Design and Analysis of Sliding-Mode Artificial Neural Network Control Strategy for Hybrid PV-Battery-Supercapacitor System

Autor: Mohamed Ali Zdiri, Tawfik Guesmi, Badr M. Alshammari, Khalid Alqunun, Abdulaziz Almalaq, Fatma Ben Salem, Hsan Hadj Abdallah, Ahmed Toumi
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Energies, Vol 15, Iss 11, p 4099 (2022)
Druh dokumentu: article
ISSN: 1996-1073
DOI: 10.3390/en15114099
Popis: Nowadays, the growing integration of renewable energy sources poses several challenges to electrical energy systems. The latter need be controlled by grid rules to ensure their stability and maintain the efficiency of renewable energy consumption. In this context, a novel HESS (hybrid energy storage system) control strategy, combining the PV (photovoltaic) generator with FLC (fuzzy logic control), SC (super-capacitor), and lithium-ion battery modules, is advanced. The proposed energy control rests on monitoring of the low-frequency and high-frequency electrical power components of the mismatch between power demand and generation, while applying the error component of the lithium-ion battery current. On accounting for the climatic condition and load variation considerations, the SC undertakes to momentarily absorb the high-frequency power component, while the low-frequency component is diverted to the lithium-ion battery. To improve the storage system’s performance, lifetime, and avoid load total disconnection during sudden variations, we consider equipping the envisioned energy control design with controllers of SM and ANN types. The MATLAB/Simulink based simulation results turn out to testify well the investigated HESS control scheme’s outstanding performance and efficiency in terms of DC bus voltage rapid regulation, thereby enhancing the battery’s lifetime and ensuring the PV system’s continuous flow.
Databáze: Directory of Open Access Journals
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