Solar power harvesting design for wireless communication based wireless sensor networks application

This study conducted a thorough exploration of the efficiency of different maximum power point tracking algorithms within photovoltaic energy harvesting-based wireless sensor networks, with a specific focus on how these algorithms respond to environmental factors such as temperature and solar irradiance. Among the various algorithms evaluated, the Particle Swarm Optimization (PSO) algorithm stood out due to its exceptional performance. It exhibited a notably low overshoot of 14.8 volts and 25 mA at 0.1 seconds. Comparatively, the Perturb and Observe (PO) algorithm displayed a slightly higher overshoot of 15 volts and 25.5 mA. The Incremental Conductance (IC) and Fuzzy Logic (FL) algorithms both showed even higher overshoots, reaching 15.3 volts and 27 mA within the same timeframe. In the critical area of output power, the PSO algorithm again emerged as the most efficient, with the IC and FL algorithms following behind, and the PO method demonstrating the weakest performance. Furthermore, the study uncovered that environmental conditions, specifically temperature and solar irradiance, significantly influence the charging process of wireless sensor batteries. These factors have a profound effect on the charging efficiency and rate, which in turn impacts the overall performance and lifespan of the batteries in wireless sensor networks. The study recommends the use of the PSO algorithm for optimizing WSN battery charging processes due to its efficiency, adaptability, and responsiveness to changing conditions. However, for limited computational resources, alternative algorithms like IC or FL can be considered. The study also emphasizes the importance of incorporating environmental variables like temperature and solar irradiance into the battery charging optimization strategy to enhance charging efficiency, improve battery longevity, and enhance overall performance in WSN systems. Despite its computational complexity, PSO offers significant improvements over the basic P&O method. The selection of batteries that can endure a range of weather conditions is essential, especially considering the impact that extreme temperatures can have on battery performance. This research provides valuable insights that can contribute to enhancing the robustness, sustainability, and reliability of wireless sensor network systems, enabling them to operate effectively even in challenging environmental conditions. It offers a comprehensive evaluation of various MPPT algorithms used in photovoltaic energy harvesting-based wireless sensor networks, assessing their performance across different environmental scenarios using MATLAB 2021b.