An efficient load-balancing in machine learning-based DC-DC conversion using renewable energy resources - Research paper outline

Aug 4, 2026

Upload by Skyler

The paper proposes an ML-based DC-DC conversion algorithm, ML-DC2A, aimed at improving load-balancing in renewable-energy-powered conversion systems. It addresses limitations of conventional control methods such as PWM and MPPT, which struggle to react to rapid renewable supply fluctuations. ML-DC2A uses predictive analytics and adaptive adjustments to optimize conversion efficiency, reliability, responsiveness to load changes, and robustness under variable energy availability. Results indicate enhanced efficiency and improved system resilience for sustainable energy management.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Abstract

Introduction

Renewable energy integration and DC-DC conversion role

Challenges of variable renewable supply

ML-enabled adaptive conversion concepts

An efficient load-balancing in machine learning-based DC-DC conversion using renewable energy resources Kavitha Hosakote Shankara1, Mallikarjunaswamy Srikantaswamy1, Sharmila Nagaraju2 1Department of Electronics and Communication Engineering, JSS Academy of Technical Education, Bengaluru, India 2Department of Electrical and Electronics Engineering, Sri Jayachamarajendra College of Engineering, Mysore, Karnataka, India Article history: Received Mar 1, 2024 Revised Jul 11, 2024 Accepted Jul 26, 2024 Keywords: DC-DC conversion Distributed clustering Energy aware clustering Isolated nodes Load balancing Machine learning Renewable energy Corresponding Author: This paper introduces the machine learning-based DC-DC conversion algorithm (ML-DC2A), a pioneering machine learning (ML) approach designed to enhance load-balancing in DC-DC conversion systems powered by renewable energy sources. Traditional control strategies, such as pulse-width modulation (PWM), maximum power point tracking (MPPT), and basic voltage and current controls, are foundational yet often fall short in adapting to the rapid fluctuation’s characteristic of renewable energy supply. The ML-DC2A optimizes crucial performance indicators including conversion efficiency, reliability, adaptability to energy supply variability, and response time to changing loads. By leveraging predictive analytics and adaptive algorithms, it dynamically manages the conversion process, offering superior performance over traditional techniques. A notable drawback of conventional methods is their inability to anticipate and adjust to real-time changes in energy availability and demand, leading to inefficiencies and potential system instability. The proposed ML-DC2A addresses these challenges by incorporating a sophisticated ML framework that predicts future energy scenarios and adaptively adjusts system parameters to maintain optimal performance. Initial results highlight the transformative potential of integrating ML into renewable energy conversion systems, promising significantly enhanced efficiency and system resilience, thus marking a significant step forward in sustainable energy management. This is an open access article under the CC BY-SA license. Mallikarjunaswamy Srikantaswamy Department of Electronics and Communication Engineering, JSS Academy of Technical Eductaion Bengaluru, India 560060 [Email: pruthvi.malli@gmail.com](Email: pruthvi.malli@gmail.com) Article Info ABSTRACT 1. INTRODUCTION The integration of renewable energy sources into the power grid has become increasingly important as global energy demands continue to rise and environmental concerns over fossil fuel usage intensify. DCDC conversion systems play a crucial role in this integration, ensuring that energy harvested from renewable sources such as solar and wind can be efficiently converted and utilized in power grids. Traditional control strategies, including pulse-width modulation (PWM), maximum power point tracking (MPPT), and basic voltage and current controls, have been the backbone of these systems, ensuring stability and efficiency in energy conversion processes. However, the inherently variable nature of renewable energy sources poses significant challenges to these traditional methods, limiting their ability to adapt to rapid fluctuations in energy supply and demand [1] . Figure 1 showcases a renewable energy system that captures and converts energy from the sun and wind to power electric vehicles (EVs) and smart homes. Solar panels absorb sunlight and transform it into direct current (DC), while wind turbines harness wind energy, converting it into alternating current (AC) . The AC from wind turbines is rectified into DC to harmonize with the output from the solar panels [2] . Figure 1. Fundamental structure of renewable energy-based DC-DC converter system Subsequently, this DC power from both sources is channelled through DC-DC converters. These converters play a cruc

FAQs

What problem does ML-DC2A target in renewable-energy DC-DC conversion systems?

It targets load-balancing challenges caused by rapid fluctuations in renewable energy supply, which reduce the effectiveness of traditional control strategies.

How does the paper compare ML-DC2A with conventional control methods like PWM and MPPT?

It argues conventional methods provide baseline stability and efficiency but often cannot anticipate real-time changes in energy availability and demand, leading to inefficiency or instability.

What performance indicators does the proposed approach aim to improve?

The algorithm is designed to enhance conversion efficiency, reliability, adaptability to energy variability, and response time when loads change.

Aug 4, 2026

Upload by Skyler