# Design and Analysis of a Hybrid MPPT Method for PV Systems Under Partial Shading Conditions
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**MPPT Algorithm** & **Merits** & **Demerits** \\ \hline Variable Step Size MPPT based on P\&O and IC [25] & Low MPP tracking oscillation, fast MPP tracking, dynamic step size. & Processing load due to continuous calculation of the number of steps. \\ \hline P\&O-MPC Hybrid MPPT [26] & Fast response time, high performance, high efficiency, control without comparison structure. & Processing load due to the number of steps, computational complexity. \\ \hline P\&O and IC MPPT [27] & Low cost system design, easy to apply MPPT, high MPPT accuracy, high efficiency, appropriate to changes in weather conditions. & Special equipment may be required for spatial conditions. \\ \hline P\&O-FSCC Hybrid MPPT [28] & Fast MPP tracking, high efficiency, appropriate for changes in weather conditions, irradiance sensorless structure, intelligent weather forecasting. & Need for fast switching equipment, temporary cancellation of PV panel and possibility of power loss. \\ \hline GWO-IC Hybrid MPPT [29] & Fast MPP tracking, fast response to changes in weather conditions, high efficiency. & Processing load due to continuous calculation of the number of steps, need special parameter selection required. \\ \hline SFLA-IC Hybrid MPPT [30] & Low computational complexity, steady performance. & Processing load due to the number of steps and may require good hardware, special parameter selection required. \\ \hline ANN-MPC-KF Hybrid MPPT [31] & Fast response to changes in weather conditions, low overshoot, high performance, high power output, high efficiency, fast MPP tracking, high MPPT accuracy. & Processing load due to the number of steps and may require good hardware, the ANN algorithm may need to be trained with sufficient data, special parameter selection required. \\ \hline Stochastic Evolutionary Search-Specific HC MPPT [32] & Low cost system design, low computational complexity. & Special parameter selection required. \\ \hline Backstepping-Sliding Mode MPPT [33] & Low MPP tracking oscillation, low overshoot, high performance. & Computational complexity, processing load due to the number of steps and may require good hardware. \\ \hline Hybrid Two-Stage Global MPPT [34] & Fast response to changes in weather conditions, low energy usage, appropriate to changes in weather conditions. & The ANN algorithm may need to be trained with sufficient data. \\ \hline Improvised Binary Sequence-P\&O Hybrid MPPT [35] & Fast response to changes in weather conditions, appropriate to changes in weather conditions, precise MPP tracking. & Special limitation selection required. \\ \hline WO-Pattern Search Algorithm based on ANFIS [36] & High power output, high efficiency, fast MPP tracking. & Computational complexity, the ANFIS algorithm may need to be trained with sufficient data, special parameter selection required. \\ \hline GA based FOCV MPPT [37] & Fast MPP tracking, high efficiency, low MPP tracking oscillation. & Computational complexity, special parameter selection required. \\ \hline DE-Feed Forward Neural Network Hybrid MPPT [38] & Fast MPP tracking, high efficiency, precise MPP tracking. & Computational complexity, the Feed Forward Neural Network algorithm may need to be trained with sufficient data, processing load due to the number of steps and may require good hardware. \\ \hline \hline ANFIS based CPS-IC Hybrid MPPT [39] & High output power, low sampling rate, low power ripple. & Computational complexity, the ANFIS algorithm may need to be trained with sufficient data, special parameter selection required. \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._