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Hybrid Energy Storage System (HESS) has been presented in conjunction with wind and solar energy conversion technologies characterized by coupling of power electronic converters, neural networks, optimization, battery, controllers, and ultra-capacitor storage systems to attain the intended performance. The power balance is regulated via an energy management system by considering the fluctuation in load demand and renewable energy power generation. Further, the voltage controller utilizes the deep Siamese neural network (deep SNN) that effectively carries out energy management among the hybrid renewable energy sources. The smelling-based hunting optimization assists in optimal parameter tuning and training of the deep SNN for enhancing the HESS\u2019s efficiency. In addition, the microgrid operates independently and offers a testing area for different energy management systems and testing scenarios. The proposed small-scale microgrid, which is based on renewable energy, can serve as a significant testing area for methods utilized in smart grid applications. The proposed SBHO model\u2019s efficiency is determined by varying the voltage, current, and power of the wind, solar, battery, and ultra-capacitor measurements. The current capacity of the battery reaches \u221211.06A and the battery voltage reaches 259.831 V in 0.82 s. The DC load measurement utilizing the SBHO approach obtained a DC bus voltage of 357.11V, a load current of 3.348 A within 0.82 s, and in DC power load attained the 1195.58 W within 0.82 s. The battery\u2019s SOC by applying the smelling-based hunting optimization is gradually increased to 50.008% in 0.82s.In terms of the PV measurement, the PV current, PV voltage, and PV power are obtained as 4.238A,241.08V, and 1021.64W for the SBHO approach which surpasses other competent techniques. The ultra-capacitor current ranges from 35A to 40A with reduced heavy discharge of SOC from 98% obtained on evaluating the performance. The output power of wind using a boost convert remains 3000 W with few harmonics.<\/jats:p>","DOI":"10.3233\/web-230009","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T10:47:28Z","timestamp":1727779648000},"page":"73-97","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Smelling-based hunting optimization for battery\u2013Super\/Ultracapacitor hybrid energy storage station in wind\/solar generation system using Siamese neural network"],"prefix":"10.1177","volume":"23","author":[{"given":"Pradeep Kumar","family":"Tiwari","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Sam Higginbottom University of Agriculture Technology and Sciences, Prayagraj, Uttar Pradesh, India."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manish Kumar","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Sam Higginbottom University of Agriculture Technology and Sciences, Prayagraj, Uttar Pradesh, India."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,3,20]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.enconman.2017.04.019","article-title":"A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy systems","author":"Al-falahi M.D.A.","year":"2017","unstructured":"Al-falahi M.D.A., Jayasinghe S.D.G., Enshaei H., A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy systems, Energy Convers Manage 143 (2017), 252\u2013274. doi:10.1016\/j.enconman.2017.04.019.","journal-title":"Energy Convers Manage"},{"issue":"8","key":"e_1_3_2_3_2","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.1016\/j.rser.2009.01.010","article-title":"Simulation and optimization of stand-alone hybrid renewable energy systems","author":"Bernal-Agust\u00edn J.L.","year":"2009","unstructured":"Bernal-Agust\u00edn J.L., Dufo-L\u00f3pez R., Simulation and optimization of stand-alone hybrid renewable energy systems, Renew. 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