Power System Load Frequency Regulation using Monte-Carlo Parameter Estimation based Support Vector Machine

  • Padmaja A Assistant Professor
  • Sudha KR Andhra University
Keywords: Support vector machine (SVM), load frequency regulation, Load Frequency Control (LFC), non-linearities, Monte-Carlo parameter estimation.


The key objective of modern power generation, being dynamic and multifarious in nature, is to maintain power exchanges and system frequency to their contractual values to meet growing energy needs. This can be achieved by Load-Frequency Regulation using adaptive controllers. The present study illustrates an innovative approach for adaptive tuning of Support Vector Machine (SVM), a supervised machine learning algorithm, which can be used as a controller for Load Frequency Control (LFC) problem of electric grid to regulate the frequency and tie-line power flows in an interconnected power system. Primarily, an optimized Proportional Integral Derivative (PID) controller is designed for a two interconnected non-reheat thermal areas in which Monte Carlo parameter estimation method is used for sampling the values of uncertain parameters randomly. The input-output data set of optimized-PID controller is used to design a PID based Support Vector Machine (SVMPID) controller. The simulation studies are conducted to find the deviations in frequency and tie-line power exchanges resulting from a step load perturbation in each area. The comparative results are presented with conventional controller, optimized-PID controller and SVMPID controller. The efficacy of the trained SVMPID controller is tested on a three area interconnected thermal-thermal-hydropower system by considering generation rate constraint (GRC), dead band (DB) and boiler dynamics (BD) to represent real-time situation. The results show the performance of the proposed SVMPID controller and its capability to ensure zero steady state error by sustaining minimum over/undershoot and settling time in the system dynamic responses under multi-operating conditions.


