3d8d13581898e6394434eed 20231218041031189 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 12 30 2023 13 1 Post Doctoral Researcher, International Innovation Program, Eudoxia Research University, USA. (Principal, JKKN College of Engineering and Technology, Komarapalayam, Namakkal, India. Dr. T. Ruban Deva Prakash https://orcid.org/0000-0001-7475-5607 Achshah R M https://orcid.org/0000-0002-9043-8019 CEO, Effigies Smart Technologies LLP, Kanyakumari, India. Nano-grids are emerging as a vital solution for sustainable energy distribution, particularly in integrating intermittent renewable energy sources. In this context, this research paper aims to address the challenges faced in Nano-grids through the innovative use of a Green Hydrogen Static Compensator (GHSC). The purpose of this study is to overcome issues related to harmonic pollution, power factor correction, and real power balancing within the Nano-grid system. The method employed involves a two-fold approach. Firstly, a modified instantaneous p-q algorithm is proposed to enhance the effectiveness of GHSC. Secondly, artificial intelligence (AI) algorithms, including deep neural networks, random forest, and XGBoost, are utilized to further refine the system's accuracy. These AI algorithms were trained using the modified p-q method, and the simulation of GHSC was performed using SimScape Toolbox in MATLAB-Simulink. The results of the study clearly demonstrate that the proposed GHSC approach, combined with the AI algorithms, outperforms traditional methods in terms of effectiveness in mitigating harmonic pollution and correcting power factors. However, it is noted that the current implementation lacks flexibility for real-time self-learning. In conclusion, the paper underscores the novelty and potential of the GHSC in conjunction with AI algorithms, while also pointing out areas for future research. Specifically, future efforts may focus on developing an automatic weight updating deep neural network that responds to real-time feedback of THD, power factor, and frequency, enhancing the system's adaptability and efficiency. 12 30 2023 47 56 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true International Journal of Innovative Technology and Exploring Engineering (IJITEE) No, I did not receive any financial support for this article. There is no conflicts of interest to the best of my knowledge. The article does not require ethical approval and consent to participate. Not relevant. All authors have equal contributions for this article. 10.35940/ijitee.A9770.1213123 https://www.ijitee.org/portfolio-item/A97701213123/