Allocation of Phasor Measurement Units in Smart Grids Considering the Effect of Switching Substations
Keywords:
phasor measurement units, smart grid, observability, state estimation, TVACPSOAbstract
Nowadays, smart power systems have undergone many changes due to economic issues and the application of novel technologies, among which are privatization and topics related to the electricity market. The effect of such changes in the structure of the power system is the introduction of new topics such as management, control, and monitoring of smart power systems, which require having accurate information on the values of current and voltage (or active and reactive power) at different points of the system. State estimation is a suitable technique for accessing smart grid information, which is based on the mathematical relationship between system state variables and measurements obtained from the system. If the state estimation relations of a system can be solved, that system is observable. If there is a measuring device in all the buses of a smart power system, it is completely observable, but this (having a meter in all buses of a system) is economically unacceptable. Therefore, it is necessary to determine suitable places for measurement for each system so that the estimation equations can be solved. In various articles, allocation of phasor measurement units (PMUs) has been carried out for state estimation in power system, but due to the smartness of power networks, the use of such equipment has been welcomed. Therefore, this study deals with the allocation of network parameters measurement tools in the form of phasors, considering the effect of switching substations in the grid and providing appropriate reliability. So far, many methods, including mathematics and intelligent methods, have been proposed for the allocation of PMUs. Obtaining accurate results and high convergence speed are the two main bottlenecks of the methods used in this regard. This research adopts classic particle swarm optimization (PSO) algorithms and a more advanced version of it that has high convergence and speed called time-varying acceleration coefficients PSO (TVACPSO) to place these devices.
References
[1] Gamm, A. Z., Kolosok, I. N., Glazunova, A. M., & Korkina, E. S. (2008, April). PMU placement criteria for EPS state estimation. In 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (pp. 645-649). IEEE.
[2] Milosevic, B., & Begovic, M. (2003). Nondominated sorting genetic algorithm for optimal phasor measurement placement. IEEE Transactions on Power Systems, 18(1), 69-75.
[3] Silva, R. P. M. D., Delbem, A. C. B., & Coury, D. V. (2013). Genetic algorithms applied to phasor estimation and frequency tracking in PMU development. International Journal of Electrical Power & Energy Systems, 44(1), 921-929.
[4] Abasi, M., Farsani, A. T., Rohani, A., & Beigzadeh, A. (2020). A Novel Fuzzy Theory-Based Differential Protection Scheme for Transmission Lines. International Journal of Integrated Engineering, 12(8), 149-160.
[5] Antonio, A. B., Torreao, J. R., & Do Coutto Filho, M. B. (2001, September). Meter placement for power system state estimation using simulated annealing. In 2001 IEEE Porto Power Tech Proceedings (Cat. No. 01EX502) (Vol. 3, pp. 5-pp). IEEE.
[6] Abasi, M., Saffarian, A., Joorabian, M., & Seifossadat, S. G. (2021). Location of double-circuit grounded cross-country faults in GUPFC-compensated transmission lines based on current and voltage phasors analysis. Electric Power Systems Research, 195, 107124.
[7] Nuqui, R. F., & Phadke, A. G. (2005). Phasor measurement unit placement techniques for complete and incomplete observability. IEEE Transactions on Power Delivery, 20(4), 2381-2388.
[8] Del Valle, Y., Venayagamoorthy, G. K., Mohagheghi, S., Hernandez, J. C., & Harley, R. G. (2008). Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Transactions on evolutionary computation, 12(2), 171-195.
[9] Peng, C., & Xu, X. (2008, June). A hybrid algorithm based on BPSO and immune mechanism for PMU optimization placement. In 2008 7th world congress on intelligent control and automation (pp. 7036-7040). IEEE.
[10] Nimbalkar, N. U., & Joshi, P. M. (2019, October). Optimal PMU Placement using ILP and ACO: A Comparative Study. In 2019 Global Conference for Advancement in Technology (GCAT) (pp. 1-4). IEEE.
[11] Liu, H., Abraham, A., & Zhang, W. (2007). A fuzzy adaptive turbulent particle swarm optimisation. International Journal of Innovative Computing and Applications, 1(1), 39-47.
[12] Shi, Y., & Eberhart, R. C. (2001, May). Fuzzy adaptive particle swarm optimization. In Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546) (Vol. 1, pp. 101-106). IEEE.
[13] Brits, R., Engelbrecht, A. P., & van den Bergh, F. (2007). Locating multiple optima using particle swarm optimization. Applied mathematics and computation, 189(2), 1859-1883.
[14] Liu, H., Abraham, A., & Zhang, W. (2007). A fuzzy adaptive turbulent particle swarm optimisation. International Journal of Innovative Computing and Applications, 1(1), 39-47.
[15] Santhi, R. K., & Subramanian, S. (2011). Adaptive binary PSO based unit commitment. International Journal of computer applications, 15(4), 1-6.
[16] Sadeghi, S. M., Daryalal, M., & Abasi, M. (2022). Two‐stage planning of synchronous distributed generations in distribution network considering protection coordination index and optimal operation situation. IET Renewable Power Generation.
[17] Zhou, M., Centeno, V. A., Phadke, A. G., Hu, Y., Novosel, D., & Volskis, H. A. (2008, April). A preprocessing method for effective PMU placement studies. In 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (pp. 2862-2867). IEEE.
