Power System Transient Stability Analysis using Decision Tree Classifier- A Case Study on the IEEE 57- Bus System

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Authors

  • Department of Electrical Engineering, Swami Vivekananda University, Kolkata – 700121, West Bengal ,IN
  • Department of Electrical Engineering, Indian Institute of Engineering Science and Technology, Shibpur, Howrah – 711103, West Bengal ,IN
  • Department of Electrical Engineering, Swami Vivekananda University, Kolkata – 700121, West Bengal ,IN
  • Department of Electrical Engineering, Swami Vivekananda University, Kolkata – 700121, West Bengal ,IN
  • Department of Electrical Engineering, Swami Vivekananda University, Kolkata – 700121, West Bengal ,IN
  • Department of Electrical Engineering, Swami Vivekananda University, Kolkata – 700121, West Bengal ,IN

DOI:

https://doi.org/10.18311/jmmf/2023/43590

Keywords:

Classification, Decision Tree, Dynamic Security, Power System Transient Stability

Abstract

This paper presents a novel method of “power system dynamic security assessment” using “decision tree (DT) classifier”. The standard “pattern recognition framework”, has been followed in the research work presented in this paper, in order to ensure that real-time implementation of the proposed framework is feasible. With the aim of recognizing the “degree of criticality” associated with various “pre-contingency operational circumstances,” the “DTSC” was created and taught offline. The “Decision Tree Security Classifier (DTSC)” was successfully implemented in a simulated environment to recognize a power system’s “unforeseen operating conditions” and predict their vulnerability to “post- contingency dynamic insecurity”.

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Published

2024-05-24

How to Cite

Mukherjee, R., De, A., Saha, P. K., Mukherjee, S. D., Dhar, A., & Adhikari, S. (2024). Power System Transient Stability Analysis using Decision Tree Classifier- A Case Study on the IEEE 57- Bus System. Journal of Mines, Metals and Fuels, 71(12A), 30–39. https://doi.org/10.18311/jmmf/2023/43590

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References

Woo CK, King M, Tishler A, Chow LCH. Costs of electric- ity deregulation. Energy. 2006; 31(6-7):747-68. https:// doi.org/10.1016/j.energy.2005.03.002

Wehenkel L. Machine learning approaches to power system security assessment. IEEE Intell Syst. 1977; 12(5):60-72. https://doi.org/10.1109/64.621229

Kundur P. Power System Stability and Control, McGraw- Hill Education; 1994.

Zhang R, Xu Y, Dong ZY, Wong KP. Post- disturbance transient stability assessment of power systems by a self-adaptive intelligent system. IET Gener Transm Distrib. 2015; 9(3):296-305. https://doi.org/10.1049/iet- gtd.2014.0264

Laufenberg MJ, Pai MA. A new approach to dynamic security assessment using trajectory sensitivities. EEE Trans Power Syst. 1998; 13(3):953-8. https://doi. org/10.1109/59.709082

Dong ZY, Xu Y, Zhang P, Wong KP. Using IS to assess an electric power system’s real-time stability. IEEE Intelligent Systems. 2013; 28(4):60-6. https://doi. org/10.1109/MIS.2011.41

Sun K, Likhate S, Vittal V, Kolluri VS, Mandal S. An online dynamic security assessment scheme using pha- sor measurements and decision trees. IEEE Transactions on Power Systems. 2007; 22(4):1935-43. https://doi. org/10.1109/TPWRS.2007.908476

James JQ, Hill DJ, Lam AYS, Gu J, Li VOK. Intelligent time-adaptive transient stability assessment system. IEEE Transactions on Power Systems. 2017; 33(1):1049- 58. https://doi.org/10.1109/TPWRS.2017.2707501

Chang HD, Chu CC, Cauley G. Direct stability analysis of electric power systems using energy functions: Theory, applications, and perspective. Proc IEEE. 1995; 83(1):1497-529. https://doi.org/10.1109/5.481632

Morteza S, Wu NE, John SB. Transient stability assess- ment of large lossy power systems. IET Gener Transm Distrib. 2018; 12(8):1822-30. https://doi.org/10.1049/ iet-gtd.2017.0864

Rahimi FA, Lauby MG, Wrubel JN, Lee KL. Evaluation of the transient energy function method for on-line dynamic security analysis. IEEE Trans Power Syst. 1993; 8(2):497-507. https://doi.org/10.1109/59.260834

Vu TL, Turitsyn K. Lyapunov functions family approach to transient stability assessment. IEEE Trans Power Syst. 2015; 31(2):1269-77. https://doi.org/10.1109/ TPWRS.2015.2425885

Chiang H. D, Li H, and Tong J, On-Line Transient Stability Screening of a Practical 14,500-Bus Power sys- tem: Methodology and Evaluations, High Performance Computing in Power and Energy Systems, pp 335-358, 2013. https://doi.org/10.1007/978-3-642-32683-7_11

Ren LY, Tian F, Yan JF, Yu ZH, Su F, Wu T. Online application and fast solving method for critical clearing time of three-phase short circuit in power system. International journal of Smart grid and Clean Energy. 2013; 2(1). https://doi.org/10.12720/sgce.2.1.93-99

Wehenkel L, Van Cutsem T, Ribbens-Pavella M. An artificial intelligence framework for on-line transient stability assessment of power systems. IEEE Power Eng Rev. 1989; 9(5):77-8. https://doi.org/10.1109/MPER.1989.4310721

Fouad AA, Vekataraman S, Davis JA. An expert system for security trend analysis of a stability-limited power system. IEEE Trans Power Syst. 1991; 6(3):1077-84. https://doi.org/10.1109/59.119249

El Sharkawi MA. Vulnerability assessment and control of power system. IEEE/PES Transmission and Distribution Conference and Exhibition: Asian Pacific; 2005. p. 656-60.

Aghamohammadi MR, Mahdavizadeh F, Bagheri R. Power system dynamic security classification using Kohenen neural networks. IEEE/PES Power Systems Conference and Exposition Date of Conference; 2009. https://doi.org/10.1109/PSCE.2009.4840047 PMid:19339796

Rituparna M, Abhinandan D. Development of an ensemble decision tree-based power system dynamic security state predictor. IEEE Systems Journal. 2020; 14(3):3836- 43. https://doi.org/10.1109/JSYST.2020.2978504

Rituparna M, Abhinandan D. Real-time dynamic security analysis of power systems using strategic PMU measurements and decision tree classification. Electr Eng; 2020,

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