Machinery & Energetics

Application of programming methods for control of fluctuations in electric power systems and networks

Rysbek Abdyldaev, Nurkul Murzakulov, Almagul Abdullaeva, Tolgonay Dzholdosheva, Muhammadsadyk Yslamov
Download article
Abstract

The purpose of this study was to increase the stability of electric power systems to fluctuations through the development and verification of control algorithms based on advanced programming and modelling methods. To solve this problem, algorithms based on proportional-integral-derivative (PID) regulators (including those optimised by evolutionary methods) and artificial neural networks were created and tested. Tests have shown that the classic PID controller can reduce the amplitude of vibrations by an average of 20-30% compared to an uncontrolled system, however, it required fine manual adjustment and was inferior in response speed to sudden load changes. Optimised PID controllers based on genetic algorithms (GA), particle swarm optimisation (PSO), and the firefly algorithm (FA) helped to further reduce the oscillation amplitude (up to 25%, 33%, and 45%, respectively) and accelerated system stabilisation, which significantly increased the reliability of power supply. Of particular interest were neural networks that provided the highest adaptability to changing conditions and allowed predicting changes in key parameters (frequency and voltage) with an error of 2-3% in the Mean Absolute Percentage Error (MAPE) indicator. As a result, the network responded to disturbances in a timely manner, reduced the frequency deviation to 0.09 Hz, and reduced the transition time to 3.5 seconds in case of sudden load changes. Thus, the neural network approach has demonstrated the best results in both vibration damping and overall stability of the system. The conducted pilot tests in conditions of intelligent power systems have confirmed the feasibility of integrating the developed algorithms into existing monitoring and control infrastructures. With sufficient computing power and an advanced telemetry system, all the proposed solutions were easily scalable and provided reliable vibration damping even in conditions of active integration of renewable energy sources. Thus, the results of the study confirmed the effectiveness of the developed oscillation control methods and their prospects for further widespread implementation in intelligent power systems

Keywords

integration of renewable energy sources, stability of energy supply, network management algorithms, stability of energy systems, simulation models of power grids, optimisation of management processes, energy security

