Learning-based Fractional Order PID Controller for Load Frequency Control of Distributed Energy Resources Including PV and Wind Turbine Generator
DOI:
https://doi.org/10.13052/dgaej2156-3306.3762Keywords:
Microgrid, Frequency Control, Renewable resourceAbstract
Due to the ever-increasing penetration of renewable resources, Frequency control of microgrids has recently been received special consideration from researchers. The continual supply of load consumption is the major issue of standalone microgrids due to the high penetration of renewable resources. Furthermore, microgrids suffer from low inertia against load changes due to their small size and unpredictable load interruption. In addition to the above-mentioned issues, the uncertain and intermittent behaviors of renewable resources cause problems to keep the balance between load and generation sides. Hence, it is very important to consider novel control methods for keeping balance and consequently control of frequency deviation. In this research, a novel learning-based fractional-order controller is proposed to control the frequency of microgrids including micro-turbines, photovoltaic panels, and wind turbines in order to increase system stability and reduce frequency fluctuation time. The efficiency of this controller has been compared with conventional methods in the simulation and result section.
Downloads
References
A. Qazi et al., “Towards sustainable energy: a systematic review of renewable energy sources, technologies, and public opinions,” IEEE Access, vol. 7, pp. 63837–63851, 2019.
G. Notton et al., “Intermittent and stochastic character of renewable energy sources: Consequences, cost of intermittence and benefit of forecasting,” Renewable and sustainable energy reviews, vol. 87, pp. 96–105, 2018.
M. Babaei, E. Azizi, M. T. H. Beheshti, and M. Hadian, “Data-Driven load management of stand-alone residential buildings including renewable resources, energy storage system, and electric vehicle,” Journal of Energy Storage, vol. 28, p. 101221, 2020.
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 transactions on smart grid, vol. 3, no. 4, pp. 1935–1944, 2012.
H. Bevrani, H. Golpîra, A. R. Messina, N. Hatziargyriou, F. Milano, and T. Ise, “Power system frequency control: An updated review of current solutions and new challenges,” Electric Power Systems Research, vol. 194, p. 107114, 2021.
A. Kumar and G. Shankar, “Load frequency control assessment of tidal power plant and capacitive energy storage systems supported microgrid,” IET Generation, Transmission & Distribution, vol. 14, no. 7, pp. 1279–1291, 2020.
Y. Hirase, Y. Ohara, and H. Bevrani, “Virtual synchronous generator based frequency control in interconnected microgrids,” Energy Reports, vol. 6, pp. 97–103, 2020.
V. Gholamrezaie, M. G. Dozein, H. Monsef, and B. Wu, “An optimal frequency control method through a dynamic load frequency control (LFC) model incorporating wind farm,” IEEE Systems Journal, vol. 12, no. 1, pp. 392–401, 2017.
C. T. Pan and C. M. Liaw, “An adaptive controller for power system load-frequency control,” IEEE Transactions on Power Systems, vol. 4, no. 1, pp. 122–128, 1989.
J. Talaq and F. Al-Basri, “Adaptive fuzzy gain scheduling for load frequency control,” IEEE Transactions on power systems, vol. 14, no. 1, pp. 145-150, 1999.
Q. P. Ha and H. Trinh, “A variable structure-based controller with fuzzy tuning for load-frequency control,” International Journal of power and energy systems, vol. 20, no. 3, pp. 146–154, 2000.
D. Das, M. L. Kothari, D. P. Kothari, and J. Nanda, “Variable structure control strategy to automatic generation control of interconnected reheat thermal system,” 1991, vol. 138: IET, 6 ed., pp. 579–585.
L. C. Saikia, “AGC of a three area thermal system using MLPNN controller: A preliminary study,” 2012: IEEE, pp. 1–4.
K. Sabahi and M. Teshnehlab, “Recurrent fuzzy neural network by using feedback error learning approaches for LFC in interconnected power system,” Energy Conversion and Management, vol. 50, no. 4, pp. 938–946, 2009.
C. S. Chang and W. Fu, “Area load frequency control using fuzzy gain scheduling of PI controllers,” Electric Power Systems Research, vol. 42, no. 2, pp. 145–152, 1997.
A. Fathy, A. M. Kassem, and A. Y. Abdelaziz, “Optimal design of fuzzy PID controller for deregulated LFC of multi-area power system via mine blast algorithm,” Neural Computing and Applications, vol. 32, no. 9, pp. 4531–4551, 2020.
H. Saadat, Power system analysis. McGraw-hill, 1999.
H. Bevrani, Robust power system frequency control. Springer.
E. Anbarasu and A. R. Basha, “An improved power conditioning system for grid integration of solar power using ANFIS based FOPID controller,” Microprocessors and Microsystems, vol. 74, p. 103030, 2020.
K. Premkumar, B. V. Manikandan, and C. A. Kumar, “Antlion algorithm optimized fuzzy PID supervised on-line recurrent fuzzy neural network based controller for brushless DC motor,” Electric Power Components and Systems, vol. 45, no. 20, pp. 2304–2317, 2017.
