Nature Inspired Hybrid Optimization Algorithms for Load Flow Analysis of Autonomous Microgrids

Abdullah, Saad Mohammad

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dc.contributor.author	Abdullah, Saad Mohammad
dc.date.accessioned	2020-10-26T08:24:05Z
dc.date.available	2020-10-26T08:24:05Z
dc.date.issued	2019-11-15
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dc.identifier.uri	http://hdl.handle.net/123456789/567
dc.description	Supervised by Prof. Dr. Ashik Ahmed	en_US
dc.description.abstract	Load flow analysis is a significant tool for proper planning, operation and dynamic analysis of a conventional power system which provides the steady state values of voltage magnitudes and angles at fundamental frequency. However, due to the absence of slack bus in an autonomous microgrid, modified load flow algorithms should be adopted considering the system frequency as one of the solution variables. This work proposes the application of nature inspired hybrid optimization algorithms for solving the load flow problem of autonomous microgrids. Several nature-inspired algorithms such as, Genetic Algorithm (GA), Differential Evolution (DE) algorithm, 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 and their performances are tested on a modified IEEE 37-Bus microgrid system as a case study. Particle swarm optimization (PSO) algorithm is also employed to perform the load flow analysis of the same case study system. Among the above-mentioned algorithms, to identify the algorithm with better performance, independent samples t-tests have been conducted using SPSS statistical analysis software. From the statistical analysis, it has been identified that ICDE exhibit better performance compared to the other algorithms in terms of the number of iterations and the execution time required to complete the optimization process for the load flow analysis.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Electronic Engineering, Islamic University of Technology,Board Bazar, Gazipur, Bangladesh	en_US
dc.title	Nature Inspired Hybrid Optimization Algorithms for Load Flow Analysis of Autonomous Microgrids	en_US
dc.type	Thesis	en_US