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@article{186272,
        author = {SAMPATHIRAO NAGARAJU and Dr. KOTTALA PADMA},
        title = {ANN BASED CONTROLLER FOR GRID INTEGRATION OF HYBRID CONVERTERLESS MICROGRID},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {302-322},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186272},
        abstract = {The increasing demand for clean and sustainable energy has led to the development of advanced hybrid renewable energy systems. This study focuses on the modelling, control, and energy management of a hybrid grid-connected power system that integrates wind energy, photovoltaic (PV) systems, a battery energy storage system (BESS), fuel cells (FC), and an electrolyser. The proposed system configuration eliminates the need for a separate PV converter by combining wind and PV as primary energy sources, BESS as a secondary source, and the combination of FC and electrolyser as tertiary sources. This design not only simplifies the system architecture but also enhances cost-effectiveness and overall reliability.
To achieve optimal performance, a Hybrid Artificial Neural Network (ANN) controller is implemented, which provides adaptive, intelligent, and non-linear control for maintaining system stability and efficient energy flow under variable wind and solar conditions. The system stability is further improved using a lead compensator with an integrator, which minimizes steady-state errors and ensures an appropriate phase margin. The Rotor Side Controller (RSC) and Grid Side Controller (GSC) operate cooperatively to maintain grid synchronization, provide frequency support, and ensure efficient power distribution from renewable sources.
The ANN-based controller effectively compensates for the intermittency and power fluctuations inherent in renewable energy sources by dynamically adjusting power flow between the BESS, FC, and electrolyser. The electrolyser stores excess energy by producing hydrogen during high-generation periods, which is later utilized by the fuel cell during low-generation conditions, thus ensuring continuous power supply. The advanced energy management system also prevents BESS overcharging, minimizes power oscillations, and guarantees a stable power output to the grid.
The proposed ANN-based control technique effectively minimizes oscillations in the DC-link voltage, improves transient response, and enhances the overall system stability when compared to conventional control methods. The simulation results, obtained using MATLAB/Simulink, clearly validate that the ANN-based hybrid system ensures steady power flow, efficient energy management, reduced converter count, and higher overall efficiency under various environmental and operational conditions. In the previously used control method, the system’s power output settled between 8.5 to 12 seconds. However, with the implementation of the ANN controller, the power settles at its final steady-state positive value much faster, reaching stability at around 0.35 seconds. This demonstrates a significant improvement in the dynamic performance and response speed of the system.},
        keywords = {},
        month = {October},
        }