Real-time control action formation for predicting post-accident electrical modes considering permissible stability margins
DOI:
https://doi.org/10.15587/1729-4061.2024.307676Keywords:
power system stability, stability margin, emergency control systems, control actionsAbstract
The object of the research is an emergency control system for ensuring the stability of electric power systems (EPS) in case of emergency unbalances. The relevance of the problem of ensuring EPS stability is due to the need to improve the efficiency of emergency control to reduce the risk of system accidents with significant damage. To solve this problem, we propose an algorithm for selecting the volume of control actions based on the principles of adaptive control for predicting the post-emergency mode with an acceptable stability margin. The algorithm of forming the volume of control actions is based on the dependence of the value of control actions on the value of stability reserves estimation by the value of the Jacobi determinant. To build this dependence, the algorithm of searching for the limiting mode by the trajectory of change in the equilibrium position of the steady state of the system from the initial to the limiting one is used. In contrast to the existing algorithms, the proposed algorithm establishes a functional dependence of the control value on the current parameters of the regime or the stability margin, which increases the efficiency of calculations for selecting control actions. Realization of the proposed algorithm is carried out on the basis of the functional scheme according to the data of the vector measurement system.
The advantage and novelty of the proposed algorithm is the possibility of eliminating the disadvantages of existing systems of mode automation, the main of which are:
– the necessity to perform numerous variant calculations for selecting the volume of control actions;
– possible excessive volume of control actions in case of a mismatch of the actual mode with the calculated one
References
- Kundur, P. (2007). Power system stability. Power system stability and control.
- Kundur, P., Malik, P. (2022). Power System Stability and Control. McGraw-Hill. Available at: https://www.accessengineeringlibrary.com/content/book/9781260473544
- Cutsem, T., Vournas, C. (1998). Voltage Stability of Electric Power Systems. Springer US. https://doi.org/10.1007/978-0-387-75536-6
- Gluskin, I. Z., Iofiev, B. I. (2009). History of development of emergency control automation. J. Relayshchik, 2.
- Wang, H., Zhang, B., Hao, Z. (2015). Response Based Emergency Control System for Power System Transient Stability. Energies, 8 (12), 13508–13520. https://doi.org/10.3390/en81212381
- Tang, C., Dong, S., Ren, X., Yin, L., Ju, L. (2018). Improved Jacobi Pretreatment Method for Solving Iterative Power Flow Calculation. Automation of Electric Power Systems, 42 (12), 81–86.
- Gluskin, I. Z., Vasiliev, A. N., Melnikov, P. V., Bogachenko, D. D., Efremova, I. Yu. (2015). Issues of overload fixation in multi-machine scheme of power system. Eurasian Scientific Journal, 11.
- Machowski, J., Lubosny, Z., Bialek, J. W., Bumby, J. R. (2020). Power system dynamics: stability and control. John Wiley & Sons, 896.
- Pourdaryaei, A., Shahriari, A., Mohammadi, M., Aghamohammadi, M. R., Karimi, M., Kauhaniemi, K. (2023). A New Approach for Long-Term Stability Estimation Based on Voltage Profile Assessment for a Power Grid. Energies, 16 (5), 2508. https://doi.org/10.3390/en16052508
- Hoseinzadeh, B., Leth Bak, C. (2018). Centralized coordination of emergency control and protection system using online outage sensitivity index. Electric Power Systems Research, 163, 413–422. https://doi.org/10.1016/j.epsr.2018.07.016
- Efremova, I. U., Glouskin, I. Z. (2017). Development of an adaptive fault detector structural scheme of automatic stability control system. Vestnik IGEU, 6, 15–24. https://doi.org/10.17588/2072-2672.2017.6.015-024
- Gunin, A., Tokhtibakiev, K., Saukhimov, A., Bektimirov, A., Didorenko, E. (2023). Improving the efficiency of mode automation using synchrophasor measurements to identify stability disturbance. Eastern-European Journal of Enterprise Technologies, 2 (8 (122)), 18–26. https://doi.org/10.15587/1729-4061.2023.275515
