Open AccessCuckoo Search approach enhanced with genetic replacement of abandoned nests applied to optimal allocation of distributed generation units
Author(s) -
Oliveira Victoria Y.M.,
Oliveira Rodrigo M.S.,
Affonso Carolina M.
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.2017.1992
Subject(s) - cuckoo search , cuckoo , benchmark (surveying) , mathematical optimization , genetic algorithm , computer science , convergence (economics) , stability (learning theory) , distributed generation , algorithm , power (physics) , mathematics , machine learning , particle swarm optimization , zoology , geodesy , economic growth , economics , biology , geography , physics , quantum mechanics
Here, it is presented a novel Cuckoo Search (CS) algorithm called Cuckoo‐GRN (Cuckoo Search with Genetically Replaced Nests), which combines the benefits of genetic algorithm (GA) into the CS algorithm. The proposed method handles the abandoned nests from CS more efficiently by genetically replacing them, significantly improving the performance of the algorithm by establishing optimal balance between diversification and intensification. The algorithm is used for the optimal location and size of distributed generation units in a distribution system, in order to minimise active power losses while improving system voltage stability and voltage profile. The allocation of single and multiple distribution generation units is considered. The proposed algorithm is extensively tested in mathematical benchmark functions as well as in the 33‐bus and 119‐bus distribution systems. Simulation results show that Cuckoo‐GRN can lead to a substantial performance improvement over the original CS algorithm and others techniques currently known in literature, regarding not only the convergence but also the solution accuracy.