and nearest neighbors rule for faults diagnostic in induction motors." Engineering Applications of Artificial Intelligence, 19(2): 169-177. [DOI:10.1016/j.engappai.2005.07.004]

7. Hafiz Muhammad Faisal,Nadeem Javaid et al. (2019). "Prediction of Building Energy Consumption Using Enhance Convolutional Neural Network.", Springer,927(2).

8. Saunders, C.S., Alamuti, M.M., Pisica, I. et al. (2019)." Feature Extraction-Based Real-Time Transient Stability Analysis." Technol Econ Smart Grids Sustain Energy 4(15). Chang, Hsueh-Hsien (2017). "Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems.", Energies 10(5): 611-618.https://doi.org/10.3390/en10050611 [DOI:10.1007/s40866-019-0069-x]

9. D.S.Javan , H.R. Mashhadi, A.Armin (2017) " Information extraction from effective descriptor variables in reconstruction of power system security region by considering correlation between loads.", International Transaction Electrical Energy System. John Wiley & Sons 27(11):1-23. [DOI:10.1002/etep.2415]

10. H.Liu, J.Sun, L. Liu, H.Zhang (2009). "Feature selection with dynamic mutual information.", Pattern Recognition(Elsevier), 42(7): 1330-1339. [DOI:10.1016/j.patcog.2008.10.028]

11. B.Remeseiro, V.Bolon-Canedo (2019)." A review of feature selection methods in medical applications.", Computers in Biology and Medicine(Elsevier) 112(3):1-9. [DOI:10.1016/j.compbiomed.2019.103375]

12. Meyer, P. E., Schretter, C., & Bontempi, G. (2008)," Information-theoretic feature selection in microarray data using variable complementarity." IEEE Journal of Selected Topics in Signal Processing 2(3): 261-274. [DOI:10.1109/JSTSP.2008.923858]

13. M.Rouhani, D.S. Javan (2016). "Two fast and accurate heuristic RBF learning rules for data classification." Neural Networks (Elsevier) 75(2): 150-161. [DOI:10.1016/j.neunet.2015.12.011]

14. Vergara, J.R., Estévez, P.A. (2014) "A review of feature selection methods based on mutual information." Neural Computer & Application(Elsevier) 24: 175-186. [DOI:10.1007/s00521-013-1368-0]

15. Hengdao Guo, Ciyan Zheng, Herbert Ho-Ching Iu, Tyrone Fernando (2017)" A critical review of cascading failure analysis and modeling of power system." Renewable and Sustainable Energy Reviews 80: 9-22. [DOI:10.1016/j.rser.2017.05.206]

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abedi M, aghamohammadi M, ameli M. Evaluating the potential of cascading failure occurrence due to line outage in power systems by information theory method and radial base functions. ieijqp 2021; 10 (2) :14-27
URL: http://ieijqp.ir/article-1-750-en.html

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