Open AccessProbabilistic load flow using the particle swarm optimisation clustering method
Author(s) -
Hagh Mehrdad Tarafdar,
Amiyan Payman,
Galvani Sadjad,
Valizadeh Naser
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2017.0678
Subject(s) - cluster analysis , particle swarm optimization , probabilistic logic , computation , monte carlo method , computer science , mathematical optimization , algorithm , similarity (geometry) , flow (mathematics) , mathematics , artificial intelligence , statistics , geometry , image (mathematics)
In this study, a clustering scheme based on the particle swarm optimisation (PSO) algorithm is used for probabilistic load flow calculation in the presence of wind generations. In this method, input random variables are first clustered in several groups according to their similarity and then a representative is assigned to each group by the PSO algorithm; finally, the deterministic load flow is performed. Using this technique, computational time is meaningfully decreased, while an acceptable level of accuracy is achieved. The IEEE 57‐bus and IEEE 118‐bus test systems were selected for the case study to demonstrate the performance of the proposed method. The results were compared with those of the Monte Carlo as well as K‐means clustering methods from the accuracy and computation time points of view. Simulation results show that the introduced method significantly reduced the computational burden while keeping a high level of accuracy.