Artificial Intelligence-Based Optimization of PID Controllers for Two-Area AGC Systems Using Particle Swarm Optimization
Abstract
Automatic Generation Control (AGC) is vital for maintaining constant and reliable power in interconnected systems, and AGC is also responsible for distributing loads between generators optimally as well as maintaining frequency stability, and controlling exchanges over tie lines. This study proposes the use of an AI-based optimisation approach to enhance the performance of AGC in a two-area power system. More specifically, the tuning settings of the Proportional-Integral-Derivative (PID) controller are optimised by employing the AI algorithm called Particle Swarm Optimisation (PSO), which is motivated by the social behaviour of swarms occurring naturally. Two scenarios are evaluated: AGC without PID control in the two areas, and AGC with PSO-optimised PID controllers in both areas. The simulation results obtained from this study demonstrate that the PSO-optimised PID controllers result in a significant reduction in frequency deviations, improve the integral of time-weighted absolute error, and provide better dynamic performance of the system. The study results also identified that AI-based optimisation methods incorporated in AGC design achieve better stability, frequency regulation, and tie-line power control with rapid response, thus preparing an intelligent and resilient operation of the power system.
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References
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3. Ullah, Z., Ullah, K., Diaz-Londono, C., Gruosso, G., Basit, A.: Enhancing Grid Operation with Electric Vehicle Integration in Automatic Generation Control. Energies. 16, 7118 (2023). https://doi.org/10.3390/en16207118.
4. Ghosh, A., Ray, A.K., Nurujjaman, Md., Jamshidi, M.: Voltage and frequency control in conventional and PV integrated power systems by a particle swarm optimized Ziegler–Nichols based PID controller. SN Appl. Sci. 3, 314 (2021). https://doi.org/10.1007/s42452-021-04327-8.
5. Ramakrishna, K.S.S., Sharma, P., Bhatti, T.: Automatic generation control of interconnected power system with diverse sources of power generation. Int. J. Eng. Sci. Technol. 2, 51–65 (2010). https://doi.org/10.4314/ijest.v2i5.60102.
6. Kumar, A., Singh, O.: Recent Strategies for Automatic Generation Control of Power Systems With Diverse Energy Sources. Int. J. Syst. Dyn. Appl. IJSDA. 10, 1–26 (2021). https://doi.org/10.4018/IJSDA.20211001.oa8.
7. Department of Electrical Engineering, F ET, GKV , Haridwar, U.K, India, Dhamanda, A., Dutt, A., Department of Electrical and Electronics Engineering, SGIET, SHIATS, Allahabad, India, Bhardwaj, A.K., Department of Electrical Engineering, SSET, SHIATS, Allahabad, U.P, India: A utomatic G eneration C ontrol in Four Area Interconnected Power System of Thermal Generating Unit through Evolutionary Technique. Int. J. Electr. Eng. Inform. 7, 569–583 (2015). https://doi.org/10.15676/ijeei.2015.7.4.3.
8. Debbarma, S., Saikia, L.C., Sinha, N.: Robust two-degree-of-freedom controller for automatic generation control of multi-area system. Int. J. Electr. Power Energy Syst. 63, 878–886 (2014). https://doi.org/10.1016/j.ijepes.2014.06.053.
9. Farshi, H., Valipour, K.: Hybrid PSO-GA Algorithm for Automatic Generation Control of Multi-Area Power System.
10. Illias, H.A., Nurjannah, N., Mokhlis, H.: Optimization of Automatic Generation Control Performance for Power System of Two-Area based on Genetic Algorithm-Particle Swarm Optimization. In: 2024 IEEE 4th International Conference in Power Engineering Applications (ICPEA). pp. 317–321 (2024). https://doi.org/10.1109/ICPEA60617.2024.10498269.
11. Gozde, H., Taplamacioglu, M.C.: Automatic generation control application with craziness based particle swarm optimization in a thermal power system. Int. J. Electr. Power Energy Syst. 33, 8–16 (2011). https://doi.org/10.1016/j.ijepes.2010.08.010.
12. Illias, H.A., Nurjannah, N., Mokhlis, H.: Optimal Performance of Automatic Generation Control for Two-Area Power System using Particle Swarm Optimization. In: 2021 Innovations in Power and Advanced Computing Technologies (i-PACT). pp. 1–5 (2021). https://doi.org/10.1109/i-PACT52855.2021.9696446.
13. Rahman, A., Saikia, L.C., Sinha, N.: Automatic generation control of an interconnected two-area hybrid thermal system considering dish-stirling solar thermal and wind turbine system. Renew. Energy. 105, 41–54 (2017). https://doi.org/10.1016/j.renene.2016.12.048.
14. Ullah, K., Basit, A., Ullah, Z., Aslam, S., Herodotou, H.: Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview. Energies. 14, 2376 (2021). https://doi.org/10.3390/en14092376.
