Open AccessOptimal controller tuning for P&O maximum power point tracking of PV systems using genetic and cuckoo search algorithms
Author(s) -
Ahmed Nabil A.,
Abdul Rahman Salahuddin,
Alajmi Bader N.
Publication year - 2021
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12624
Subject(s) - maximum power point tracking , cuckoo search , photovoltaic system , control theory (sociology) , maximum power principle , transient (computer programming) , robustness (evolution) , computer science , algorithm , engineering , inverter , voltage , particle swarm optimization , control (management) , artificial intelligence , electrical engineering , biochemistry , chemistry , gene , operating system
Background Tracking the maximum power point (MPPT) of photovoltaic (PV) systems is essential to increase the utilization efficiency. One of the most widely used algorithms is perturb and observe (P&O) technique. Methods In this paper, tuning algorithms based on genetic algorithm (GA) and cuckoo search (CS) are proposed to provide optimal controller parameters for P&O MPPT. This optimal tuning is crucial in P&O MPPT applications of PV systems where it is difficult to tune the controllers using conventional tuning techniques to attain good performance because of the wide variations in the system parameters and its nonlinearity. The proposed tuning algorithms are used to overcome the drawbacks of the classical P&O MPPT under rapidly changing atmospheric conditions such as oscillation around the maximum power point (MPP) and low convergence speed. Results The robustness of the algorithms is confirmed against fast varying insolation proving its ability to track the MPP in case of random and fast changing atmospheric conditions. An experimental set up of a 1 kW PV system is carried out and implemented using dSPACE DS1103 controller board and E4360 Keysight modular solar array emulator to validate the performance of the proposed optimal tuning algorithms. The measured results are in a close agreement with the simulation and the analytical system. Conclusion Experimental and simulation results demonstrate a superior improvement in the transient and steady‐state performances and in the maximum power tracking efficiency. The steady‐state and the transient efficiencies of the presented work are 99.95% and 94.02% which are almost 17.59% and 10.61%, respectively superior to the classical P&O one and are 14.66% and 1.52%, respectively as compared to the manual tuning approach. This proposed optimal tuning approaches can be used to enhance the performance of any other controllers.