# Design and Analysis of a Hybrid MPPT Method for PV Systems Under Partial Shading Conditions
## 4 Conclusions  
In this study, a new hybrid MPPT method that combines the Perturb & Observe and Particle Swarm Optimization algorithms has been successfully developed and tested in MATLAB/Simulink. The proposed MPPT method leverages the strengths of both P&O and PSO techniques to achieve faster convergence, lower voltage undershoots, and higher tracking accuracy. The proposed method is validated through simulations and compared with the conventional P&O and P&O-Kalman Filter Hybrid MPPT methods under varying irradiance conditions. The obtained results demonstrate that the proposed hybrid approach significantly improves the tracking performance and reference voltage accuracy, especially under partial shading conditions. The proposed hybrid MPPT algorithm has been tested by running it with two other simulation scenarios, namely the PV system with the Perturb & Observe algorithm and the PV system using the Perturb & Observe and Kalman Filter Hybrid MPPT algorithm. The proposed method and other two simulation studies have been tested in the MATLAB/Simulink simulation program using 1000 W/m\({}^{2}\), 500 W/m\({}^{2}\), 600 W/m\({}^{2}\), 900 W/m\({}^{2}\) and 800 W/m\({}^{2}\) irradiance values, respectively. In the Perturb & Observe MPPT algorithm, the undershoot voltage is determined as 3 V. In the Perturb & Observe and Kalman Filter Hybrid MPPT method, the undershoot voltage is seen to be 2 V. In the proposed Perturb & Observe and Particle Swarm Optimization Hybrid MPPT method, a lower undershoot voltage compared to the others is determined as 1.5 V. When the simulated methods are examined, it is seen that the convergence times for the Perturb & Observe MPPT algorithm, Perturb & Observe and Kalman Filter Hybrid MPPT method, and the proposed Perturb & Observe and Particle Swarm Optimization Hybrid MPPT method are 8 ms, 6 ms, and 5 ms, respectively. These improvements make it highly suitable for practical PV systems operating under partial shading conditions. In addition to these simulation studies, the proposed hybrid method has been tested with real solar irradiance and temperature data. In simulations with real solar irradiance data, the proposed method performs more electric power production. This paper contributes to the literature by expressing some limitations of the proposed method. Under changing irradiance conditions, steady state oscillation can be observed in the Perturb & Observe method. In addition, the Particle Swarm Optimization algorithm may create excessive processing load on hardware and may require more powerful hardware setups. Finally, parameter selection is the most important part for PSO algorithm, and if the selection is not performed well, local MPP points may be detected incorrectly. Future work will focus on hardware implementation to further validate the proposed method. The use of more advanced power converters and grid integration is planned. In addition, the proposed method will be tested by using it in newly installed PV plants. The method performance will be improved by better tuning the control and algorithm parameters to improve the performance. O.T. and B.K.U. ; writing--review and editing, O.T. and B.K.U. ; visualization, O.T. and B.K.U. ; supervision, O.T. ; project administration, O.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available on request from the corresponding author. The authors declare no conflicts of interest.