International Journal of Electrical Power & Energy Systems (forthcoming 2011)Īrefi, A., Haghifam, M.R., Fathi, S.H., et al.: A novel algorithm based on Honey Bee Mating Optimization for distribution harmonic state estimation including distributed generators. IEEE Transactions on Power Delivery, 1-1 (2010)Īrefi, A., Haghifam, M.R., Fathi, S.H.: Observability analysis of electric networks considering branch impedance. 1–5 (2008)Īminifar, F., Fotuhi-Firuzabad, M., Shahidehpour, M., et al.: Probabilistic multistage PMU placement in electric power systems. In: IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, pp. Expert Systems with Applications 38, 7263–7269 (2011)Īmin, M.: Challenges in reliability, security, efficiency, and resilience of energy infrastructure: Toward smart self-healing electric power grid. IEEE Transactions on Power Systems 14, 1273–1278 (1999)Īhmadi, A., Alinejad-Beromi, Y., Moradi, M.: Optimal PMU placement for power system observability using binary particle swarm optimization and considering measurement redundancy. IEEE Transactions on Power Systems 2, 552–558 (1987)Ībur, A., Magnago, F.H.: Optimal meter placement for maintaining observability during single branch outages. Marcel Dekker Inc., New York (2004)Ībur, A., Keyhani, A., Bakhtiari, H.: Autoregressive filters for the identification and replacement of bad data in power system state estimation. IEEE Transactions on Power Systems 5, 894–901 (1990)Ībur, A., Exposito, A.G.: Power System State Estimation Theory and Implementation. This process is experimental and the keywords may be updated as the learning algorithm improves.Ībur, A.: A bad data identification method for linear programming state estimation. These keywords were added by machine and not by the authors. Distribution HSE (DHSE) and meter placement for SDSE are also presented. Some characteristics of proposed SDES are distributed, hybrid, multi-micro grid and islanding support, Harmonic State Estimation (HSE), observability analysis and restore, error processing, and network parameter estimation. The main features of smart grid SE, which is here named as “Smart Distributed SE” (SDSE), are discussed. Since most principles of smart power grids are appropriate to distribution networks, the Distribution SE (DSE) considering load correlation is argued and illustrated by an example. The observability analysis as a prior task of SE is demonstrated and an analytical method to consider the impedance values of the branches is developed and discussed by examples. The trend of SE error with respect to the increasing of the smart grids implementation investigated. In this chapter, the role of State Estimation (SE) in smart power grids is presented.
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