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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.

This paper proposes an improved scheme of fault detection and classification for multi -terminal transmission lines using discrete wavelet transform (DWT). The proposed scheme can correctly detect and classify the faults. The proposed scheme is dependent on line current data only. This scheme is derived in the spectral domain and is based on the application of the DWT. The scheme uses an adaptive threshold level to detect and classify the faults. The ATP/EMTP program is used to evaluate the presented approach. Additionally, the nuclear power plants are planned to be integrated with the Egypt electric network in 2026, hence, the presented approach takes into consideration the installation of El Dabaa power station. The presented approach achieves accurate results under numerous and enhanced the fault detection and classification methodologies.