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Frequency Regulation in a Multi-Area Power System in the Presence of Hybrid Power System using Support Vector Machine Based Load Frequency Controller

A. Padmaja*, K. R. Sudha**

* Department of Electrical & Electronics Engineering, JNTUK-UCEV, Vizianagaram, India.

** Department of Electrical Engineering, AUCE (A), Andhra University, Visakhapatnam, India.

Periodicity:July - September'2020
DOI : https://doi.org/10.26634/jee.14.1.17619

Abstract

This work focuses on a unique approach for an on-line adaptive tuning of Support Vector Machine (SVM) controller to regulate power system frequency whenever load perturbation occurs. A multi-area power system integrating with Hybrid Wind-Diesel system is considered for the present analysis. The proposed SVM controller is trained by input-output dataset of Proportional-Integral-Derivative (PID)-Load Frequency Controller. Primarily, the SVM controller is trained and analyzed with a three-area, nine-machine power system by neglecting system non-linearities. Further, to account for real-time conditions of the power system, all non-linearities and system uncertainties including variable wind input power are considered for the simulation to test the efficacy of the designed controller. The results show the robustness of the designed SVM controller over conventional PID controller. SVM based Load Frequency Controller ensures the zero steady state error for deviations in the frequency and maintaining minimum over/undershoots, the settling time of the frequency and deviation in the tie-line power under all operating conditions.

Keywords

Load Frequency Control, Support Vector Machine, Wind-Diesel Hybrid Power System.

How to Cite this Article?

Padmaja, A., and Sudha, K. R. (2020). Frequency Regulation in a Multi-Area Power System in the Presence of Hybrid Power System using Support Vector Machine Based Load Frequency Controller. i-manager's Journal on Electrical Engineering, 14(1), 13-24. https://doi.org/10.26634/jee.14.1.17619

References

[1]. Abe, S. (2005). Support vector machines for pattern classification (Vol. 2, p. 44). London: Springer.

[2]. Bevrani, H. (2009). Robust power system frequency control. New York: Springer.

[3]. Bevrani, H., & Hiyama, T. (2011). Intelligent automatic generation control. Taylor & Francis Group.

[4]. Boonprasert, U., Rakpenthai, C., Theera-Umpon, N. A. (2002). Comparison of support vector machines and back propagation neural networks in adaptive power system th stabilizer. In Proceedings of the 25 Electrical Engineering Conference, PW278-282, Prince of Songkla University, Thailand.

[5]. Boonprasert, U., Theera-Umpon, N., & Rakpenthai, C. (2003, May). Support vector regression based adaptive power system stabilizer. In Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS'03. (Vol. 3, pp. III-III). IEEE. https://doi.org/10.1109/ ISCAS.2003.1205033

[6]. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.

[7]. Hunter, R., & Elliot, G. (Eds.). (1994). Wind-diesel systems: A guide to the technology and its implementation. Cambridge University Press.

[8]. Liu, J., Zhao, Z., Tang, C., Yao, C., Li, C., & Islam, S. (2019). Classifying transformer winding deformation fault types and degrees using FRA based on support vector machine. IEEE Access, 7, 112494-112504.

[9]. Li, X., Wu, S., Li, X., Yuan, H., & Zhao, D. (2020). Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers. Chinese Journal of Mechanical Engineering, 33(1), 1-10. https://doi.org/10.1186/s10033-019-0428-5

[10]. Padmaja, A., & Sudha, K. R. (2017). Power system load frequency regulation using Monte-Carlo parameter estimation based support vector machine. Majlesi Journal of Energy Management, 6(4), 1-14.

[11]. Santhi, R. V., & Sudha, K. (2014). A robust decentralized controller for stand-alone wind systems and hybrid wind-diesel systems using type-2 fuzzy approach. International Journal of Signal Processing Systems, 2(1), 48- 54.

[12]. Shayeghi, H. A. S. H., Shayanfar, H. A., & Jalili, A. (2009). Load frequency control strategies: A state-of-the-art survey for the researcher. Energy Conversion and Management, 50(2), 344-353. https://doi.org/10.1016/j. enconman.2008.09.014

[13]. Sudha, K. R., Raju, Y. B., & Reddy, P. P. (2010). Adaptive power system stabilizer using support vector machine. International Journal of Encineerinc Science and Technology, 2(3), 442-447.

[14]. Supriyadi, C.A.N., Hashiguchi, T., Goda, T., & Tumiran. (2011). Control Scheme of Hybrid Wind-Diesel Power Generation System. In From Turbine to Wind Farms - Technical Requirements and Spin-Off Products. IntechOpen. https://doi.org/10.5772/15152

[15]. Vapnik, V. (2013). The nature of statistical learning theory. Springer Science & Business Media.

[16]. Yang, A., Li, W., & Yang, X. (2019). Short-term electricity load forecasting based on feature selection and least squares support vector machines. Knowledge-Based Systems, 163, 159-173.

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