{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:03:10Z","timestamp":1777705390255,"version":"3.51.4"},"reference-count":19,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,2,14]]},"abstract":"<jats:p>The distribution system plays a pivotal role in connecting power generation sources to vital facilities like nuclear reactors. In this intricate network, losses occur while supplying electricity, demanding a reduction for enhanced performance. The quality of power reaching the nuclear plant is imperative due to the susceptibility of sensitive equipment to poor power conditions. This study presents a reconfiguration strategy to bolster dependability and curtail power losses in distribution networks. Leveraging the Modified Genetic Optimization Algorithm (MGOA), the reconfiguration conundrum is tactfully addressed to determine optimal switch operation schemes. The MGOA-based reconfiguration not only minimizes energy wastage but also refines voltage profiles, elevating operational efficiency. The effectiveness of this approach is substantiated through its successful application to radial distribution systems comprising 33, 69, and 136 buses. Embracing diverse scenarios encompassing normal and abnormal operating states, as well as varying loads, the method\u2019s robustness is showcased. The validity of the proposed methodology is reinforced by comprehensive simulation results, underscoring its reliability and potential for real-world implementation.<\/jats:p>","DOI":"10.3233\/jifs-233917","type":"journal-article","created":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T12:19:41Z","timestamp":1702988381000},"page":"3577-3591","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing efficiency in electrical distribution systems: A novel approach via modified genetic optimization algorithm for loss reduction in optimal network distribution"],"prefix":"10.1177","volume":"46","author":[{"given":"K.","family":"Rajalakshmi","sequence":"first","affiliation":[{"name":"Department of Robotics and Automation, Kings Engineering College, Chennai, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. 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