Dynamic LADRC-Based CFOA-LSTM MPPT optimizer for enhancing Grid-Connected renewable energy sources

Abstract

In the past decade, interest in hybrid photovoltaic/wind energy conversion systems (PV/WECS) has grown due to its nonpolluting and non-depleting nature. However, these power sources rely on ecological circumstances to generate electricity. And the fluctuations of internal variables or computational errors as inner disturbances and the instability of the grid as outer disturbances are generated either via voltage dips or frequency droops. Therefore, this study introduces a dynamic linear active disturbance rejection control (LADRC) strategy for PV/WECS (Low-level control). The system integrates a wind turbine equipped with a permanent magnet synchronous generator (PMSG), which is connected to the electrical grid. Furthermore, to enhance the performance of maximum power point tracking (MPPT), an efficient metaheuristic technique called catch fish optimization algorithm (CFOA) with long short-term memory (LSTM) (CFOA-LSTM) is established (High-level control). Given that the PV/WECS is a complex nonlinear system, an enhanced version of LADRC is developed that utilizes an extended state observer (ESO) for estimating both interior and exterior disturbances, including modeling faults and parameter fluctuations. This approach regulates the DC-Link voltage and controls the active and reactive power by adjusting the utility grid currents to ensure a unity power factor. The simulation findings demonstrate that the proposed approach outperforms conventional control strategies with respect to of speedy tracking and robustness to both interior and exterior disturbances.