African vulture optimizer algorithm based vector control induction motor drive system

Mohammed, Reham H.; Ismaiel, Ahmed M.; Elnaghi, Basem E.; Dessouki, M. El-Shahat

doi:10.11591/ijece.v13i3.pp2396-2408

Cited by 5 publications

(2 citation statements)

References 29 publications

(46 reference statements)

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“…In the first stage, the sliding surface (SS) is established after the control rule is designed in order to guide the regulator to track the reference abruptly [25][26][27][28][29]. The second-order sliding mode controller operates to provide a rapid dynamic response and enhance the stability of the system during the system's transient state [34]. The SS is computed by taking the second derivatives of the error.…”

Section: Second-order Adaptive Fuzzy Logic Controllermentioning

confidence: 99%

The Validation and Implementation of the Second-Order Adaptive Fuzzy Logic Controller of a Double-Fed Induction Generator in an Oscillating Water Column

Elnaghi,

Abelwhab,

Mohammed

et al. 2024

Electronics

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This article presents a second-order adaptive fuzzy logic controller (SO-AFLC) to improve the performance of a grid-connected double-fed induction generator (DFIG) in an oscillating water column power plant (OWCPP). The proposed SO-AFLC was used to improve the maximum power point tracking (MPPT), DC link voltage stability, and reactive power tracking for the DFIG oscillating water column power plant. The SO-AFLC reduces oscillations, overshooting, and mean square error. The SO-AFLC improved the mean square error by 40.4% in comparison to the adaptive fuzzy logic controller (AFLC) and by 84.9% in comparison to the proportional–integral differential controllers (PIDs). To validate the simulation results, an experimental investigation was performed on the Dspace DS 1104 control board. The SO-AFLC shows a faster response time, reduced undershooting, lower peak overshooting, and very low steady-state error in terms of DC link voltage, rotor speed, and maximum power point tracking. Moreover, the integral absolute error (IAE) index of the oscillating water column turbine was calculated. This index is meant to evaluate the SO-AFLC’s feasibility against the PID and AFLC under the same wave conditions.

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Section: Second-order Adaptive Fuzzy Logic Controllermentioning

confidence: 99%

The Validation and Implementation of the Second-Order Adaptive Fuzzy Logic Controller of a Double-Fed Induction Generator in an Oscillating Water Column

Elnaghi,

Abelwhab,

Mohammed

et al. 2024

Electronics

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show abstract

“…6. One idea states that the input/output fuzzy sets are represented by the triangular membership functions (MFs) with overlap (PB) [32], [33]. The fuzzy inference employs 49 control rules to generate the proper signal, as illustrated in Table I.…”

Section: Adaptive Fuzzy Logic Controllermentioning

confidence: 99%

Experimental Validation of Second-Order Adaptive Fuzzy Logic Controller for Grid-Connected DFIG Wind Power Plant

Elnaghi,

Abelwhab,

Abdel-Kader

et al. 2023

IEEE Access

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This paper introduces a second-order adaptive fuzzy logic controller (SO-AFLC) to enhance the characteristics of a doubly fed induction generator (DFIG) inside a grid-tied wind power plant (WPP). SO-AFLCs were utilized to maximize the output of the DFIG wind power plant (WPP) and improve dynamic responsiveness with extremely low mean square error. When comparing the mean square error of SO-AFLC with proportional-integral controllers (PI) and adaptive fuzzy logic controllers (AFLC), the reductions are 87.38% and 40.39%, respectively. This controller prevents overshoots and oscillations. DFIG wind power plant is modeled and simulated using Matlab/Simulink package. Under the unit step wind speed profile, SO-AFLC improved the steady-state error in the Cp waveform by 63.25% compared with the PI controller and 13.12% compared with AFLC. DSpace1104 is used to conduct an experimental investigation to validate the simulation results. In addition, realistic data from the wind farm at RAS Ghareb in the Gulf of Suez, Egypt, are used in this study to achieve more realistic results. Compared to those obtained with PI and AFLC, the results obtained using SO-AFLCs showed fast time response, high convergence rate, reduced peak overshoot, less undershoot, and low steady-state error in terms of power coefficient of the turbine, DC link voltage control, and rotor speed tracking. In addition, a wind turbine performance index based on gross system integral absolute error (IAE) is provided. This index is used to illustrate the SO-AFLC methodology's viability compared to AFLC and PI under the same wind turbine conditions.

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A comprehensive survey of artificial intelligence-based techniques for performance enhancement of solid oxide fuel cells: Test cases with debates

Ashraf,

Draz

2024

Artif Intell Rev

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Since installing solid oxide fuel cells (SOFCs)-based systems suffers from high expenses, accurate and reliable modeling is heavily demanded to detect any design issue prior to the system establishment. However, such mathematical models comprise certain unknowns that should be properly estimated to effectively describe the actual operation of SOFCs. Accordingly, due to their recent promising achievements, a tremendous number of metaheuristic optimizers (MHOs) have been utilized to handle this task. Hence, this effort targets providing a novel thorough review of the most recent MHOs applied to define the ungiven parameters of SOFCs stacks. Specifically, among over 300 attempts, only 175 articles are reported, where thirty up-to-date MHOs from the last five years are comprehensively illustrated. Particularly, the discussed MHOs are classified according to their behavior into; evolutionary-based, physics-based, swarm-based, and nature-based algorithms. Each is touched with a brief of their inspiration, features, merits, and demerits, along with their results in SOFC parameters determination. Furthermore, an overall platform is constructed where the reader can easily investigate each algorithm individually in terms of its governing factors, besides, the simulation circumstances related to the studied SOFC test cases. Over and above, numerical simulations are also introduced for commercial SOFCs’ stacks to evaluate the proposed MHOs-based methodology. Moreover, the mathematical formulation of various assessment criteria is systematically presented. After all, some perspectives and observations are provided in the conclusion to pave the way for further analyses and innovations.

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African vulture optimizer algorithm based vector control induction motor drive system

Cited by 5 publications

References 29 publications

The Validation and Implementation of the Second-Order Adaptive Fuzzy Logic Controller of a Double-Fed Induction Generator in an Oscillating Water Column

The Validation and Implementation of the Second-Order Adaptive Fuzzy Logic Controller of a Double-Fed Induction Generator in an Oscillating Water Column

Experimental Validation of Second-Order Adaptive Fuzzy Logic Controller for Grid-Connected DFIG Wind Power Plant

A comprehensive survey of artificial intelligence-based techniques for performance enhancement of solid oxide fuel cells: Test cases with debates

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