Design of support vector machine controller for hybrid power system automatic generation control

References

• Abe, S. 2015. “Advances in pattern recognition: Support vector Machines for Pattern classification. London: Springer.
   Google Scholar
• Alhelou, H. H., M.-E. Hamedani-Golshan, R. Zamani, E. Heydarian-Forushani, and P. Siano. 2018. Challenges and opportunities of load frequency control in conventional, modern and future smart power systems: a comprehensive review”. Energies 11 (10):2497. doi:10.3390/en11102497.
   Web of Science ®Google Scholar
• Anderson, C., J. E. Solem, and B. Eliasson. 2005. Classification of Power System Stability using Support Vector Machines. IEEE Power Engineering Society general Meeting 1:650–55.
   Google Scholar
• AYang, W. L., and X. Yang. 2019. Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines. Knowledge-Based Systems 163 (1):159–73. doi:10.1016/j.knosys.2018.08.027.
   Web of Science ®Google Scholar
• Banakar, H., L. Changling, and B. T. Ooi. 2008. Impacts of wind power minute-to minute variations on power system operation. IEEE Trans. Power Syst 23 (1):150–60. doi:10.1109/TPWRS.2007.913298.
   Web of Science ®Google Scholar
• Bevrani, H. 2009. Robust power system frequency control. New York: Springer.
   Google Scholar
• Bevrani, H., P. R. Daneshmand, P. Babahajiani, Y. Mitani, and T. Hiyama. 2013. Intelligent LFC concerning high penetration of wind power: synthesis and real-time application. IEEE Trans. Sustain. Energy 5 (2):655–62. doi:10.1109/TSTE.2013.2290126.
   Web of Science ®Google Scholar
• Bevrani, H., and T. Hiyama. 2011. “Intelligent Automatic Generation Control.” LLC: CRC Press,Taylor & Francis Group.
   Google Scholar
• Bhatti, T. S., A. A. F. Al-Ademi, and N. K. Bansal. 1997. Load frequency control of isolated wind diesel hybrid power system. Energy Conver. Mgnt 3 (9):829–37. doi:10.1016/S0196-8904(96)00108-2.
   Web of Science ®Google Scholar
• Boonprasert, U., N. Theera-Umpon, and C. Rakpenthai, “Support Vector Regression based adaptive power system stabilizer. Circuits and systems”, Proceedings of the 2003 International Symposium, Bangkok, Thailand, 25-28 May 2003. IEEE, 2003; 3: III-371-III–374.
   Google Scholar
• Celik, E., N. Öztürk, Y. Arya, and C. Ocak. 2021. (1+ PD)-PID cascade controller design for performance betterment of load frequency control in diverse electric power systems. Neural Computing and Applications 33 (22):15433–56. doi:10.1007/s00521-021-06168-3.
   Web of Science ®Google Scholar
• Cortes, C., and V. Vapnik. 1995. Support-vector networks. Mach Learn 20:273–97. doi:10.1007/BF00994018.
   Web of Science ®Google Scholar
• CukSupriyadi, A. N., and T. Hashiguchi, “Control Scheme of Hybrid Wind-Diesel Power Generation System in From Turbine to Wind Farms - Technical Requirements and Spin-Off Products,” www.intechopen.com, April 4, 2011, p. Chapter 5.
   Google Scholar
• Deng, Y.-C., X.-H. Tang, Z.-Y. Zhou, Y. Yang, and F. Niu. 2021. Application of machine learning algorithms in wind power: a review, Energy Sources. Part A: Recovery, Utilization, and Environmental Effects 1–22. doi:10.1080/15567036.2020.1869867.
   Google Scholar
• Eke, I., M. Saka, H. Gozde, Y. Arya, and M. C. Taplamacioglu. 2021. Heuristic optimization based dynamic weighted state feedback approach for 2DOF PI-controller in automatic voltage regulator. Engineering Science and Technology, an International Journal 24 (4):899–910. doi:10.1016/j.jestch.2020.12.023.
   Google Scholar
• Elgerd, O. I., and C. Fosha. 1970. Optimum megawatt frequency control of multi-area electric energy systems. IEEE Trans. Power Appar. Syst PAS-89 (4):556–63. doi:10.1109/TPAS.1970.292602.
   Web of Science ®Google Scholar
• Haruni, A. M. O., A. Gargoom, M. E. Haque, and M. Negnevitsky, “Voltage and frequency stabilisation of wind-diesel hybrid remote area power systems,” in Proc. Australia’s. Univ. Power Eng. Conf, Adelaide, SA, Australia, 27-30 Sept. 2009.2009, pp. 1–8.
   Google Scholar
• Hunter, R., and E. George. 1994. “Wind-diesel systems a guide to technology and its implementation.” Cambridge: Cambridge University Press.
