The Comparison Study Among Optimization Techniques in Optimizing a Distribution System State Estimation

  • Authors

    • Rosli Omar
    • Hazim Imad Hazim
    • Imad Hazim Mohammed
    • Ahmed N Abdalla
    • Marizan Sulaiman
    • Mohammed Rasheed
    https://doi.org/10.14419/ijet.v7i3.28.23425
  • Estate Estimation, Power System, Firefly Algorithm.
  • State estimation considered the main core of the Energy Management System and plays an important role in stability analysis, control and monitoring of electric power systems. Therefore, accurate and timely efficient state estimation algorithm is a prerequisite for a stable operation of modern power grids. These papers introduce an intelligent centralized State Estimation method based on Firefly algorithm for distribution power systems. The mathematical procedure of distribution system state estimation which utilizing the information collected from available measurement devices in real-time. A consensus based static state estimation strategy for radial power distribution systems is proposed in this research. The states of these systems are first estimated through centralized approach using the proposed algorithm to compare with power flow algorithm. The result a proved to be computational efficient and accurately evaluated the impact of distributed generation on the power system. In addition, the proposed FA show faster with increasing the number of buses.

     

     

     
  • References

    1. [1] Ahmed N. A. A. Simulation model of brushless excitation system. American Journal of Applied Sciences, 2007, 4(12), 1079-1083.

      [2] Alganahi H. S., Ahmed M. H. & Ahmed N. A. Experimental study of using renewable energy in Yemen. Australian Journal of Basic and Applied Sciences, 2009, 3(4), 4170-4174.

      [3] Donoho D. L. Breakdown properties of multivariate location estimators. PhD qualifying paper, Harvard University, 1982.

      [4] Ahmed N. A., Cheng S. J., Wen J. Y. & Jing Z. Model parameter identification of excitation system based on a genetic algorithm techniques. Proceedings of the International Conference on Power System Technology, 2006, pp. 1-5.

      [5] Mili L. & Cutsem T. V. Implementation of HTI method in power system state estimation. IEEE Transactions on Power Systems, 1988, 3(3), 887-893.

      [6] Abur A. & Celik M. K. A fast algorithm for the weighted least absolute value state estimation [for power systems]. IEEE Transactions on Power Systems, 1991, 6(1), 1-8

      [7] Du P., Huang Z., Sun Y., Diao R., Kalsi K., Anderson K. K., Li Y. & Lee B. Distributed dynamic state estimation with extended kalman filter. Proceedings of the North American Power Symposium, 2011, pp. 1-6.

      [8] Clements K. A. & Davis P. W. Multiple bad data detectability and identifiability: A geometric approach. IEEE Transactions on Power Delivery, 1986, 1(3), 355-360.

      [9] Abur A. & Exposito A. G. Power system state estimation: Theory and implementation. Marcel Dekker, 2004.

      [10] Kezunovic M. & Perunicic B. An accurate fault location using synchronized sampling. Electric Power System Research Journal, 1994, 29(3)161-169,

      [11] Arif W. & Naeem A. Analysis and optimization of IEEE 33 bus radial distributed system using optimization algorithm. Journal of Emerging Trends in Applied Engineering, 2016, 1(2), 34-37.

      [12] Tungadio D. H., Numbi B. P., Siti M. W. & Jimoh A. A. Particle swarm optimization for power system state estimation. Neurocomputing, 2015, 148,175–180.

      [13] Monticelli A. Electric power system state estimation. Proceedings of the IEEE, 2000, 88(2), 262–282.

      [14] Diambomba H. T., Jacobus A. J. & Mukwanga W. S. Power system state estimation solution using modified models of PSO algorithm: Comparative study. Measurement, 2016, 92, 508–523.

  • Downloads

  • How to Cite

    Omar, R., Imad Hazim, H., Hazim Mohammed, I., N Abdalla, A., Sulaiman, M., & Rasheed, M. (2018). The Comparison Study Among Optimization Techniques in Optimizing a Distribution System State Estimation. International Journal of Engineering & Technology, 7(3.28), 214-217. https://doi.org/10.14419/ijet.v7i3.28.23425