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Dependable Multi-population Different Evolutionary Particle Swarm Optimization for Distribution State Estimation using Correntropy
This paper proposes dependable multi-population differential evolutionary particle swarm optimization (DEEPSO) for distribution state estimation (DSE) using correntropy. Considering deregulation of power systems and high penetration of renewable energies, power flow can be changed suddenly. One of the solutions for the problem is applications of parallel and distributed computing. Since power system is one of the infrastructures of social community, not only fast computation, but also sustainable control (dependability) is strongly required for DSE. From the viewpoint of dependability, evolutionary computation techniques with multiple searching points have a big advantage. The results by the proposed multi-population DEEPSO based method are compared with various numbers of sub-swarms. It is found that the proposed method with more than one sub-swarm is superior to the proposed method with only one sub-swarm.
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