Belgacem Toual

This paper presents a new method based on Artificial Intelligence (AI), for the optimal control of a bidirectional converter. The goal is to improve the DC bus voltage efficiency of a grid-independent hybrid system. The proposed design consists of a Photovoltaic System (PVS), a Wind Energy Conversion System (WECS), a Battery Storage System (BSS), and a variety of electronic power devices to extract the maximum energy from the generation sources. A bidirectional converter is featured in the use of an Adaptive Network-based Fuzzy Inference System (ANFIS) based controller, to improve the energy quality of the DC bus. The proposed approach provided good results compared to the Proportional Integral (PI) controller and the Fuzzy Logic controller (FLC). The use of this approach reduced the value of oscillations, improved the response time of the system, and contributed to the elimination of the problem of overshoot. This experiment was conducted using Matlab and Simulink.

This paper describes the management of a standalone hybrid energy system (HES) based on two renewable sources. The sun, wind. and the battery supports the system. Solar panels and wind turbines (WT) connected with permanent magnet synchronous generators (PMSG) were used for power production. Power converters have been used together with control algorithms for efficient power management. The filters were used to dispose of the largest amount of harmonics in the inverter. The proposed model provides a strategy for managing energy under various generating conditions. In order to save energy for a fixed load. Proposed hybrid accession simulated with MATLAB/Simulink

This paper proposes a novel hybrid control strategy that integrates Feedback Linearization Control (FBLC) with Sliding Mode Control (SMC) to significantly enhance the performance of Permanent Magnet Synchronous Motor (PMSM) drives. The proposed control strategy leverages the strengths of both FBLC and SMC to address the inherent challenges associated with PMSM control in demanding industrial applications. The FBLC component of the hybrid controller effectively linearizes the nonlinear dynamics of PMSMs. By transforming the nonlinear system into an equivalent linear system, FBLC facilitates precise trajectory tracking and improves transient response, thereby ensuring high control accuracy. This linearization process simplifies the control design and enables the implementation of advanced linear control techniques. On the other hand, the SMC component ensures robustness and reliability of the PMSM drive system. SMC is known for its inherent robustness against parameter variations, uncertainties, and external disturbances. By incorporating SMC into the hybrid controller, the system maintains stable and reliable operation even in the presence of these adverse conditions. The SMC component enhances disturbance rejection capabilities, providing a robust control solution that significantly improves the overall system performance. The integration of FBLC and SMC into a unified control architecture results in a synergistic improvement in PMSM drive performance. The hybrid FBLC-SMC controller combines the precise tracking capabilities of FBLC with the robustness of SMC, leading to superior tracking accuracy, effective disturbance rejection, and enhanced overall robustness compared to traditional control methods. Extensive simulation studies are conducted to validate the effectiveness of the proposed hybrid control strategy. The simulation results demonstrate the ability of the FBLC-SMC controller to achieve excellent performance metrics, including improved tracking accuracy, faster transient response, and robust disturbance rejection. The hybrid control strategy is shown to be a promising solution for industrial applications requiring high performance and reliability in PMSM control.

Hybrid Renewable Energy Sources (HRES) integrated into a microgrid (MG) are a cost-effective and convenient solution to supply energy to off-grid and rural areas in developing countries. This research paper focuses on the optimization of an HRES connected to a stand-alone microgrid system consisting of photovoltaics (PV), wind turbines (WT), batteries (BT), diesel generators (DG), and inverters to meet the energy demand of fifteen residential housing units in the city of Djelfa, Algeria. In this context, the multiobjective salp swarm algorithm (MOSSA), which is among the latest nature-inspired metaheuristic algorithms recently introduced for hybrid microgrid system (HMS) optimization, has been proposed in this paper for solving the optimization of an isolated HRES. The proposed multiobjective optimization problem takes into account the cost of energy (COE) and loss of power supply probability (LPSP) as objective functions. The proposed approach is applied to determine three design variables, which are the nominal power of photovoltaic, the number of wind turbines, and the number of battery autonomy days considering higher reliability and minimum COE. In order to perform the optimum size of HMG, MOSSA is combined with a rule-based energy management strategy (EMS). The role of EMS is the coordination of the energy flow between different system components. The effectiveness of using MOSSA in addressing the optimization issue is investigated by comparing its performance with that of the multiobjective dragonfly algorithm (MODA), multiobjective grasshopper optimization algorithm (MOGOA), and multiobjective ant lion optimizer (MOALO). The MATLAB environment is used to simulate HMS. Simulation results confirm that MOSSA achieves the optimum system size as it contributed 0.255 USD/kW h of COE and LPSP of 27.079% compared to MODA, MOGOA, and MOALO. In addition, the optimization results obtained using the proposed method provided a set of design solutions for the HMS, which will help designers select the optimal solution for the HMS.