Examinando por Autor "Alharbi, Mohammed"
Mostrando 1 - 4 de 4
- Resultados por página
- Opciones de ordenación
Ítem Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation(MDPI, 2022-11-11) Hussien, Ahmed M.; Kim, Jonghoom; Alkuhayli, Abdulaziz; Alharbi, Mohammed; Hasanien, Hany M.; Tostado-Véliz, Marcos; Turky, Rania A.; Jurado-Melguizo, FranciscoThe present research produces a new technique for the optimum operation of an isolated microgrid (MGD) based on an enhanced block-sparse adaptive Bayesian algorithm (EBSABA). To update the proportional-integral (PI) controller gains online, the suggested approach considers the impact of the actuating error signal as well as its magnitude. To reach a compromise result between the various purposes, the Response Surface Methodology (RSMT) is combined with the sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms. To demonstrate the success of the novel approach, a benchmark MGD is evaluated in three different Incidents: (1) removing the MGD from the utility (islanding mode); (2) load variations under islanding mode; and (3) a three-phase fault under islanding mode. Extensive simulations are run to test the new technique using the PSCAD/EMTDC program. The validity of the proposed optimizer is demonstrated by comparing its results with those obtained using the least mean and square root of exponential method (LMSRE) based adaptive control, SFO, and PSO methodologies. The study demonstrates the superiority of the proposed EBSABA over the LMSRE, SFO, and PSO approaches in the system’s transient reactions.Ítem Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter(MDPI, 2023-07) Fahmy, Hend M.; Swief, Rania A.; Hasanien, Hany M.; Alharbi, Mohammed; Maldonado-Ortega, José Luis; Jurado-Melguizo, FranciscoThis paper establishes an accurate and reliable study for estimating the lithium-ion battery’s State of Charge (SoC). An accurate state space model is used to determine the parameters of the battery’s nonlinear model. African Vultures Optimizers (AVOA) are used to solve the issue of identifying the battery parameters to accurately estimate SoC. A hybrid approach consists of the Coulomb Counting Method (CCM) with an Adaptive Unscented Kalman Filter (AUKF) to estimate the SoC of the battery. At different temperatures, four approaches are applied to the battery, varying between including load and battery fading or not. Numerical simulations are applied to a 2.6 Ahr Panasonic Li-ion battery to demonstrate the hybrid method’s effectiveness for the State of Charge estimate. In comparison to existing hybrid approaches, the suggested method is very accurate. Compared to other strategies, the proposed hybrid method achieves the least error of different methods.Ítem Optimal Model Predictive Control for Virtual Inertia Control of Autonomous Microgrids(MDPI, 2023-03) Saleh, Amr; Hasanien, Hany M.; Turky, Rania A.; Turdybek, Balgynbek; Alharbi, Mohammed; Jurado-Melguizo, Francisco; Omran, Walid A.For the time being, renewable energy source (RES) penetration has significantly increased in power networks, particularly in microgrids. The overall system inertia is dramatically decreased by replacing traditional synchronous machines with RES. This negatively affects the microgrid dynamics under uncertainties, lowering the microgrid frequency stability, specifically in the islanded mode of operation. Therefore, this work aims to enhance the islanded microgrid frequency resilience using the virtual inertia frequency control concept. Additionally, optimal model predictive control (MPC) is employed in the virtual inertial control model. The optimum design of the MPC is attained using an optimization algorithm, the African Vultures Optimization Algorithm (AVOA). To certify the efficacy of the proposed controller, the AVOA-based MPC is compared with a conventional proportional–integral (PI) controller that is optimally designed using various optimization techniques. The actual data of RES is utilized, and a random load power pattern is applied to achieve practical simulation outcomes. Additionally, the microgrid paradigm contains battery energy storage (BES) units for enhancing the islanded microgrid transient stability. The simulation findings show the effectiveness of AVOA-based MPC in improving the microgrid frequency resilience. Furthermore, the results secure the role of BES in improving transient responses in the time domain simulations. The simulation outcomes are obtained using MATLAB software.Ítem Parameter Identification of Lithium-Ion Battery Model Based on African Vultures Optimization Algorithm(MDPI, 2023-05-08) Fahmy, Hend M.; Sweif, Rania A.; Hasanien, Hany M.; Tostado-Véliz, Marcos; Alharbi, Mohammed; Jurado-Melguizo, FranciscoThis paper establishes a study for an accurate parameter modeling method for lithium-ion batteries. A precise state space model generated from an equivalent electric circuit is used to carry out the proposed identification process, where parameter identification is a nonlinear optimization process problem. The African vultures optimization algorithm (AVOA) is utilized to solve this problem by simulating African vultures’ foraging and navigating habits. The AVOA is used to implement this strategy and improve the quality of the solutions. Four scenarios are considered to take the effect of loading, fading, and dynamic analyses. The fitness function is selected as the integral square error between the estimated and measured voltage in these scenarios. Numerical simulations were executed on a 2600 mAhr Panasonic Li-ion battery to demonstrate the effectiveness of the suggested parameter identification technique. The proposed AVOA was fulfilled with high accuracy, the least error, and high closeness with the experimental data compared with different optimization algorithms, such as the Nelder–Mead simplex algorithm, the quasi-Newton algorithm, the Runge Kutta optimizer, the genetic algorithm, the grey wolf optimizer, and the gorilla troops optimizer. The proposed AVOA achieves the lowest fitness function level of the scenarios studied compared with relative optimization algorithms.