[1] P. Kundur, “Power System Stability and Control”, McGraw Hill, Inc., New York, 1994.
[2] H.Shayeghi, H.A.Shayanfar, and A.Jalili, “Load frequency control strategies: A state-of-the-art survey for the researcher,” Energy conversion and management, Vol.50, 2, pp.344-353. 2009.
[3] S.K.Pandey, S.R.Mohanty, and N.Kishor, “A literature survey on load frequency control for conventional and distribution generation power systems”, Renewable and Sustainable Energy Reviews, vol.25, pp. 318–334, 2013.
[4] Yogesh V. Hote, and Shivam Jain, “PID controller design for load frequency control: Past, Present and future challenges” 3rd IFAC Conference on Advances in Proportional-Integral-Derivative Control, pp. 604-609, 2018.
[5] K.R.M.Vijaya Chandrakala and S.Balamurugan, “ Simulated annealing based optimal frequency and terminal voltage control of multi-source multi area system”, International Journal of Electrical Power & Energy Systems, Vol.78, pp. 823-829, 2016.
[6] F. Daneshfar and H. Bevrani, “Load–frequency control: a GA-based multi-agent reinforcement learning”, Vol 4, 1, pp. 13 – 26, 2010.
[7] V.S. Vakula and K.R. Sudha, “Design of differential evolution algorithm-based robust fuzzy logic power system stabiliser using minimum rule base”, IET Generation, Transmission & Distribution, Vol.6, 2 pp.121 – 132, 2012
[8] M. Omar, M. Soliman, A. M. Abdel Ghany, and F. Bendary, “Optimal Tuning of PID Controllers for Hydrothermal Load Frequency Control Using Ant Colony Optimization”, International Journal on Electrical Engineering and Informatics, Vol. 5, 3, pp. 348-360. 2013.
[9] H.Moklisha and A.H.A.Bakar, “Multiobjective optimization using weighted sum Artificial Bee Colony algorithm for Load Frequency Control” International Journal of Electrical Power & Energy Systems, Vol. 55, pp. 657-667, 2014.
[10] Banaja Mohanty and P.K.Hota, “A hybrid chemical reaction-particle swarm optimisation technique for automatic generation control”, Journal of Electrical Systems and Information Technology, Vol. 5, 2, pp. 229-244, 2018.
[11] S.Pothiya, I. Ngamroo, S. Runggeratigul and P. Tantaaswadi, “Design of optimal fuzzy logic based PI controller using multiple Tabu Search algorithm for load frequency control”, Int J Cont Autom Syst, Vol. 4, 2, pp.155-164, 2006.
[12] Chandan Kumar, Shiva and V.Mukherjee, “A novel quasi-oppositional harmony search algorithm for automatic generation control of power system”, Applied Soft Computing, Vol. 35, pp. 749-765, 2015.
[13] G.T.Chandra Sekhar, Rabindra Kumar Sahu, and A.K.Baliarsingh, “Load frequency control of power system under deregulated environment using optimal firefly algorithm”, International Journal of Electrical Power & Energy Systems, Vol. 74, pp.195-211, 2016.
[14] S. M. Abd-Elazim and E. S. Ali, “Firefly algorithm-based load frequency controller design of a two area system composing of PV grid and thermal generator”, Electrical Engineering, Vol. 100, 2, pp. 1253-1262. 2018
[15] A.Y.Abdelaziz and E.S.Ali, “Cuckoo Search algorithm based load frequency controller design for nonlinear interconnected power system”, International Journal of Electrical Power & Energy Systems, Vol. 73, pp. 632-643, 2015.
[16] H. Haroonabadi and M.R. Haghifam, “Generation reliability assessment in power markets using Monte Carlo simulation and soft computing”, Applied Soft Computing, Vol. 11, 8, pp.5292–5298, 2011.
[17] R. N. Allan, Y. A. Jebril, A. Saboury and J. Roman, “ Monte Carlo Simulation Applied to Power System Reliability Evaluation”, 10th Advances in Reliability Technology Symposium, pp. 149-162.
[18] Kevin J. Timko ; Anjan Bose and Paul M. Anderson, “Monte Carlo Simulation of Power System Stability”, IEEE Transactions on Power Apparatus and Systems PER, Vol.3, 10, pp.3453 – 3459, 1983.
[19] Vapnik. V and Lerner, “Pattern recognition using generalized portrait method”, Automation and Remote Control, 24, 774–780, 1963.
[20] C.Cortes and V.Vapnik, “Support Vector Networks: Machine Learning”, Vol. 20, 3, pp. 273-297, 1995.
[21] Mojtaba Mohammadpoor, Abbas Mehdizadeh, and Hava Alizadeh Noghabi, “A Novel Method for Persian Handwritten Digit Recognition using Support Vector Machines”, Majlesi Journal of Electrical Engineering, Vol. 12, 3, 61-65, 2018.
[22] SayedMasoud Hashemi Amroabadi, MohammadReza Ahmadzadeh, and Ali Hekmatnia, “Tumor Detection in Digital Mammogram Based on Support Vector Machine Using Co-occurrence Matrix”, Majlesi Journal of Electrical Engineering, Vol. 3, 2, pp. 7-17, 2009.
[23] L. Moulin, A. daSilva, M. El-Sharkawi, and R. J. Marks, “Support vector machines for transient stability analysis of large-scale power systems”, IEEE Trans. Power Syst. , Vol. 19, 2, pp. 818–825, 2004.
[24] Illias HA, and Zhao Liang W, “Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimization”, PLoS ONE, Vol. 13, 1, e0191366,https://doi.org/10.1371/journal.pone.0191366, 2018.
[25] Sami Ekici, “Support Vector Machines for classification and locating faults on transmission lines”, Applied Soft Computing, Vol.12, 6, pp.1650-1658, 2012.
[26] Shafiullah M, Ijaz M, Abido MA, and Al-Hamouz Z, “Optimized support vector machine & wavelet transform for distribution grid fault location”, 11th IEEE international conference on compatibility, power electronics and power engineering, pp.77–82. doi: 10.1109/CPE.2017.7915148., 2017.
[27] Zendehboudi, Alireza, M. A. Baseer, and R. Saidur. "Application of support vector machine models for forecasting solar and wind energy resources: A review", Journal of Cleaner Production 199 pp. 272-285, 2018
[28] K.R. Sudha, Y.B. Raju, P.V.G.D. Prasad Reddy, “Adaptive Power System Stabilizer using Support Vector Machine” , Journal of Engineering Science and Technology, Vol.2, 3, pp. 442-447, 2010.
[29] Taher Seyed Abbas and Hematti Reza, “Robust decentralized load frequency control using multi variable QFT method in deregulated power systems”, Am J Appl Sci, Vol. 5, 7, pp.:818–28, 2008.
[30] K.R. Sudha and R. Vijaya Santhi, “Robust decentralized load frequency control of interconnected power system with Generation Rate Constraint using Type-2 fuzzy approach”, Electrical Power and Energy Systems, Vol. 33, pp. 699–707, 2011.
[31] B. Anand and A. E. Jeyakumar, “Fuzzy logic based load frequency control of hydro-thermal system with non-linearities”, Int.J.Electrical and Power Engg.Tech, Vol.3, 2, pp.112-118, 2009.
[32] S.C. Tripathy, T.S. Bhatti, C.S. Jha, O.P. Malik, and G.S. Hope, “Sampled data automatic generation control analysis with reheat steam turbines and governor dead band effects”, IEEE Trans. Power Apparatus and Systems, Vol.103, .5, pp.1045-1050, 1984.
[33] B.Y. Rubinstein, “Simulation and the Monte Carlo Method”, New York, Wiley & Sons, 1981.
[34] C. Cortes and V. Vapnik, “Support vector networks. Machine Learning”, Vol. 20, pp. 273-297, 1995.
How to Cite
A, P., & KR, S. (2019). Power System Load Frequency Regulation using Monte-Carlo Parameter Estimation based Support Vector Machine. Majlesi Journal of Energy Management, 6(4), 1-14. Retrieved from http://journals.iaumajlesi.ac.ir/em/index/index.php/em/article/view/345