article · PLoS ONE
Solar photovoltaic systems face challenges in maintaining peak power generation as weather conditions like temperature and sunlight irradiance shift. Maximum power point tracking techniques help address this variability. An adaptive control method, known as INC-FST, improves the classical incremental conductance algorithm by incorporating a proportional-integral-derivative controller updated using fuzzy self-tuning logic. Designed for grid-connected solar installations, this mechanism dynamically adjusts the boost converter signal between the direct-current solar array output and the grid inverter. When tested across three distinct climate scenarios against conventional approaches such as perturb and observe, standard incremental conductance, and fuzzy logic incremental conductance, the system reached operating efficiencies of 99.80 percent, 99.76 percent, and 99.73 percent. It also improved overall control precision by reducing rise times, minimising overshoot, and extracting greater power.
Solar panels generate variable amounts of electricity as weather shifts throughout the day. Improving control systems allows solar arrays to continuously capture the maximum available energy and feed it stably into the power grid. By reducing energy losses and response delays, advanced tracking algorithms can make renewable power generation more efficient and reliable.
This control technique could be integrated into grid-tied solar inverters and power conversion systems by renewable energy technology developers. It improves solar power harvesting under changing weather conditions. The research represents an applied algorithm that has been evaluated across distinct climate conditions against conventional tracking techniques, placing it at the technical testing and validation stage rather than as a deployed commercial solution.
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Solar energy, a prominent renewable resource, relies on photovoltaic systems (PVS) to capture energy efficiently. The challenge lies in maximizing power generation, which fluctuates due to changing environmental conditions like irradiance and temperature. Maximum Power Point Tracking (MPPT) techniques have been developed to optimize PVS output. Among these, the incremental conductance (INC) method is widely recognized. However, adapting INC to varying environmental conditions remains a challenge. This study introduces an innovative approach to adaptive MPPT for grid-connected PVS, enhancing classical INC by integrating a PID controller updated through a fuzzy self-tuning controller (INC-FST). INC-FST dynamically regulates the boost converter signal, connecting the PVS's DC output to the grid-connected inverter. A comprehensive evaluation, comparing the proposed adaptive MPPT technique (INC-FST) with conventional MPPT methods such as INC, Perturb & Observe (P&O), and INC Fuzzy Logic (INC-FL), was conducted. Metrics assessed include current, voltage, efficiency, power, and DC bus voltage under different climate scenarios. The proposed MPPT-INC-FST algorithm demonstrated superior efficiency, achieving 99.80%, 99.76%, and 99.73% for three distinct climate scenarios. Furthermore, the comparative analysis highlighted its precision in terms of control indices, minimizing overshoot, reducing rise time, and maximizing PVS power output.
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DOI: 10.1371/journal.pone.0293613
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