Enhancement of Wind Energy Penetration Levels In Rural Grid By Power Quality Mitigation Using DSTATCOM Controlled by Adaptive Algorithms: Wind Energy (WE) penetration levels in rural grids are limited due to the Power Quality (PQ) issues, wind speed inconsistency and low short circuit ratios. This is further aggravated due to the increase in non-linear loads. This work proposes a methodology to enhance WE penetration levels by mitigating PQ issues using a distribution static compensator (DSTATCOM) controlled by the Adaptive control algorithms. The proposed adaptive controls estimate load currents' active and reactive weight components by adapting the changes in wind generation, DC-link, and grid voltage. These weights are used to generate reference current signals to switch the DSTATCOM to mitigate PQ disturbances. The proposed method has been implemented to enhance WE penetration in the rural grid under the non-linear load. The results obtained by simulation in MATLAB studies have been validated using experimental studies. These research work established the effectiveness of the proposed method for enhancing WE penetration levels in rural grids by up to 30%. 

As a Research Associate, my focus revolves around research initiatives, with a significant emphasis on developing a DC Microgrid experimental prototype within an RTDS/Microgrid lab. I developed a 1 kW setup of a DC microgrid (DCMG) with two nodes of boost converters feeding a common load on a DC bus, complemented by a battery bank connected via a bidirectional DC-DC converter (BDC). This setup included three DC-DC converters: two boost converters supplying power to the load and one BDC for managing the DC bus voltage. I conducted simulation studies in MATLAB Simulink to analyze and optimize the performance of this system, focusing on efficient energy management and seamless integration of DC sources. This work has laid a strong foundation for further research and development in the field of DC microgrids, contributing significantly to the advancement of sustainable energy solutions. 

I engage in multifaceted research activities encompassing government and industry projects. I have worked extensively on developing a High-Frequency AC (HFAC) testbed as part of a Govt sponsored project. This project focuses on advancing HFAC microgrid technology, addressing the challenges and opportunities associated with high-frequency power distribution systems. Additionally, I have been involved in a Toyota-sponsored project aimed at optimizing the smart charging and discharging of electric vehicle (EV) batteries using advanced AI and machine learning models. This work leverages big data analysis to enhance the efficiency and reliability of Vehicle-to-Grid (V2G) systems, contributing to the precise scheduling of EVs for home and advice hours and carbon-zero goals.