Suggested citation
Abdyldaev, R., Murzakulov, N., Abdullaeva, A., Dzholdosheva, T., & Yslamov, M. (2025). Application of programming methods for control of fluctuations in electric power systems and networks. Machinery & Energetics, 16(2), 70-82. https://doi.org/10.31548/machinery/2.2025.70
References
  1. Agarwal, P.K., & Kumar, C. (2019). Oscillation detection and mitigation using synchrophasor technology in the Indian power grid. In S. Nuthalapati (Ed.), Power system grid operation using synchrophasor technology (pp. 195-216). Cham: Springer. doi: 10.1007/978-3-319-89378-5_8.
  2. Aleikish, K., & Øyvang, T. (2023). Real-time identification of electromechanical oscillations via deep learning enhanced dynamic mode decomposition. In Proceedings of the power & energy society general meeting (pp. 1-5). Orlando: IEEE. doi: 10.1109/PESGM52003.2023.10252195.
  3. Bekturov, A., & Chymyrov, A. (2020). Accuracy assessment of free global digital terrain models (DTM) and digital terrain models DTM based on soviet topographic maps for road planning in Kyrgyzstan. Herald of KRSU, 20(12), 169-177.
  4. Beridze, Т., Kasatkina, І., & Boiko, S. (2023). Study of the criteria for assessment of the quality indicators of electricity supply of industrial enterprises. Journal of Kryvyi Rih National University, 21(2), 121-127. doi: 10.31721/2306-5451-2023-1-57-121-127.
  5. Costanzo, L., Rubino, G., Rubino, L., & Vitelli, M. (2024). PFC control signal driven MPPT technique for grid-connected PV systems. IEEE Transactions on Power Electronics, 39(8), 10368-10379. doi: 10.1109/TPEL.2024.3393294.
  6. Dai, S., & Zhang, X.-P. (2022). Advanced identification methods for power system oscillations based on measurements. In Proceedings of the 16th international conference on compatibility, power electronics, and power engineering (pp. 1-6). Birmingham: IEEE. doi: 10.1109/CPE-POWERENG54966.2022.9880879.
  7. Devarapalli, R., Sinha, N.K., & Márquez, F.P. (2022). A review on the computational methods of power system stabilizer for damping power network oscillations. Archives of Computational Methods in Engineering, 29, 3713-3739. doi: 10.1007/s11831-022-09712-z.
  8. Fu, S., Sun, Y., Liu, Z., Hou, X., Han, H., & Su, M. (2022). Power oscillation suppression in multi-VSG grid with adaptive virtual inertia. International Journal of Electrical Power & Energy Systems, 135, article number 107472. doi: 10.1016/j.ijepes.2021.107472.
  9. Han, W., & Stanković, A.M. (2022). Model-predictive control design for power system oscillation damping via excitation – a data-driven approach. IEEE Transactions on Power Systems, 38(2), 1176-1188. doi: 10.1109/TPWRS.2022.3177561.
  10. Islam, H., Suresh, K., & Senthilmurugan, S. (2020). Power analysis and stabilization techniques for smart grids. International Energy Journal, 20(2), 265-270.
  11. Liu, Y., & Liu, C. (2024). Intelligent control strategy for optimal utilization of energy in smart grid. Renewable Energy and Power Quality Journal, 22(5), 107-117. doi: 10.52152/4035.
  12. Lu, S., Wu, M., Liu, J., Wang, H., Yang, L., Liu, Q., Yang, J., & Sui, Y. (2024). Low‐frequency oscillation damping strategy for power system based on virtual dual‐input power system stabilizer. IET Renewable Power Generation, 18(1), 4439-4452. doi: 10.1049/rpg2.13174.
  13. Mehta, K., Mingaleva, E., Zörner, W., Degembaeva, N., & Baibagyshov, E. (2022). Comprehensive analysis of the energy legislative framework of Kyrgyzstan: Investigation to develop a roadmap of Kyrgyz renewable energy sector. Cleaner Energy Systems, 2, article number 100013. doi: 10.1016/j.cles.2022.100013.
  14. Orynbayev, S., Tokmoldayev, A., Abdlakhatova, N., Zhanpeisova, A., & Tumanov, I. (2024). Improving power plant technology to increase energy efficiency of autonomous consumers using geothermal sources. Energy Harvesting and Systems, 11(1), article number 20230082. doi: 10.1515/ehs-2023-0082.
  15. Panov, A., & Tymchuk, S. (2023). Model for regulating electricity quality indicators in 0.4-10 kV distribution networks. Bulletin of Cherkasy State Technological University, 28(2), 13-23. doi: 10.24025/2306-4412.2.2023.275897.
  16. Peres, W., & Nascimento da Costa, J.N. (2020). Comparing strategies to damp electromechanical oscillations through STATCOM with multi-band controller. ISA Transactions, 107, 256-269. doi: 10.1016/j.isatra.2020.08.005.
  17. Porawagamage, G., Dharmapala, K., Chaves, J.S., Villegas, D., & Rajapakse, A. (2024). A review of machine learning applications in power system protection and emergency control: Opportunities, challenges, and future directions. Frontiers in Smart Grids, 3, article number 1371153. doi: 10.3389/frsgr.2024.1371153.
