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All Journal International Journal of Robotics and Control Systems
Mohamed I. Mosaad
Yanbu Industrial College
Control & Systems Engineering Electrical & Electronics Engineering

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Adaptive Neuro-Fuzzy Self Tuned-PID Controller for Stabilization of Core Power in a Pressurized Water Reactor Hany Abdelfattah; Said A. Kotb; Mohamed Esmail; Mohamed I. Mosaad
International Journal of Robotics and Control Systems Vol 3, No 1 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i1.710

Abstract

There has been a lot of interest in generating electricity using nuclear energy recently. This interest is due to the features of such a source of energy. The main part of the nuclear energy system is the reactor core, especially the most widely used Pressurized Water Reactor (PWR). This reactor is the hottest part of the nuclear system; security risks and economic possibilities must be considered. Controlling this reactor can increase the security and efficiency of nuclear power systems. This study presents a dynamic model of the (PWR), including the reactor's core, the plenums of the upper and lower, and the connecting piping between the reactor core and steam generator. In addition, an adaptive neuro-fuzzy (ANFIS) self-tuning PID Controller for the nuclear core reactor is presented. This adaptive controller is used to enhance the performance characteristics of PWR by supporting the profile of the reactor power, the coolant fuel, and hot leg temperatures. The suggested proposed ANFIS self-tuning controller is estimated through a comparison with the conventional PID, neural network, and fuzzy self-tuning controllers. The results showed that the proposed controller is best over traditional PID, neural network, and fuzzy self-tuning controllers. All simulations are throughout by using MATLAB/SIMULINK.
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.
Co-Authors Borni Abdelhalim Fahd A. Banakhr Hany Abdelfattah Mammeri Oussama Mohamed Esmail Mohamed Metwally Mahmoud Noureddine Bessous Ouassa Mohammed Lamine Said A. Kotb