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All Journal Buletin Ilmiah Sarjana Teknik Elektro International Journal of Robotics and Control Systems
Mohamed Metwally Mahmoud
Aswan University
Control & Systems Engineering Electrical & Electronics Engineering

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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.
Adaptive Smart Energy Management for Wind-Battery Systems Considering Grid Fault Scenarios Habib Chaib; Housseyn Chaib; Belkacem Belabbas; Fajer M. Alelaj; Alfian Ma’arif; Mohamed Metwally Mahmoud; Vojtech Blazek; Shady M. Sadek
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 4 (2026): August
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i4.16033

Abstract

This work introduces a novel energy management system based on radial basis function neural network (RBFNN) adapted for both grid-connected and segmented modes. The system components are electrical distribution grid, wind generator, power electronic converters and batteries. The study uses intelligent control and prevents the spread of potential problems or disturbances. This system incorporates various controllers responsible of specific functions like MPP monitoring, of battery charging and discharging, and of an inverter for effectively managing the transition between energy sources based on load requirements and available sources operating at their MPP. The objectives are to facilitate coordinated operation among distributed energy resources, ensuring the provision of necessary active power and additional services as necessary. The simulation uses MATLAB/Simulink environment. Simulation results demonstrate the effectiveness and feasibility of the proposed strategy. Overall, the obtained results affirm the practicality and advantages of employing neural networks in energy management systems.
Co-Authors Ahmed Tawfik Hassan Alfian Ma’arif Belkacem Belabbas Belkacem Belabbas Borni Abdelhalim Fahd A. Banakhr Fajer M. Alelaj Habib Chaib Housseyn Chaib Kheloud Ayati Mammeri Oussama Mohamed I. Mosaad Noureddine Bessous Ouassa Mohammed Lamine Rabia Haloui Samira Heroual Shady M. Sadek Vojtech Blazek