An intelligent Hybrid Energy Management Control model (HEMC) is utilized in grid integrates hybrid renewable energy system. This system incorporates a primary source in the form of a solar photovoltaic (PV), followed by wind power, storage element consisting of a Super Capacitor and supporting element of grid. To ensure optimal operation, a perceptive mode-based controller is employed in HEMC model, which utilizes a Shuffled Frog Leaping Algorithm with trained Artificial Neural Network (SFLA-ANN). The objective function is to minimize the transient power oscillations and settling time during static and dynamic load change condition with different environmental climatic conditions. To evaluate its performance, it is compared with existing algorithms such as Sliding Mode Controller (SMC), Shuffled Frog Leaping Algorithm (SFLA) and Sliding Mode Controller ‒ Artificial Neural Network (SMC-ANN), which have been implemented using the Simulink/ MATLAB Platform. The analytical output response of HEMC presented and reflects significant performance improvement of the proposed controller is tested under both static and dynamic load conditions.
Sliding Mode Controller, Super Capacitor, Photovoltaic Cell, ANN, Shuffled Frog Leaping Algorithm.
A. K. SURESH, G. Giftson SAMUEL, "Hybrid SFLA-ANN Method for Effective Power Management of Hybrid Power Sources in a Variety of Weather Scenarios", Studies in Informatics and Control, ISSN 1220-1766, vol. 33(2), pp. 27-38, 2024. https://doi.org/10.24846/v33i2y202403
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