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MirMohammadAli Roudaki, SeyedHamed, Abedi, Mehrdad, Pourkeivani, Iraj. (1402). Power System State Estimation through Optimal PMU Placement and Neural Network using Whale Algorithm. نشریه مهندسی عمران امیرکبیر, 55(Issue 3 (Special Issue)), 351-364. doi: 10.22060/eej.2023.22232.5525
SeyedHamed MirMohammadAli Roudaki; Mehrdad Abedi; Iraj Pourkeivani. "Power System State Estimation through Optimal PMU Placement and Neural Network using Whale Algorithm". نشریه مهندسی عمران امیرکبیر, 55, Issue 3 (Special Issue), 1402, 351-364. doi: 10.22060/eej.2023.22232.5525
MirMohammadAli Roudaki, SeyedHamed, Abedi, Mehrdad, Pourkeivani, Iraj. (1402). 'Power System State Estimation through Optimal PMU Placement and Neural Network using Whale Algorithm', نشریه مهندسی عمران امیرکبیر, 55(Issue 3 (Special Issue)), pp. 351-364. doi: 10.22060/eej.2023.22232.5525
MirMohammadAli Roudaki, SeyedHamed, Abedi, Mehrdad, Pourkeivani, Iraj. Power System State Estimation through Optimal PMU Placement and Neural Network using Whale Algorithm. نشریه مهندسی عمران امیرکبیر, 1402; 55(Issue 3 (Special Issue)): 351-364. doi: 10.22060/eej.2023.22232.5525

Power System State Estimation through Optimal PMU Placement and Neural Network using Whale Algorithm

AUT Journal of Electrical Engineering
مقاله 4، دوره 55، Issue 3 (Special Issue)، 2023، صفحه 351-364    اصل مقاله (1.21 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/eej.2023.22232.5525
نویسندگان
SeyedHamed MirMohammadAli Roudaki؛ Mehrdad Abedi* ؛ Iraj Pourkeivani
Amirkabir University of Technology, Tehran, Iran
چکیده
The efficient operation and planning along with security of power systems have always occupied an important position. The power system becomes increasingly complex due to the rapidly growth in energy demand. Such a system requires a real-time approach to monitoring and control. Therefore, State Estimation (SE) tools are necessary, especially for nonlinear power grids. Most of network applications use the real-time data provided by the state estimator. Therefore, an optimal performance of state estimation output is the ultimate concern for the system operator. This need is particularly more in focus today due to deregulated and congested systems and smart grid initiatives. The output of the state estimator nearly represents a true state of the system. The present paper, describes the general framework of state estimation in power networks. Also, in the present study linear state estimation method accompanied by optimal placement for Phasor Measurement Unit (PMU) for complete observability and artificial neural network (ANN) trained by Whale Optimization Algorithm (WOA) is employed. The trained model can be used to estimate voltage magnitudes and phase angles as power system states. The proposed method increases accuracy and execution speed while the complication in the formulation will be reduced considerably. A seasonal load profile is considered to measure the accuracy of the state estimation and make the simulation more realistic. Finally, the minimum estimation error will be shown for IEEE 14 and 30 buses benchmark.
کلیدواژه‌ها
Whale optimization algorithm؛ power system state estimation؛ optimal placement of phasor measuring unit؛ artificial neural network
مراجع
  1. Abedi, Operation, Optimization and control in Electric Power Systems. Tehran: Nahardanesh., 2018.
  2. J. Wood, B. F. Wollenberg, G. B. Sheble, Power generation, operation, and control, 3rd ed., Wiley, 2014.
  3. G. Phadke, J. S. Thorp, Synchronized Phasor Measurements and Their Applications. New York: Springer, 2008.
  4. Mosbah and M. El-Hawary, “Evaluating the Impact of Phasor Measurement Units on the Accuracy of State Estimation,” 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE), pp. 1-5, 2018.
  5. Chakrabarti, E. Kyriakides, G. Ledwich, and A. Ghosh, “Inclusion of PMU current phasor measurements in a power system state estimator,” IET Gen. Trans. Dist., Vol. 4, No. 10, pp. 1104-1115, 2009.
  6. Valverde, S. Chakrabarti, E. Kyriakides, and V. Terzija, “A constrained formulation for hybrid state estimation,” IEEE Trans. Power Syst., Vol. 26, No. 3, pp. 1102-1109, 2011.
  7. F. Nuqui and A. G. Phadke, “Phasor Measurement Unit Placement Techniques for Complete and Incomplete Observability”, IEEE Transactions on Power Delivery, vol. 20, no. 4, pp 2381–2388, Oct. 2005.
  8. Gou, “Optimal placement of PMUs by integer linear programming”, IEEE Transactions on Power Systems, vol. 23, no. 3, pp 1525–1526, Aug. 2008.
  9. C. Huang and Y.-F. Huang, “Learning algorithms for perceptions using back-propagation with selective updates,” IEEE Control Syst. Mag., vol. 10, no. 3, pp. 56–61, Apr. 1990.
  10. Seyedali Mirjalili, Andrew Lewis, “The Whale Optimization Algorithm,” Advances in Engineering Software., pp. 51–67, 2016.
  11. Aljarah, I., Faris, H. and Mirjalili, S., Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Computing, 22(1), pp.1-15. 2018.
  12. Manitsas, R. Singh, B. C. Pal and G. Strbac, “Distribution System State Estimation Using an Artificial Neural Network Approach for Pseudo Measurement Modeling,” in IEEE Transactions on Power Systems, vol. 27, no. 4, pp. 1888-1896, Nov. 2012.
  13. Soni, S. Bhil, D. Mehta and S. Wagh, “Linear state estimation model using phasor measurement unit (PMU) technology,” 2012 9th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 1-6, 2012.
  14. Power Svstems Test Case Archive. llniversitiv of Washington. Website: http://www.ee. washington. edulresearch/pstca
  15. Tian, G., Gu, Y., Shi, D., Fu, J., Yu, Z., & Zhou, Q. (2021). Neural-network-based power system state estimation with extended observability. Journal of Modern Power Systems and Clean Energy, 9(5), 1043-1053.
  16. Mishra, B., Thakur, S. S., Rai, P., & Tanti, D. K. (2021). Linear State Estimation for Power Systems Based on PMU Measurements. In Intelligent Communication, Control and Devices: Proceedings of ICICCD 2020 (pp. 249-261). Springer Singapore.
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