   Google Scholar
• Jiang, L., W. Yao, Q. H. Wu, J. Y. Wen, and S. J. Cheng. 2011. Delay-dependent stability for load frequency control with constant and time-varying delays. IEEE Transactions on Power systems 27 (2):932–41. doi:10.1109/TPWRS.2011.2172821.
   Web of Science ®Google Scholar
• Kunder, P., N. J. Balu, and M. G. Lauby. 1994. Power system stability and control. New York: McGraw-Hill.
   Google Scholar
• Li, B., C. Delpha, A. Migan-Dubois, and D. Diallo. 2021. Fault diagnosis of photovoltaic panels using full I–V characteristics and machine learning techniques. Energy Conversion and Management 248:114785. doi:10.1016/j.enconman.2021.114785.
   Web of Science ®Google Scholar
• Li, X., S. Wu, X. Li, H. Yuan, and D. Zhao. 2020. Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers. Chin. J. Mech. Eng 33 (6):1–10. doi:10.1186/s10033-019-0428-5.
   Google Scholar
• Luo, C., and B. T. Ooi. 2006. Frequency deviation of thermal power plants due to wind farms. IEEE Trans. Energy Convers 21 (3):708–16. doi:10.1109/TEC.2006.874210.
   Web of Science ®Google Scholar
• Murthy, S. S., S. Mishra, G. Mallesham, and P. C. Sekhar. 2010. Voltage and frequency control of wind diesel hybrid system with variable speed wind turbine. Proc. Joint Int. Conf. Power Electron. Drives Energy Syst 1–6.
   Google Scholar
• Padmaja, A., and K. R. Sudha. 2017. Power System Load Frequency Regulation using Monte-Carlo Parameter Estimation based Support Vector Machine. Majlesi Journal of Energy Management 6 (4):1–14.
   Google Scholar
• Padmaja, A., and K. R. Sudha. 2020. Frequency regulation in a multi-area power system in presence of hybrid power system using Support Vector Machine based load frequency controller. i-manager’s Journal on Electrical Engineering 14 (1):13–24. doi:10.26634/jee.14.1.17619.
   Google Scholar
• Sharma, G., K. R. N. Ibraheem Nasiruddin, and R. C. Bansal. 2018. Automatic Generation Control (AGC) of Wind Power System: A Least Squares-Support Vector Machine (LS-SVM) Radial Basis Function (RBF) Kernel Approach. Electric Power Components and Systems 46 (14–15):1621–33. doi:10.1080/15325008.2018.1511003.
   Web of Science ®Google Scholar
• Sharma, G., A. Panwar, Y. Arya, and M. Kumawat. 2020. Integrating layered recurrent ANN with robust control strategy for diverse operating conditions of AGC of the power system. IET Generation, Transmission & Distribution 14 (18):3886–95. doi:10.1049/iet-gtd.2019.0935.
   Web of Science ®Google Scholar
• Sharma, G., A. Panwar, I. Nasiruddin, and R. C. Bansal. 2018. Non-linear LS-SVM with RBF-kernel-based approach for AGC of multi-area energy systems. IET Gener. Transm. Distrib 12 (14):3510–17. doi:10.1049/iet-gtd.2017.1402.
   Web of Science ®Google Scholar
• Shayeghi, H., H. A. Shayanfar, and A. Jalili. 2009. Load frequency control strategies: A state of-the-art survey for the researcher. Energy conversion and management 50 (2):344–53. doi:10.1016/j.enconman.2008.09.014.
   Web of Science ®Google Scholar
• Stankovic, A. M., G. I. L. E. A. D. Tadmor, and T. A. Sakharuk. 1998. On robust control analysis and design for load frequency regulation. IEEE Transactions on Power Systems 13 (2):449–55. doi:10.1109/59.667367.
   Web of Science ®Google Scholar
• Sudha, K. R., T. Butchi Raju, and P. V. G. D. Prasad Reddy. 2010. “Adaptive Power System Stabilizer using Support Vector Machine. Journal of Engineering Science and Technology” 2 (3):442–47.
   Google Scholar
• Sudha, K. R., and R. Vijaya Santhi. 2011. Robust decentralized load frequency control of interconnected power system with generation rate constraint using type-2 fuzzy approach. International Journal of Electrical Power & Energy Systems 33 (3):699–707. doi:10.1016/j.ijepes.2010.12.027.
   Web of Science ®Google Scholar
• Vijaya Santhi, R., and K. R. Sudha. 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.
   Google Scholar
• Vladimir, N. V. 1995. “The nature of statistical Learning Theory.” New York: Springer.
   Google Scholar
• Zhang, M., K. Li, and Y. Hu. 2012. Classification of power quality disturbances using wavelet packet energy and multiclass support vector machine”. COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 31 (2):424–42. doi:10.1108/03321641211200518.
   Web of Science ®Google Scholar