Dependable Multi-population Different Evolutionary Particle Swarm Optimization for Distribution State Estimation using Correntropy

Iwata, Sohei; Fukuyama, Yoshikazu; Jintsugawa, Toru; Fujimoto, Hisashi; Matsui, Tetsuro

doi:10.1016/j.ifacol.2018.11.698

Cited by 2 publications

(1 citation statement)

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“…The basic PSO algorithm has first been applied for blind channel estimation in PLC systems with time-varying and frequency-selective channel response impaired by impulsive noise [6], besides its application for distribution state estimation [22], parameter identification and minimization of power losses [23] in power distribution networks. Assuming a stationary channel, the received values of transmitted M-QAM OFDM symbols at a given subcarrier, i may belong to one of M possible symbols which is considered a particle in the PSO algorithm.…”

Section: Pso or Improved Pso (Ipso) Algorithmmentioning

confidence: 99%

Efficient Low-Complexity Optimized Channel Estimating Methods for OFDM-Based Low-Voltage Broadband Power Line Communication Systems

Akpari

Nunoo

Deynu

et al. 2022

J. Microw. Optoelectron. Electromagn. Appl.

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Broadband Power Line Communication (BB-PLC) technology enables data transmission for smart grid applications. Nevertheless, channel equalizers are required in the receiver to estimate and compensate for the nonlinear time-variant impulse response and noise interference effects introduced by the BB-PLC channel. In this paper, we append the Particle Swarm Optimization (PSO) algorithm and its proposed improved version to the commonly used Least-Square (LS) and Linear Minimum Mean Square Error (LMMSE) algorithms to advance four new blocktype pilot-aided hybrid channel estimation algorithms for lowvoltage Orthogonal Frequency Division Multiplexing (OFDM)based BB-PLC systems. Extensive numerical simulation results for four different M-QAM formats (M = 8, 16, 32, 64) show that the proposed algorithms significantly improve the performance of the traditional LS and LMMSE estimators, at least for the parameters of the BB-PLC system studied in this work. In addition, the computational load complexity of the PSO-inspired LMMSE algorithm is lower compared to the conventional LMMSE estimator.

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Section: Pso or Improved Pso (Ipso) Algorithmmentioning

confidence: 99%