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Microcontroller-Driven MPPT System for Enhanced Photovoltaic Efficiency: An Experimental Approach in Nepal
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Solar energy utilization in places like Nepal, is often obstructed by unpredicted environmental factors and existing technological barriers. The challenges encountered often result in fluctuating energy outputs, hindering the transition to greener energy solutions. To tackle these issues, this study introduces a custom-designed Maximum Power Point Tracking (MPPT) controller, seamlessly incorporated into a microcontroller-based battery charging system. This approach seeks to enhance the efficiency of photovoltaic (PV) systems, aligning with the global shift towards renewables. The research's primary objective is to enhance PV module power yield employing MPPT techniques, thereby reducing dependency on non-renewable energy sources. Key goals include real-time MPP tracking for optimal power extraction from PV modules and the integration of a real-time monitoring mechanism for PV and battery states. Leveraging a coordinated interplay of sensors measuring temperature, voltage, and current, vital metrics are fed to the microcontroller. This, in turn, generates a precise Pulse Width Modulation (PWM) signal, fine-tuning the voltage regulation of the buck-boost converter Metal Oxide Semiconductor Field Effect Transistor (MOSFET) for optimal operation. The adopted approach emphasizes monitoring environmental metrics, overseeing power outputs, and generating PWM signals to adeptly manage the buck-boost converter MOSFET voltage. Concurrently, data is transmitted hourly to a cloud platform, facilitating real-time monitoring capabilities showcasing the IoT application. As a result of these integrations, an efficiency improvement of approximately 37.28% was observed. In essence, this research underscores the profound impact of merging advanced technologies within the renewable energy sector, offering a robust blueprint for enhancing energy stability and productivity.
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Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise
SpoQFL applies sporadic learning to quantum federated learning by suppressing noisy client updates, claiming higher accuracy and more stable convergence in noisy-device simulations.
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