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Abstract
Nowadays, owing to the growing interest in renewable energy, Photovoltaic systems (PV) are responsible of supplying more than 500,000 GW of the electrical energy consumed around the world. Therefore, different converters topologies, control algorithms, and techniques have been studied and developed in order to maximize the energy harvested by PV sources. Maximum Power Point Tracking (MPPT) methods are usually employed with DC/DC converters, which together are responsible for varying the impedance at the output of photovoltaic arrays, leading to a change in the current and voltage supplied in order to achieve a dynamic optimization of the transferred energy. MPPT algorithms such as, Perturb and Observe (P&O) guarantee correct tracking behavior with low calibration parameter dependence, but with a compromised relation between the settling time and steady-state oscillations, leading to a trade off between them. Nevertheless, proposed methods like Particle Swarm Optimization- (PSO) based techniques have improved the settling time with the addition of lower steady-state oscillations. Yet, such a proposal performance is highly susceptible and dependent to correct and precise parameter calibration, which may not always ensure the expected behavior. Therefore, this work presents a novel alternative for MPPT, based on the Earthquake Optimization Algorithm (EA) that enables a solution with an easy parameters calibration and an improved dynamic behavior. Hence, a boost converter case study is proposed to verify the suitability of the proposed technique through Simscape Power Systems™ simulations, regarding the dynamic model fidelity capabilities of the software. Results show that the proposed structure can easily be suited into different power applications. The proposed solution, reduced between 12% and 36% the energy wasted in the simulation compared to the P&O and PSO based proposals.
Highlights
In power electronics systems, energy generation through renewable sources has gained greater importance in an endless number of industrial and social applications
The premature convergence of the algorithm has been studied in [11], where the results show how the classic Particle Swarm Optimization (PSO)-based solution can fall into a local solution or induce greater oscillations due to the required re-initialization of the algorithm after irradiation changes, leading to conclude that the algorithm may not always ensure the expected tracking behavior
The quantification of the results showed that after the performed scenarios the Earthquake Optimization Algorithm (EA)-based Maximum Power Point Tracking (MPPT) consistently managed to harvest the most energy, with a lower standard deviation regarding the voltage at Maximum Power Point (MPP), having been reduced between 12% and 36% of the energy wasted in the 10 s of simulations compared to both solutions
Summary
Energy generation through renewable sources has gained greater importance in an endless number of industrial and social applications. Together the MPPT algorithms with the DC/DC converters are responsible for varying the impedance at the output of photovoltaic arrays, leading to a change in the current and voltage they supply, in order to achieve a dynamic optimization of the transferred energy, as explained in [1]. S-wave equation, in order to address the dynamic optimization issue of the MPPT for solar cells, allowing to transfer the fine search properties of the algorithm to this new solution, achieving a solution that improves energy harvesting. The quantification of the results showed that after the performed scenarios the EA-based MPPT consistently managed to harvest the most energy (measured through the power through the simulation time), with a lower standard deviation regarding the voltage at MPP, having been reduced between 12% and 36% of the energy wasted in the 10 s of simulations compared to both solutions.
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