Optimized maximum power point tracker for partially shaded PV system: adaptive duty cycle control

“Under partial shade, the power–voltage (P–V) curve of solar arrays has numerous peaks.” Normally, differences in solar irradiance as well as temperature dictate the PV module's dynamic characteristics, as they tend to affect the PV array's behavior. When there is a constant shift in irradiance or temperature, or when partial shade occurs, the output power of a PV array changes. The main drawbacks of power tracking methods under PSCs includes low accuracy, extracting the local maxima instead of the actual global maxima, slow rate of tracking, highly complex to implement, & high oscillation around the tracked maximum power. Hence, to overcome this, a methodology is proposed in this paper, which has an NN for partial shading detection, followed by the P–V curve segmentation and an optimization based MPPT. The NN is one of the best techniques for mapping input–output nonlinear functions with voltage & current samples of the PV array by which it can detect the partial shading of any random data with high accuracy. The complications associated with the tracking of the MPP of a PV array under PSCs makes an effort to utilize the optimized MPPT method. In order to solve this, a new hybrid algorithm which is named as Salp Swarm Insisted Moth Flame (SSI-MF) Algorithm that incorporates both the concept of Salp Swarm Algorithm (SSA) and Moth Flame Optimization (MFO). Therefore, the proposed method helps to enhance the MPPT capability for photovoltaic system by adapting the duty cycle as well in which the duty cycle gets updated automatically with respect to the Moth’s/Salp’s position. Finally, the proposed hybrid algorithm for the PV System together with boost converter was executed in MATLAB/Simulink as well as their outcomes are analyzed for three different levels of PSCs such as zero shading, weak shading and severe shading whose results are compared with respect to tracking error, cost function and tracking time.