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Differential evolutionary particle swarm optimization for load adjustment distribution state estimation using correntropy
Author(s) -
Iwata Sohei,
Fukuyama Yoshikazu
Publication year - 2018
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.23147
Subject(s) - particle swarm optimization , outlier , differential evolution , mathematical optimization , least squares function approximation , multi swarm optimization , evolutionary computation , least trimmed squares , evolutionary algorithm , minification , algorithm , computer science , mathematics , control theory (sociology) , non linear least squares , estimation theory , statistics , artificial intelligence , estimator , control (management)
This paper proposes differential evolutionary particle swarm optimization (DEEPSO) for load adjustment distribution state estimation (DSE) using correntropy. Practical equipment in distribution systems causes nonlinear characteristics in an objective function and evolutionary computation methods have been applied to DSE so far. This paper applies DEEPSO in order to improve estimation equality. Minimization of sum of square errors by the least squares method has a problem when outliers exit in measured values. Quality of estimated results is largely affected by the outliers using the least squares method, while correntropy has a possibility not to be affected by the outliers. The proposed method is applied to a typical distribution system. The results indicate that the proposed DEEPSO‐based method can improve estimation results compared with conventional particle swarm optimization (PSO) and hybrid PSO‐based method, and the correntropy‐based proposed method can estimate distribution system conditions more accurately than the conventional least squares method.