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has been cited by the following article:

• TITLE: Optimal Static State Estimation Using hybrid Particle Swarm-Differential Evolution Based Optimization

  AUTHORS: Sourav Mallick, S. P. Ghoshal, P. Acharjee, S. S. Thakur

  KEYWORDS: Differential Evolution; Ill-conditioned System; Particle Swarm Optimization; State Estimation

  JOURNAL NAME: Energy and Power Engineering, Vol.5 No.4B, October 30, 2013

  ABSTRACT: In this paper, swarm optimization hybridized with differential evolution (PSO-DE) technique is proposed to solve static state estimation (SE) problem as a minimization problem. The proposed hybrid method is tested on IEEE 5-bus, 14-bus, 30-bus, 57-bus and 118-bus standard test systems along with 11-bus and 13-bus ill-conditioned test systems under different simulated conditions and the results are compared with the same, obtained using standard weighted least square state estimation (WLS-SE) technique and general particle swarm optimization (GPSO) based technique. The performance of the proposed optimization technique for SE, in terms of minimum value of the objective function and standard deviations of minimum values obtained in 100 runs, is found better as compared to the GPSO based technique. The statistical error analysis also shows the superiority of the proposed PSO-DE based technique over the other two techniques.