15. Oshnoei, A., Khezri, R., Muyeen, S.M., Blaabjerg, F.: On the Contribution of Wind Farms in Automatic Generation Control: Review and New Control Approach. Appl. Sci. 8, 1848 (2018). https://doi.org/10.3390/app8101848.
16. Garg, K., Kaur, J.: Particle Swarm Optimization Based Automatic Generation Control of Two Area Interconnected Power System.
17. Kumari, S., Pathak, P.K.: A State-of-the-Art Review on Recent Load Frequency Control Architectures of Various Power System Configurations. Electr. Power Compon. Syst. 52, 722–765 (2024). https://doi.org/10.1080/15325008.2023.2234373.
18. Kumari, S., Pathak, P.K.: A State-of-the-Art Review on Recent Load Frequency Control Architectures of Various Power System Configurations. Electr. Power Compon. Syst. 52, 722–765 (2024). https://doi.org/10.1080/15325008.2023.2234373.
19. Panda, S., Yegireddy, N.K.: Automatic generation control of multi-area power system using multi-objective non-dominated sorting genetic algorithm-II. Int. J. Electr. Power Energy Syst. 53, 54–63 (2013). https://doi.org/10.1016/j.ijepes.2013.04.003.
20. Hakimuddin, N., Nasiruddin, I., Bhatti, T.S., Arya, Y.: Optimal Automatic Generation Control with Hydro, Thermal, Gas, and Wind Power Plants in 2-Area Interconnected Power System. Electr. Power Compon. Syst. 48, 558–571 (2020). https://doi.org/10.1080/15325008.2020.1793829.
21. Guha, D., Roy, P.K., Banerjee, S.: Load frequency control of interconnected power system using grey wolf optimization. Swarm Evol. Comput. 27, 97–115 (2016). https://doi.org/10.1016/j.swevo.2015.10.004.
22. Load frequency control of a two-area power system with renewable energy sources using brown bear optimization technique | Electrical Engineering, https://link.springer.com/article/10.1007/s00202-023-02143-4, last accessed 2025/07/07.
23. Tabak, A., İlhan, İ.: An effective method based on simulated annealing for automatic generation control of power systems. Appl. Soft Comput. 126, 109277 (2022). https://doi.org/10.1016/j.asoc.2022.109277.
24. Application of Fractional Order-PID Control Scheme in Automatic Generation Control of a Deregulated Power System in the Presence of SMES Unit, https://www.mdpi.com/2227-7390/10/3/521, last accessed 2025/07/07.
25. Research on Automatic Generation Control with Wind Power Participation Based on Predictive Optimal 2-Degree-of-Freedom PID Strategy for Multi-area Interconnected Power System, https://www.mdpi.com/1996-1073/11/12/3325, last accessed 2025/07/07.
26. Optimal automatic generation control of two-area power systems with energy storage units under deregulated environment | Journal of Renewable and Sustainable Energy | AIP Publishing, https://pubs.aip.org/aip/jrse/article/9/6/064105/286317, last accessed 2025/07/07.
27. Bennett, S.: Development of the PID controller. IEEE Control Syst. Mag. 13, 58–62 (1993). https://doi.org/10.1109/37.248006.
28. PID Control: New Identification and Design Methods | SpringerLink, https://link.springer.com/book/10.1007/1-84628-148-2, last accessed 2025/07/07.
29. Automated Generation Control of Multiple‐Area Electrical System with an Availability‐Based Tariff Pricing Scheme Regulated by Whale Optimized Fuzzy PID Controller - Hamodat - 2021 - International Journal of Photoenergy - Wiley Online Library, https://onlinelibrary.wiley.com/doi/full/10.1155/2021/5596527, last accessed 2025/07/07.
30. A review of particle swarm optimization. Part I: background and development | Natural Computing, https://link.springer.com/article/10.1007/s11047-007-9049-5, last accessed 2025/07/07.
31. Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review | Archives of Computational Methods in Engineering, https://link.springer.com/article/10.1007/S11831-021-09694-4, last accessed 2025/07/07.
32. Shouran, M., Alsseid, A.: Particle Swarm Optimization Algorithm-Tuned Fuzzy Cascade Fractional Order PI-Fractional Order PD for Frequency Regulation of Dual-Area Power System. Processes. 10, 477 (2022). https://doi.org/10.3390/pr10030477.
33. Shami, T.M., El-Saleh, A.A., Alswaitti, M., Al-Tashi, Q., Summakieh, M.A., Mirjalili, S.: Particle Swarm Optimization: A Comprehensive Survey. IEEE Access. 10, 10031–10061 (2022). https://doi.org/10.1109/ACCESS.2022.3142859.
34. 25 Years of Particle Swarm Optimization: Flourishing Voyage of Two Decades | Archives of Computational Methods in Engineering, https://link.springer.com/article/10.1007/s11831-022-09849-x, last accessed 2025/07/07.
35. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks. pp. 1942–1948 vol.4 (1995). https://doi.org/10.1109/ICNN.1995.488968.
Published
2025-10-08
Section
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