Photovoltaics is the branch of technology concerned with the production of electric current at the junction of two substances. The most common application involves the conversion of light into electricity using semiconducting materials that exhibit photovoltaic effects. In practice, it has been established that the output power of photovoltaic systems changes nonlinearly when subjected to variations in solar irradiations and ambient temperature. Consequently, researchers have resulted in convectional algorithms for maximum power point tracking (MPPT). Unfortunately, such convectional algorithms become inadequate to resolve the incumbent hot-spot issue which arise when the photovoltaic system in under partial shading condition. To overcome this shortfall, bypass diodes are often connected in parallel to the group of cells. In general, conventional MPPT algorithms might frequently fail to identify the global maximum power point among the numerous local maximum power points, and therefore, the PV system fails to operate at its maximum capacity. More often than not, most solar MPPT algorithms, including the firefly algorithm (FA) and other nature-inspired algorithms, normally require sampling processes that change the operating voltage or current and check the corresponding output power to determine the best operating point along the power-voltage curve.
To date, it has already been established that by using a smaller sampling step-size often provides better accuracy, but it slows tracking speed, while as a larger step-size in the change of a position causes a faster response, but it also increases the oscillations around the maximum power point (MPP) and reduces the accuracy. Therefore, it is imperative that such redundant propagations/samplings be omitted to accelerate the tracking speed while easing the oscillations around the MPP. In this view, a team of researchers from the National Quemoy University in Taiwan: Professor Yu-Pei Huang and his graduate students Ming-Yi Huang and Cheng-En Ye developed a fusion firefly algorithm (FFA) with a novel simplified propagation process (SPP) for photovoltaic systems under partial shading conditions. Their work is currently published in the research journal, IEEE Transactions on Sustainable Energy.
In their approach, the performance of the proposed FFA was evaluated and compared with the conventional firefly algorithm, neighborhood attraction firefly algorithm and simplified firefly algorithm using MATLAB software simulation and a hardware evaluation system. In addition, for comparison, the tracking efficiency improvement and performance influences of the proposed SPP process for reducing the sampling events were evaluated by adding or removing it from the FFA and the conventional FA algorithm.
The authors reported that by integrating the neighborhood attraction firefly algorithm and simplified firefly algorithm, the proposed FFA was capable of tracking the global maximum power points with high accuracy. Moreover, the proposed SPP process was seen to reduce the sampling events by omitting redundant propagations, thereby accelerating the tracking speed and reducing the energy loss and oscillations during the sampling process.
In summary, the study introduced the fusion firefly algorithm with a novel simplified propagation process in order to ease the oscillations and reduce redundant samplings during the MPPT tracking process for PV systems under PSCs. Remarkably, the study showed that the proposed FFA algorithm with SPP process could offer high accuracy, low energy loss, and high efficiency with rapid tracking speed under different PSCs. In a statement to Advances in Engineering, Professor Yu-Pei Huang highlighted that the proposed SPP process was capable of significantly reducing the sampling events not only when integrating it with the FFA, but also with other conventional FA algorithms.
Yu-Pei Huang, Ming-Yi Huang, Cheng-En Ye. A Fusion Firefly Algorithm with Simplified Propagation for Photovoltaic MPPT under Partial Shading Conditions. IEEE Transactions on Sustainable Energy