Solar photovoltaic (PV) cells play a major role as natural, renewable energy sources. It is characterized by having nonlinear photoelectric voltage and current characteristics. These properties depend on the amount of solar radiation and temperature. PV can be used as an electrical charge circuit. But due to the low efficiency of the resulting photoelectric power, it should operate in conditions of maximum power point. There are several algorithms for achieving this maximum power point condition. In this paper, a PV system is proposed to obtain the maximum power point using a modified firefly algorithm. The modifications have been made both in fireflies’ locations and their random movement. Several simulations are implemented using MATLAB to verify the performance of the proposed system. From the simulation results, the proposed algorithm outperforms all traditional algorithms such as firefly and perturbation and observation technique. Moreover, the impacts of some variants of the proposed technique are studied. The variants are the number of the fireflies, the randomness, the maximum iterations, and the effect of changing the sampling time. A proposed modified firefly is presented with an MPPT controller in the PV system to ensure operating the PV at the MPP. Additionally, the mathematical expressions are explained. Moreover, MATLAB simulation programs are done to compare the performance of the proposed scheme with other related ones.
Recent research has focused on photovoltaic (PV) systems due to their important properties. The efficiency of the PV system can be enhanced by many Maximum Power Point Tracking (MPPT) algorithms proposals. MPPT algorithms are used to achieve maximum PV output power by optimizing the duty cycle of the DC–DC buck/boost converter. This paper introduces an RNA algorithm as an efficient MPPT algorithm for the photovoltaic system. This proposed RNA algorithm consists of two main segments. The first segment is an artificial neural network for generating reference power. The second segment is a proposed Recursive Bit Assignment (RBA) network to allow variable step size of the boost converter duty cycle. The instant PV power adopts the RBA network to produce the variable duty cycle increment value. Additionally, the neural network is implemented in such a way to obtain the best performance. Many simulation results using MATLAB to test the system performance are presented. The performance characteristics of the photovoltaic system with variable irradiance and variable temperature are simulated. From results, the proposed RNA algorithm achieves fast tracking time, high energy efficiency, true maximum power point and acceptable ripple. Additionally, comparisons between the RNA algorithm and other related algorithms such as Perturb and Observe, the Neural Network and the Adaptive Neural Inference System Algorithms are executed. The proposed RNA algorithm achieves the best performance in all case studies such as; irradiance profile variation, severe temperature and irradiance diversions, and partial shading conditions. Besides, the experimental circuit of the PV system is also presented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.