In recent decades, Distributed Generation (DG) has emerged as the most effective solution for Radial Distribution Systems (RDS) to reduce power losses, primarily due to the significant increase in energy consumption, while also improving the voltage profile. This paper discusses the application of two algorithms: the White Shark Optimizer (WSO) and the Exponential Distribution Optimizer (EDO). These algorithms are designed to determine the optimal distribution of Renewable Energy Resources (RERs) for Photovoltaic (PV) systems and Wind Turbine Generators (WTGs) within a distribution network. The main objectives are to minimize power losses and enhance the voltage profile of the distribution system. Simulations were conducted across various case studies, considering multiple scenarios to assess the impact of PV and WTG systems on power losses and the Voltage Deviation Index (VDI). The effectiveness of the proposed algorithms is demonstrated through a comprehensive performance analysis applied to the IEEE 33-bus system.Results indicate that, in the best scenario involving multi-objective functions, the WSO can reduce power losses and VDI by up to 90.7% and 98.98%, respectively, while improving the minimum voltage from 0.9131 to 0.9804 p.u. These findings were compared with other techniques, highlighting the superiority and effectiveness of the proposed algorithms. Overall, the results show that these algorithms effectively determine the ideal sizes and placements for PV and WTG units, leading to a significant reduction in active power loss and an improvement in the minimum bus voltage.

Studying and evaluating the power quality (PQ) of an electrical network for nuclear installation is an important issue and a hot research topic for guaranteeing reliable and safe operation of sensitive electrical loads during this type of installation. As several PQ phenomena determine the overall PQ performance, analyzing PQ signals for evaluating the overall PQ is one of the major challenges for researchers in this field. Technically, voltage imbalance, current imbalance, voltage harmonic distortion, current harmonic distortion, and the power factor are five important PQ phenomena that judge the overall PQ performance of an electrical system. Multicriteria decision-making (MCDM) is used here as a methodology to identify a weighting for each PQ phenomenon. This paper proposes a power quality evaluation method for a nuclear research reactor (NRR) electrical network based on two MCDM. Methods the analytic hierarchy process (AHP) and criterion importance through inter-criteria correlation (CRITIC). A MATLAB/Simulink model for the NRR electrical system is presented, and then its validity and credibility are verified via measurements. In this study, different abnormal conditions are simulated in the NRR network to generate power quality disturbances, including a three-phase nonlinear load to simulate harmonics, an unbalanced load to simulate unbalance, and an inductive load to simulate the change in the power factor. The effectiveness and robustness of the proposed methodology are demonstrated through these different case studies. The results show that the obtained CPQI based on the dynamic weight approach allows for more accurate evaluations by adjusting the importance of various PQ phenomena depending on operational conditions and priorities. The main contribution of this paper is that a single compound power quality index (CPQI) was developed based on both the dynamic weights obtained from AHP-CRITIC methods and the results of the five PQ phenomena obtained under different abnormal conditions, considering the threshold level for each of these PQ phenomena. The analysis of the obtained results shows that this method accurately evaluates the overall PQ performance.

Nowadays, especially after (COVID-19) era and obligatory closing of training centers, schools and universities, etc., training and education have changed dramatically to be carried out remotely and on digital platforms. As a result, the need for explanatory learning was raised, and the importance of simulation examples and pictorial illustration increased to facilitate understanding of the scientific phenomena for learners (trainees) and the acquisition of ideas for lecturers (trainers) to explain them. The definition of electrical power components remains mysterious and confusing for many electrical engineers, even for those with high technical knowledge. For scientists, there is still no consensus on this topic and there is no definitive definition of the various components of power and energy. This paper reviews common depiction examples of power in single-phase AC electrical systems. Then, it presents a mathematically proven concept for power and energy definition that was accomplished previously. Finally, it provides an analogy and pictorial illustration to clarify the physical essence of electrical energy flow so that it is better understood.

Studying the power quality (PQ) is an essential issue to ensure the safe and accurate operation of sensitive equipment particularly for nuclear installations. Assessment of PQ involves collecting and analysing data resources and then evaluating it with reference to PQ standards. There are many alternatives for PQ and it is difficult to make an appropriate selection among them in the existence of their multiple criteria which are usually conflicted. So this selection subject can be classified as a Multi Criteria Decision Making (MCDM) problem. To do so, a reliable and scientific method for studying and evaluating the overall system PQ is required. This study aims to assess performance of PQ for the electrical power system at a Nuclear Research Reactor (NRR) during a certain period using multiple measures for the most decisive PQ phenomena. It focuses on a number of the most important PQ phenomena namely frequency fluctuation (deviation), unbalances of current and voltage, current and voltage harmonic distortion, flicker and power factor. After combining all results into six samples (alternatives), the criteria weights are determined based on an objective method for weighting which is called CRITIC method. Then, the alternatives are ranked using compromise MCDM method-VIKOR method. The obtained results are analyzed and discussed to evaluate performance of NRR electrical system from the PQ view. It showed that the compromise solution that obtained by CRITIC-VIKOR can be a guide to facilitate the PQ evaluation of nuclear installation electrical system. Also, it can empower the operators with the benefits of benchmarking and monitoring a single index instead of several indices. Moreover, it is very useful for helping stakeholders to understand how the PQ performance changes under a certain operating condition of the facility. Finally, it is can be considered as a good model to weight each PQ phenomena and identify the time intervals for best and worst total PQ in NRR.

