DOI:10.35833/MPCE.2019.000317 Nature-inspired Hybrid Optimization Algorithms for Load Flow Analysis of Islanded Microgrids Page view: 0        Net amount: 78 Author: Saad Mohammad Abdullah1,Ashik Ahmed1,Quazi Nafees Ul Islam1 Author Affiliation: Department of Electrical and Electronic Engineering, Islamic University of Technology, Board Bazar, Gazipur 1704, Bangladesh Foundation: Abstract: Load flow analysis is a significant tool for proper planning, operation, and dynamic analysis of a power system that provides the steady-state values of voltage magnitudes and angles at the fundamental frequency. However, due to the absence of a slack bus in an islanded microgrid, modified load flow algorithms should be adopted considering the system frequency as one of the solution variables. This paper proposes the application of nature-inspired hybrid optimization algorithms for solving the load flow problem of islanded microgrids. Several nature-inspired algorithms such as genetic algorithm (GA), differential evolution (DE), flower pollination algorithm (FPA), and grasshopper optimization algorithm (GOA) are separately merged with imperialistic competitive algorithm (ICA) to form four hybrid algorithms named as ICGA, ICDE, ICFPA, and ICGOA. Performances of these algorithms are tested on the 6-bus test system and the modified IEEE 37-bus test system. A comparison among the proposed algorithms is carried out in terms of statistical analysis conducted using SPSS statistics software. From the statistical analysis, it is identified that on an average, ICDE takes less number of iterations and consequently needs less execution time compared with other algorithms in solving the load flow problem of islanded microgrids. Keywords: Nature-inspired hybrid algorithm ; global optimization ; islanded microgrid ; load flow analysis Received:May 13, 2019               Online Time:2020/12/03 View Full Text       Download reader