MODELING AND OPTIMISATION OF A SOLAR ENERGY HARVESTING SYSTEM FOR WIRELESS SENSOR NETWORK

Abstract

The rapid proliferation of Wireless Sensor Networks (WSNs) in various applications has underscored the need for efficient and sustainable power sources. Solar energy harvesting has emerged as a promising solution to address the power constraints of battery-operated sensor nodes. The proposed system comprises solar photovoltaic (PV) panels, power management circuitry, and energy storage components. To achieve accurate modeling, a detailed analysis of the solar panel's characteristics, such as I-V and P-V curves, is conducted. Moreover, environmental factors such as solar irradiance, temperature, and shading effects are considered to enhance the realism of the model. The optimization process aims to maximize the overall energy harvesting efficiency by dynamically adapting the system's operating parameters. Advanced algorithms like Maximum Power Point Tracking (MPPT) are employed to ensure that the solar panels operate at their peak power output under varying environmental conditions. Furthermore, predictive models are utilized to anticipate the availability of solar energy, enabling the system to proactively adapt to changing light conditions. To validate the proposed modeling and optimization approach, a series of real-world experiments are conducted in various geographical locations and climate conditions. Performance metrics such as energy conversion efficiency, harvested energy density, and sensor node power consumption are analyzed and compared with traditional energy harvesting techniques. The results demonstrate significant improvements in the efficiency and reliability of the solar energy harvesting system for WSNs. The optimized system exhibits greater adaptability to dynamic environmental changes, leading to prolonged sensor network lifetime and reduced dependency on battery replacement or recharging.