As a result of various loads, including critical installations (industries, nuclear facilities, etc.), electrical distribution networks (EDNs) must operate safely and sustainably in order to overcome problems such as high power losses and voltage drops, which must be addressed with the most efficient location and capacity of distributed generators (DGs). In order to address this purpose, the proposed research introduces a robust modified forensic-based investigation (mFBI) optimization method that is demonstrated first time to produce the optimum allocation of DGs in EDNs for minimizing power losses and voltage deviations. Moreover, the analytical hierarchy process approach is employed to generate the most applicable weighting factors of the multi-objective function (MOF). Validation and demonstration of the newly developed mFBI technique is conducted by studying the impact of DG integration on 118 IEEE EDN nodes and real Delta-Egypt EDNs. Additionally, an in-depth comprehensive analysis has been carried out between the novel mFBI and 7 recent proposed optimizers, considering the Wilcoxon sign rank test that is used to verify the significant nature of the results. The numeric results best demonstrate the advantage and utility of incorporating the MOF approach and the superior mFBI technique in the EDN to derive an efficient optimum solution.

Presently, distribution companies are concerned with the penalty applications in energy tariff to control harmonic distortion levels in distribution systems. It's common to consider this for industrial and commercial customers. But, nowadays the usage of modern electronic equipment in residential section is increasing and people are going to do their jobs at home, which mean more distortion in single phase systems. So, in the early future companies will pay more attention for requesting residential customers to provide better power quality. This paper proposes a distinct rate structure to regulate the harmonic distortion in single phase systems, which is applicable for residential loads. The idea is to evaluate the responsibility of harmonic distortion at the Point of Common Coupling (PCC) between utility and customer for each harmonic order of power frequency. By finding the utility and customer harmonic contributions, it is possible to achieve fairer cost sharing through the proposed rate structure. This method has been verified through a simplified case study mentioned in the IEEE Std. 1459-2000.

Biometric recognition is an automated technique of recognising persons based on their traits. Because of their exceptional texture, the biometric features' ostensibly random nature makes them good candidates for recognition. These features are unique for each individual even for identical twins authentication. The latest developments in Deep Learning (DL) and computer vision has proved that Convolutional Neural Networks (CNNs) can extract generic descriptors that can represent complex image features. How to protect the biometric data and ensure user’s privacy is a main concern, nowadays. Hence, several cancelable biometric scenarios have been proposed. In this paper, we propose a novel cancelable biometric recognition system based on a CNN model with bio-convolution. The performance metrics are estimated on different face and iris datasets. In contrary to most conventional secure biometric recognition systems, the proposed system achieves superior accuracy results, while keeping the ability to cancel the biometric traits if compromised. The experimental findings on each database are shown and compared to those of the state-of-the-art systems that have been tested on the same database. Furthermore, the recognition rates reach 99.15%, 98.35%, 97.89, and 95.48% with the LFW, FERET, IITD, and CASIA-IrisV3 databases, respectively.

Today, after more than twenty years of operation, Egypt Second Research Reactor (ETRR-2) electrical power system needs to be re-evaluated to ensure that the system is still adequate for present situation of continuous operation under different circumstances, which may be severe in some instances. In the process of re-evaluation of the ETRR-2 electrical power system, it is very important to calculate the short circuit currents at several locations in the system. For electrical system, it is well known that determination of the short circuit current at different locations of the system is very important for two main reasons: first, selection of the short circuit rating for each equipment or the switchboard and second, selection of the rating and setting for protective devices in the protection scheme. This work presents an analytical short circuit study of the ETRR-2 electrical power system by building its model using Electrical Transient Analysis Program (ETAP) software, which was not available during the ETRR-2 design stage at the time. The built model was tested by comparing its simulation results with the designer calculation that was performed on limited points of the system. The authors take the advantages of this new advanced software to evaluate the system in every point in the system. Based on the results of this study, it is feasible to decide which protective device or equipment that needs upgrading to be adequately rated for the present operating circumstances. Moreover, the verified model can be used in future expansion, replacement and renewal studies.

