Analysis and Evaluation Performance of MPPT Algorithms: Perturb & Observe (P&O), Firefly, and Flower Pollination (FPA) in Smart Microgrid Solar Panel Systems

Abstract

There are various factors that can affect the performance quality of solar panel systems, one of them is the use of Artificial Intelligence (AI) algorithms. The AI algorithm can be used in switching converter control systems to achieve the Maximum Power Point Tracking (MPPT) position from the power characteristic curve of solar panel voltage. The use of an appropriate AI algorithm will support tracking quality or assessment of the duty cycle. Changes in the value of the duty cycle will affect the solar power output value of the solar panel. Tracking the accurate duty cycle will produce the most optimal output of solar power. To support system performance, the algorithm used as MPPT must have a short tracking rate or convergence time. To find out which algorithm is the best, a comparison is made by simulating a solar panel system which is then connected to a smart microgrid network. Testing of solar panel systems is carried out under varying irradiation conditions. In this study, simulations were carried out with several different algorithms to produce maximum power values. Based on the results of the test, it can be concluded that the FPA algorithm is considered the most optimal with the largest output power of 140.81 watts. In addition, the FPA algorithm also succeeded in becoming the fastest method of finding power with a maximum time of 0.02 seconds. The final results of the study can be used to support the improvement in the quality of performance of the smart microgrid solar panel system.