  18. Robin, M., Renedo, J., Gonzalez-Torres, J.C., Garcia-Cerrada, A., Benchaib, A., & Garcia-Gonzalez, P. (2023). An algorithm for DC segmentation of AC power systems to mitigate electromechanical oscillations. ARXIV. doi: 10.48550/arXiv.2302.09931.
  19. Sabo, A., Odoh, T.E., Shahinzadeh, H., Wahab, N.I., Moazzami, M., & Gharehpetian, G.B. (2024). Enhancing power oscillation control: Comparative analysis of damping controllers and hybrid computational intelligence methods for power system stabilization. In Proceedings of the 20th CSI international symposium on artificial intelligence and signal processing (pp. 1-6). Babol: IEEE. doi: 10.1109/AISP61396.2024.10475219.
  20. Sarkar, D., & Prakash, T. (2022). A neural network approach to design power system stabilizer for damping power oscillations. In Proceedings of the 22nd national power systems conference (pp. 837-842). New Delhi: IEEE. doi: 10.1109/NPSC57038.2022.10070020.
  21. State Standard of Ukraine No. IEC 60038:2015. (2016). Reference voltage according to the IES. Retrieved from https://online.budstandart.com/ua/catalog/doc-page.html?id_doc=66584.
  22. Sukhodub, I., & Serdechnyi, P. (2024). Analysis of scenarios for increasing the level of energy efficiency of public buildings with integration of RES. Technologies and Engineering, 25(2), 44-56. doi: 10.30857/2786-5371.2024.2.5.
  23. Sundaramurthy, A., Ramasamy, K., Velusamy, D., & Vaithiyalingam, C. (2024). Enhancing smart grid stability: Data-driven predictive modeling in distribution systems. International Journal of Electrical and Electronics Research, 12(2), 623-631. doi: 10.37391/IJEER.120239.
  24. Titz, M., Kaiser, F., Kruse, J., & Witthaut, D. (2022). Predicting dynamic stability from static features in power grid models using machine learning. ARXIV. doi: 10.48550/arXiv.2210.09266.
  25. Vanfretti, L., & Bombois, X. (2024). Power system oscillation monitoring and damping control re-design under ambient conditions and multiple operating points. doi: 10.13140/RG.2.2.22504.28166.
  26. Vives, J. (2022). Vibration analysis for fault detection in wind turbines using machine learning techniques. Advances in Computational Intelligence, 2, article number 15. doi: 10.1007/s43674-021-00029-1.
  27. Wang, Y., Xia, Z., & Xie, R. (2022). Vibration control technology of ship transmission based on machine learning. In Proceedings of the international conference on artificial intelligence in everything (pp. 580-584). Lefkosa: IEEE. doi: 10.1109/AIE57029.2022.00115.
  28. Witharama, W.M., Bandara, K.M., Azeez, M.I., Bandara, K., Logeeshan, V., & Wanigasekara, C. (2024). Advanced genetic algorithm for optimal microgrid scheduling considering solar and load forecasting, battery degradation, and demand response dynamics. IEEE Access, 12, 83269-83284. doi: 10.1109/ACCESS.2024.3412914.
  29. Yang, J., Wang, S., Li, Z., Tian, X., Liu, B., & Liu, X. (2022). Oscillation suppression method based on power control optimization of the wind farms. In Proceedings of the international power electronics and application conference and exposition (pp. 367-372). Guangzhou: IEEE. doi: 10.1109/PEAC56338.2022.9959449.
  30. Yang, Y., Zhao, H., Li, J., Zhou, H., Gu, H., Xu, D., Li, Y., & Liu, K. (2024). Research on broadband oscillation suppression strategy in power system based on genetic algorithm. Scalable Computing: Practice and Experience, 25(6), 5507-5551. doi: 10.12694/scpe.v25i6.3344.
  31. Yusuf Ibrahim, S., Ilyas, M.A., Li, Q., Khan, A., & Othman. M.B. (2023). Machine learning motor vibration monitoring system with a service estimation date. In Advances in machinery, materials science and engineering application IX (pp. 1098-1105). London: IOS Press. doi: 10.3233/ATDE230583.
  32. Zhang, R. (2024). Research on vibration suppression algorithm based on continuous input shaping. In Proceedings of the 3rd international conference on robotics, artificial intelligence and intelligent control (pp. 308-312). Mianyang: IEEE. doi: 10.1109/RAIIC61787.2024.10671139.
  33. Zhang, S., Srividya, K., Kakaravada, I., Karras, D.A., Jagota, V., Hasan, I., & Rahmani, A.W. (2022). A global optimization algorithm for intelligent electromechanical control system with improved filling function. Scientific Programming, 2022(1), article number 3361027. doi: 10.1155/2022/3361027.
  34. Zhanpeisova, A., Tleshova, A., Abildaeva, A., Uzbekova, D., & Abdlakhatova, N. (2024). Analysing efficiency and economic aspects in organic Rankine cycle systems. Polityka Energetyczna, 27(3), 87-108. doi: 10.33223/epj/188495.
  35. Zhu, L., Yu, W., Jiang, Z., Zhang, C., Zhao, Y., & Dong, J. (2021). A comprehensive method to mitigate forced oscillations in large interconnected power grids. IEEE Access, 9, 22503-22515. doi: 10.1109/ACCESS.2021.3056123.