- Yang, H., Zhang, W., Shi, F., Xie, J., Ju, W. (2019). PMU-based model-free method for transient instability prediction and emergency generator-shedding control. International Journal of Electrical Power & Energy Systems, 105, 381–393. https://doi.org/10.1016/j.ijepes.2018.08.031
- Gupta, A., Gurrala, G., Sastry, P. S. (2019). An Online Power System Stability Monitoring System Using Convolutional Neural Networks. IEEE Transactions on Power Systems, 34 (2), 864–872. https://doi.org/10.1109/tpwrs.2018.2872505
- Bento, M. E. C. (2021). Monitoring of the power system load margin based on a machine learning technique. Electrical Engineering, 104 (1), 249–258. https://doi.org/10.1007/s00202-021-01274-w
- Bento, M. E. C. (2022). A method for monitoring the load margin of power systems under load growth variations. Sustainable Energy, Grids and Networks, 30, 100677. https://doi.org/10.1016/j.segan.2022.100677
- Azman, S. K., Isbeih, Y. J., Moursi, M. S. E., Elbassioni, K. (2020). A Unified Online Deep Learning Prediction Model for Small Signal and Transient Stability. IEEE Transactions on Power Systems, 35 (6), 4585–4598. https://doi.org/10.1109/tpwrs.2020.2999102
- Altukhova, M. K., Lyulina, M. A., Ryndina, I. E., Chudny, V. S., Ivanova, E. A., Chilibev, A. G. (2023). Computational Methods for Electric Power Systems Marginal Steady-State Modes and Algorithms of Implementation. 2023 Seminar on Industrial Electronic Devices and Systems (IEDS). https://doi.org/10.1109/ieds60447.2023.10425966
- Altukhova, M. K., Lyulina, M. A., Ryndina, I. E., Chudny, V. S., Ivanova, E. A., Pershikov, G. A. (2024). Methodology for Identifying Sensing Elements in Electric Power System Using Marginal Mode Equations. 2024 Conference of Young Researchers in Electrical and Electronic Engineering (ElCon). https://doi.org/10.1109/elcon61730.2024.10468081
- Guk, O. M., Odintsov, M. V., Sevastyanova, A. V., Smolovik, S. V. (2012). Investigations of the possibility of using the numerical value of the determinant of the Jacobi matrix for analysing the static stability of power systems. Problems of Power Engineering, 3-4.
- Dusabimana, E., Yoon, S.-G. (2020). A Survey on the Micro-Phasor Measurement Unit in Distribution Networks. Electronics, 9 (2), 305. https://doi.org/10.3390/electronics9020305
- Boussadia, F., Belkhiat, S. (2021). A New Adaptive Underfrequency Load Shedding Scheme to Improve Frequency Stability in Electric Power System. Journal Européen Des Systèmes Automatisés, 54 (2), 263–271. https://doi.org/10.18280/jesa.540208
- Li, H., Ma, Z., Weng, Y. (2022). A Transfer Learning Framework for Power System Event Identification. IEEE Transactions on Power Systems, 37 (6), 4424–4435. https://doi.org/10.1109/tpwrs.2022.3153445
- Hong, Q., Ji, L., Blair, S. M., Tzelepis, D., Karimi, M., Terzija, V., Booth, C. D. (2022). A New Load Shedding Scheme With Consideration of Distributed Energy Resources’ Active Power Ramping Capability. IEEE Transactions on Power Systems, 37 (1), 81–93. https://doi.org/10.1109/tpwrs.2021.3090268
- Khrushchev, Yu. V. (2005). Methods of calculation of stability of power systems. Tomsk, 176.
- Pawar, P. S., Mishra, D. R., Dumka, P. (2022). Solving first order ordinary differential equations using least square method: a comparative study. International Journal of Innovative Science and Research Technology (IJISRT), 7 (3), 857–864. https://doi.org/10.5281/zenodo.6418458
- Kenessov, Y., Tokhtibakiev, K., Saukhimov, A., Vassilyev, D., Gunin, A., Iliyasov, A. (2024). Construction of a recurrent neural network-based electrical load forecasting model for a 110 kV substation: a case study in the Western Region of The Republic of Kazakhstan. Energy-Saving Technologies and Equipment, 2 (8 (128)), 6–15. https://doi.org/10.15587/1729-4061.2024.299192
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Copyright (c) 2024 Karmel Tokhtibakiev, Alexandr Gunin, Yerlan Kenessov, Daniil Vassilyev, Anur Bektimirov
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