PID-Based Load Frequency Control for Smart Grid Applications
DOI:
https://doi.org/10.59535/faase.v2i2.297Keywords:
Smart Grid, Automatic Generation Control (AGC), Load Frequency Control (LFC), Tie-line Power Flow Control, PID ControllerAbstract
Ensuring stable frequency and power balance in modern power systems is essential, particularly within smart grids and advanced multi-area configurations. This study evaluates an enhanced control strategy employing Proportional-Integral-Derivative (PID) controllers for load frequency control in a three-area grid system, which represents scenarios found in power networks with dynamic loads and inter-area power transfers. Using MATLAB/Simulink, a three-area model was developed to simulate the application of PID controllers within the secondary control loop of Automatic Generation Control (AGC). The simulation results indicated significant improvements in frequency regulation and tie-line power variations, demonstrating the efficacy of PID controllers in bolstering stability and performance in complex, interconnected systems.
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L. A. Pozo, D. Papavasiliou, A. Molzahn, D. K. Kazempour, J. Conejo, (2023). ‘Power systems optimization under uncertainty: A review of methods and applications’. Electric Power Systems Research, 214, 108725.
G. Mandic, Nasiri, A. Muljadi, E. Oyague, (2012). Active torque control for gearbox load reduction in a variable-speed wind turbine. IEEE Transactions on Industry Applications, 48(6), 2424-2432.
A. N. Abdullah, & M. H. Ali, (2020). Direct torque control of IM using PID controller. International Journal of Electrical and Computer Engineering (IJECE), 10(1), 617-625.
Z. Li, G. Chen, & C. Zhang, (2022). Research on Position and Torque Loading System with Velocity-Sensitive and Adaptive Robust Control. Sensors, 22(4), 1329.
C. Wang, Jiao, Z. Wu, S. Shang, (2014). Nonlinear adaptive torque control of electro-hydraulic load system with external active motion disturbance. Mechatronics, 24(1), 32-40.
E. Osundina, P. D. Onwe, E. C. Eluebo, E. E., Awominure, A. A., & Oyetayo, T. T. (2024). Neurofuzzified Analysis on Torque Control of a Squirrel Cage Induction Machine. Journal Electrical and Computer Experiences, 2(1), 8-13.
G. Kim, You, S. Lee, S. Shin, D. Kim, (2023). Robust Nonlinear Torque Control Using Steering Wheel Torque Model for Electric Power Steering System. IEEE Transactions on Vehicular Technology.
X. Sunp, Zhu, Y., Cai, Y. Yao, M. Sun, Y. Lei, (2023). Optimized-sector-based model predictive torque control with sliding mode controller for switched reluctance motor. IEEE Transactions on Energy Conversion.
Y. Tian, Zhang, Y. Xiao, X. Yildirim, T. (2024). Weighting factors design in model predictive direct torque control based on cascaded neural network. Asian journal of control, 26(3), 1323-1338.
J. Croonen, Deraes, A. L. J., Beckers, J. Devesse, W. Hegazy, O. Verrelst, (2024). Active Torque Control for Speed Ripple Elimination: A Mechanical Perspective. Machines, 12(4), 222.
N. Ruiz, Cobelo, I. Oyarzabal, (2009). A direct load control model for virtual power plant management. IEEE Transactions on Power Systems, 24(2), 959-966.
H. Liu, W. Niu, Y. Guo, (2024). Direct torque control for PMSM based on the RBFNN surrogate model of electromagnetic torque and stator flux linkage. Control Engineering Practice, 148, 105943.
G. Kamalapur, & M. S. Aspalli, (2023). Direct torque control and dynamic performance of induction motor using fractional order fuzzy logic controller. International Journal of Electrical & Computer Engineering (2088-8708), 13(4).
T. E. Sola, Chiu, H. J. Liu, & A. N. Rahman, (2022). Improved Direct Torque Control of Induction Motor for Torque Ripple Minimization. IEEE Access, 10, 131980-131995.
M. Elgbaily Anayi, F. Packianather, (2022). Performance improvement-based torque ripple minimization for direct torque control drive fed induction motor using fuzzy logic control. In Control, Instrumentation and Mechatronics: Theory and Practice (pp. 416-428). Singapore: Springer Nature Singapore.
S., Mencou, Yakhlef, M. B., & Tazi, E. B. (2022, March). Advanced torque and speed control techniques for induction motor drives: A review. In 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) (pp. 1-9). IEEE
A., Angelopoulos, Candes, E., & Tibshirani, R. J. (2024). Conformal PID control for time series prediction. Advances in Neural Information Processing Systems, 36.
M., Abdillah, & Setiadi, H. (2023). Area control error enhancement of two-area power system using hybrid intelligence optimal controller. Indonesian Journal of Electrical Engineering and Computer Science, 31(3), 1258-1265.
L. A. C. Ahakonye, Nwakanma, C. I., Lee, J. M., & Kim, D. S. (2024). Low computational cost convolutional neural network for smart grid frequency stability prediction. Internet of Things, 25, 101086.
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Copyright (c) 2024 Emmanuel Mayowa Osundina, Moyosoluwalorun Odunayo Sonola, Olumide Cornelius Osungbohun, Tim Peter Oritsetimeyin, Peter Daffin Onwe, Eluebo Emmanuel Chuka, Olumide Ifedapo Oluwole , Gregory Okwor Odama, Sazgar Abdualaziz Wali, Yifan Hu, Duberney Florez
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