Loss reduction and reliability improvement in dis-tributed network using HF-SOA based optimal in-stallation of DG, SCs and STF

Ithaya Rajagopalan, Jagatheeswari Ponnuswamy


In recent years, most of the research works related to Distributed Generation (DG), targeted on the loss minimization and reliability enhancement due to the existence of intermittent Renewable Energy Sources (RES). In this research work, this target is attained by an optimal installation of DG, shunt capacitors (SCs) and single tuned filter (STF) through a novel hybrid fuzzy based seagull optimization algorithm (HF-SOA) in a distributed power network. Compared to the literatures better harmonics mitigation is achieved in this research work due to the presence of STF. The proposed research problem is considered as multi-objective and a novel objective function that incorporates, minimization of power loss, harmonics and enhancement of voltage profile (VP) as well as system reliability is introduced in this research article. The fuzzy membership function is framed for each objective function parameter and the fuzzified membership functions are considered as an objective function for the SOA approach. Three case studies are conducted in both IEEE 33 and 66 radial networks to examine the influence of the HF-SOA algorithm in satisfying the proposed multi-objective function (MOF).  In the case studies, the percentage loss, THD reduction, VP enhancement and reliability improvement measured through expected interruption cost (ECOST) are analyses in detail. The coding of HF-SOA and analysis of the proposed work are resolved in the MATLAB R2022a Editor Software. The simulation results confirms that the proposed HF-SOA is superior than the with the recently published optimization approaches named genetic moth swarm algorithm (GMSA) and salp swarm optimization algorithm (SSA).


Distributed Generation (DG); hybrid fuzzy based seagull optimization algorithm (HF-SOA); reliability improvement; seagull optimi-zation algorithm (SOA); Sizing and Allocation of DG

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DOI: https://doi.org/10.33180/InfMIDEM2023.102


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