Mohamed Metwally Mahmoud
Aswan University

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A Combination of INC and Fuzzy Logic-Based Variable Step Size for Enhancing MPPT of PV Systems Ouassa Mohammed Lamine; Noureddine Bessous; Borni Abdelhalim; Fahd A. Banakhr; Mohamed I. Mosaad; Mammeri Oussama; Mohamed Metwally Mahmoud
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1428

Abstract

The significance of using the variable step Incremental Conductance (INC) technique in Maximum Power Point monitoring (MPPT) of photovoltaic (PV) systems resides in its capacity to improve the efficiency of energy conversion. This is accomplished through the constant measurement and comparison of incremental changes in current and voltage, precisely monitoring the maximum power point amidst changing environmental conditions. This traditional INC-MPPT approach has two primary disadvantages. Initially, it employs a predetermined scaling factor that necessitates human adjustment. Furthermore, it adjusts the inclination of the PV characteristics curve to modify the step size. This implies that even little changes in PV module voltage will have a substantial impact on the total step size. As a result, it shifts the operating point away from the intended reference maximum power point. The objective of this work is to improve the efficiency of traditional INC by overcoming the constraints associated with step size modifications. This is achieved by using a fuzzy logic (FL) technique to adjust the step size adaptively in response to environmental changes. The presented INC-FL-MPPT successfully achieves MPPT for a PV system under enhanced steady-state and transient-state settings. The results demonstrate the superiority of the suggested approach compared to three distinct MPPT strategies, namely Perturb and Observe (PO), Classical INC, and PO-FL technique.
Genetic Algorithm Tuned Controllers for High-Performance Indirect Field-Oriented Control in DFIG-Based WECS Samira Heroual; Belkacem Belabbas; Kheloud Ayati; Rabia Haloui; Ahmed Tawfik Hassan; Alfian Ma’arif; Mohamed Metwally Mahmoud; Vojtech Blazek
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.15529

Abstract

Due to rising environmental awareness, rising fuel prices, and increasing power consumption, wind power is currently the world's fastest-growing electricity source. One essential form of renewable energy generation is wind energy conversion using a Doubly Fed Induction Generator (DFIG). Moreover, DFIGs are the best option, as wind turbines with variable speeds often have substantial megawatt capacity. Their cost-effectiveness, high operational efficiency, adaptable control mechanisms, and capacity to autonomously regulate the exchange of active and reactive power are the reasons for this selection. Classical control, which is based on PI regulators and employs several loops, is the most popular control approach that makes use of the indirect field-oriented vector method. In order to ensure stability across the whole speed range, it also requires strict regulation and is highly dependent on the correctness of the machine parameters. This paper presents a comparison between the classical PI and the metaheuristic Genetic Algorithm (GA), aiming to enhance the power extraction of DFIG under varying wind conditions. The simulation was carried out using MATLAB-SIMULINK, enabling the exploration of its performance across a range of operational scenarios. The results indicate that the PI controller optimized by GA demonstrates significant improvements over traditional controllers, particularly noted for its simplicity, faster convergence, and greater efficiency in power management.