Frequency Control in a Micro grid by Model Predictive Control Optimized by PSO Optimization Algorithm

  • Mohammad Janali Department of Electrical and Electronic Engineering, Majlesi Branch, Islamic Azad University, Majlesi, Isfahan, Iran
  • Amir Hossein Zaeri Department of Electrical and Electronic Engineering, Shahinshahr Branch, Islamic Azad University, Shahinshahr, Isfahan, Iran
Keywords: Micro grid, frequency control, particle swarm optimization algorithm, Model Predictive Control


In this paper, the predictive control model is designed for controlling the frequency in a micro grid in the island mode with respect to the disturbances entered into the system. One of the important issues in the micro grid is controlling the frequency in the system. In case of turbulence in the micro grid, when the micro grid is connected to the network, and also in island mode, the predictive control model changes the frequency of the system. The proposed micro grid is intended for three areas. In order to improve the efficiency of the system, the particle swarm optimization algorithm is used to determine the controller parameters including prediction horizon, control horizon, and sampling time.


[1] M. N. Ibrahim and M. K. Hussein, “Load Frequency Control for Two-area Multi-Source Interconnected Power System using Intelligent Controllers,” Tikrit J. Eng. Sci., vol. 7589, pp. 12–19, 2018.
[2] R. GOCHHAYAT, “PSO Based Pi Controller for Load Frequency Control of Interconnected Power Systems,” vol. 88, no. 7, pp. 1–40, 2014.
[3] M. Sarkar, A. Dev, and P. Asthana, “Chattering Free Robust Adaptive Integral Higher Order Sliding Mode Control Chattering free robust adaptive integral higher order sliding mode control for load frequency problems in multi-area power systems,” no. July, 2018.
[4] D. Guha, P. Kumar, and S. Banerjee, “Application of backtracking search algorithm in load frequency control of multi-area interconnected power system,” Ain Shams Eng. J., vol. 9, no. 2, pp. 257–276, 2018.
[5] H. Bevrani, F. Habibi, P. Babahajyani, M. Watanabe, and Y. Mitani, “Intelligent frequency control in an AC microgrid: Online PSO-based fuzzy tuning approach,” IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 1935–1944, 2012.
[6] N. K. Kumar and I. E. S. Naidu, “Load Frequency Control for A Multi Area Power System Involving Wind, Hydro and Thermal Plants,” vol. 3, no. 1, pp. 1008–1013, 2014.
[7] S. R. Krishna, P. Singh, and M. S. Das, “Control of load frequency of power system by PID controller using PSO,” Int. J. Recent Dev. Eng. Technol., vol. 5, no. 6, p. 37, 2016.
[8] K. R. Sudha, “Robust type-2 fuzzy c-means load frequency controller for multi area ( geothermal hydrothermal ) interconnected power system with GRC Anand Gondesi , R . Vijaya Santhi and,” vol. 2, no. 3, 2017.
[9] H. A. Yousef, K. Al-kharusi, and M. H. Albadi, “Load Frequency Control of a Multi-Area Power System : An Adaptive Fuzzy Logic Approach,” vol. 29, no. 4, pp. 1822–1830, 2014.
[10] E. S. Ali, “Electrical Power and Energy Systems Load frequency controller design via BAT algorithm for nonlinear interconnected power system Proportional plus Integral the Integral of Square Error,” vol. 77, pp. 166–177, 2016.
[11] S.Mohammadpoor, “Frequency-load control of multi-zone power system including thermal, wind and water power plants,” Natl. Conf. Electr. Eng. Telecommun. Sustain. Dev., 2014.
[12] M. Zribi and M. Alrifai, “Adaptive decentralized load frequency control of multi-area power systems,” vol. 27, pp. 575–583, 2005.
[13] I. Petrovi, K. Vrdoljak, and N. Peri, “Improved Particle Swarm Optimization Based Load Frequency Control In A Single Area Power System,” vol. 80, pp. 514–527, 2010.
[14] E. F. Camacho, Model predictive contro, 2nd ed. 2004.
[15] J. H. Lee, “Model predictive control: Review of the three decades of development,” Int. J. Control. Autom. Syst., vol. 9, no. 3, pp. 415–424, 2011.
[16] K.Eshghi, Algorithm analysis and design of meta-phrasal methods. 2016.
How to Cite
Janali, M., & Zaeri, A. H. (2019). Frequency Control in a Micro grid by Model Predictive Control Optimized by PSO Optimization Algorithm. Majlesi Journal of Mechatronic Systems, 8(4), 33-40. Retrieved from