[18] Abasi, M., Heydarzadeh, N., & Rohani, A. (2022). Broken conductor fault location in power transmission lines using GMDH function and single-terminal data independent of line parameters. Journal of Applied Research in Electrical Engineering, 1(1), 22-32.
[19] Abasi, M., Joorabian, M., Saffarian, A., & Seifossadat, S. G. (2021). A Comprehensive Review of Various Fault Location Methods for Transmission Lines Compensated by FACTS devices and Series Capacitors. Journal of Operation and Automation in Power Engineering, 9(3), 213-225.
[20] Baldwin, T. L., Mili, L., Boisen, M. B., & Adapa, R. (1993). Power system observability with minimal phasor measurement placement. IEEE Transactions on Power systems, 8(2), 707-715.
[21] Abasi, M., Farsani, A. T., Rohani, A., & Shiran, M. A. (2019, February). Improving differential relay performance during cross-country fault using a fuzzy logic-based control algorithm. In 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI) (pp. 193-199). IEEE.
[22] Sanati, S., & Alinejad‐Beromi, Y. (2021). Prevention of the current transformer saturation by using negative resistance. IET Generation, Transmission & Distribution, 15(3), 508-517.
[23] Abasi, M., Rohani, A., Hatami, F., Joorabian, M., & Gharehpetian, G. B. (2021). Fault location determination in three-terminal transmission lines connected to industrial microgrids without requiring fault classification data and independent of line parameters. International Journal of Electrical Power & Energy Systems, 131, 107044.
[24] Rohani, A., Abasi, M., Beigzadeh, A., Joorabian, M., & B. Gharehpetian, G. (2021). Bi‐level power management strategy in harmonic‐polluted active distribution network including virtual power plants. IET Renewable Power Generation, 15(2), 462-476.
[25] Abasi, M., Joorabian, M., Saffarian, A., & Seifossadat, S. G. (2021). A Comprehensive Review of Various Fault Location Methods for Transmission Lines Compensated by FACTS devices and Series Capacitors. Journal of Operation and Automation in Power Engineering, 9(3), 213-225.
[26] Ebrahimi, J., & Abedini, M. (2022). A two-stage framework for demand-side management and energy savings of various buildings in multi smart grid using robust optimization algorithms. Journal of Building Engineering, 53, 104486.
[27] Abasi, M., Joorabian, M., Saffarian, A., & Seifossadat, S. G. (2022). An algorithm scheme for detecting single-circuit, inter-circuit, and grounded double-circuit cross-country faults in GUPFC-compensated double-circuit transmission lines. Electrical Engineering, 1-24.
[28] Sanati, S., & Alinejad-Beromi, Y. (2020). Avoid current transformer saturation using adjustable switched resistor demagnetization method. IEEE Transactions on Power Delivery, 36(1), 92-101.
[29] Sadeghi, M., & Abasi, M. (2021). Optimal placement and sizing of hybrid superconducting fault current limiter for protection coordination restoration of the distribution networks in the presence of simultaneous distributed generation. Electric Power Systems Research, 201, 107541.
[30] Ebrahimi, J., Abedini, M., Rezaei, M. M., & Nasri, M. (2020). Optimum design of a multi-form energy in the presence of electric vehicle charging station and renewable resources considering uncertainty. Sustainable Energy, Grids and Networks, 23, 100375.
[31] Khalifeh, M., Mortazavi, S. S., Joorabian, M., & Davatgaran, V. (2013, May). Studding two indices of voltage stability in reliability constrained unit commitment in a day-ahead market. In 2013 21st Iranian Conference on Electrical Engineering (ICEE) (pp. 1-6). IEEE.
[32] Davatgaran, V., Mortazavi, S. S., Saniei, M., & Khalifeh, M. (2013, May). Different strategies of interruptible load contracts implemented in reliability constrained unit commitment. In 2013 21st Iranian Conference on Electrical Engineering (ICEE) (pp. 1-6). IEEE.
[33] Abasi, M., Farsani, A. T., Rohani, A., & Beigzadeh, A. (2020). A Novel Fuzzy Theory-Based Differential Protection Scheme for Transmission Lines. International Journal of Integrated Engineering, 12(8), 149-160.
[34] Makvandi, H., Joorabian, M., & Barati, H. (2021). A new optimal design of ACD-based UPFC supplementary controller for interconnected power systems. Measurement, 182, 109670.
[35] Sanati, S., & Alinejad-Beromi, Y. (2021). Fast and complete mitigation of residual flux in current transformers suitable for auto-reclosing schemes using Jiles-Atherton modeling. IEEE Transactions on Power Delivery, 37(2), 765-774.
[36] Abasi, M., Bahmani, H., Joorabian, M., Ebrahimi, J., & Razavi, M. (2022, December). Designing an Energy Managing System for Distributed Dispersion in Smart Microgrids Based on Environmental Constraints. In 2022 12th Smart Grid Conference (SGC) (pp. 1-6). IEEE.
[37] Sefidgar‐Dezfouli, A., & Davatgaran, V. (2020). Smart microgrid optimal scheduling with stable and economic islanding capability using optimal load contribution as spinning reserve. International Transactions on Electrical Energy Systems, 30(11), e12566.