Abstract
The partial shading of photovoltaic (PV) modules due to clouds or blocking objects, such as buildings or tree leaves, is a common problem for photovoltaic systems. To address this, maximum power point tracking (MPPT) is implemented to find the global maximum power point (GMPP). In this paper, a new hybrid MPPT is proposed that combines a modified grasshopper optimization algorithm (GOA) with incremental conductance (IC). In the first stage, the proposed modified GOA is implemented to find a suitable tracking area where the GMPP is located. Then the system moves to the second stage by implementing IC to get the correct GMPP. IC is a fast-performing and reliable algorithm. By combining GOA and IC, the proposed method can find the GMPP accurately with a short tracking time. Various experimental results show that the proposed method yields the highest tracking efficiency and lowest tracking time compared to some of the state-of-the-art MPPT algorithms, such as particle swarm and modified firefly optimizations.
Highlights
IntroductionTo help reduce the adverse effects of fossil fuels, renewable energy has grown rapidly worldwide
To help reduce the adverse effects of fossil fuels, renewable energy has grown rapidly worldwide.One kind of renewable energy is solar energy providing abundant resources and emission-free.The solar energy can be categorized into concentrated solar power (CSP) and photovoltaics (PVs).CSP employs solar thermal power to generate electricity
This paper proposes a hybrid algorithm the proposed modified grasshopper optimization algorithm (GOA) and the
Summary
To help reduce the adverse effects of fossil fuels, renewable energy has grown rapidly worldwide. The weakness of conventional maximum power point tracking (MPPT) algorithms (e.g., perturb and observe (P&O) [4], incremental conductance (IC) [5], and golden section search (GSS) [6]) is that they are trapped in local maxima, as they generally use a deterministic approach to track the maximum point Those aforementioned methods will lead to power loss and low efficiency. Since the meta-heuristic approaches employ random variable to escape from trapping in a local optimum, the final convergent point will be different every time the algorithm is executed, even though the same initial values are used To avoid such a problem, it is better to first use a metaheuristic approach to locate the area where the GMPP is located, and use a deterministic method to pinpoint the exact GMPP.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