Electrical system of nuclear facility should have suitable safety mechanisms and minimum losses for providing reliability of power supply to guarantee the continuity of operation.The purpose of having electrical infrastructure with high efficiency is to have energy delivery systems with high efficiency.So in this research, a proposed model of the nuclear reactor electrical system will be presented to study the effect of connecting new energy saving loads and advanced controllers on the system performance in order to keep safe and good performance of this nuclear facility.By using Electrical Transient Analysis Program (ETAP) software the NRR simulation model was developed.The analysis was performed with using nonlinear energy saving lighting systems and Variable Frequency Drive (VFD) installed as a controlled for secondary pump of the reactor by providing smooth motor starting also its advantage for energy saving.From the results, active power consumed and current were reduced, while VTHD and ITHD increase clearly.Finally the influences of using large number of LED (Light Emitting Diode) lamps and VFDs on the power quality were observed but not exceed the IEEE standard limits.Also, its impact on energy savings and economic benefits with safe and reliable operation was achieved.

Nuclear and renewables energies are the two variants for low-carbon energy and the evolving grid suggests possible synergies between them. Nuclear energy introduces supple operations based on power demand, while renewables such as PV and wind hybrid systems depend on the presence and strength of sunlight or wind. For grid stability, there is need to improve their performance in order to overcome the impact of this disadvantage. The paper is a step in this direction as it addresses a detailed comprehensive dynamic modeling and an efficient control of grid-connected energy sources such as PV or wind system to increase system reliability and to ensure the power quality and safe operation of critical demands. The behavior of the suggested hybrid system is tested at different climate circumstances such as variation of sun radiation and wind speed. The PV is equipped with a boost converter and a three-phase pulse width modulation (PWM) inverter. The wind energy comprises a doubly fed generator (DFIG) based on a variable-speed wind turbine. The two controllers' rotor-side and grid-side converters of DFIG have the ability to generate and observe reactive power, to keep constant speed of the rotor and control the DC-link voltage. The proposed scheme was investigated using MATLAB software. The maximum power point tracking (MPPT) was used for two systems, PV and wind, in varying weather conditions. The simulation results prove that the voltage at the point of common coupling was constant. Furthermore, the injected current of the grid side was in sinusoidal form and was synchronized with grid side voltage. In addition, the injected power-to-utility grid was around power delivered by the hybrid PV and wind system.

A process for establishing an Integrated Management System (IMS) for operation and maintenance of the ETRR-2 reactor is initiated. Development and application of the IMS consider the usage of a graded approach in order to commensurate the resources devoted to the management system with the importance of an item/activity and the magnitude of the risks. Literatures have introduced different methods for the concept of graded approach which is very important in applying the management, safety and technical requirements. This paper provides an additional example for the methodology of applying such approach. It suggests a grading methodology and determines the considerations that need to be taken into account in grading the application of IMS requirements. The paper introduces a proposal for classification of items or activities in the ETRR-2 into four grades. Then it gives a grading example of specific controls for maintenance of structures, systems and components (SSCs) in the plant and explains the management requirements appropriate for each grade. Finally, the approach is explained briefly using an application practice on the Standby Power Supply system of the plant as a case study.

Iris recognition is one of the Biometric systems used for persons identification based on their special iris traits, which are unique featuresfor each individual. It is clear that the progress in deep learning show how efficient the extracted features from convolutional neural networks (CNNs) to describe the complex image patterns. However, the influence of noise is a serious problem in most image processing systems. It may ariseto the iris recognition systems due to environmental conditions that can affects the features extracted from the iris images. Hence, the objective of this paper is to study the performance of CNNs based Deep learning (Alex net, Vgg16 and Vgg19) when used for iris recognition with the presence of noise and compares it with Masek algorithm. Simulation results reveal that using the deep learning greatly improves iris recognition accuracy for Alex CNN. We achieve 100%, 100%, 88.9% for interval, lamp and twins datasets respectively.

Proposing a high effective objective function by utilizing optimal weighting factors plays an important role in power systems to boost the quality, attitude, and efficiency of evaluating the position and capacity of renewable distributed generators (RDGs) optimally. This research introduces a comprehensive study of different effective objective functions. A comprehensive analysis between the most modern optimization techniques, like hybrid particle swarm optimization (PSO) with Quazi-Newton, hybrid PSO with gravitational search algorithm, grasshopper optimization algorithm, moth-flame optimization, and slap-swarm algorithm, is done in order to determine the best optimizer with respect to high performance, high accuracy, and the minimum convergence time. The best prepared methodology is proposed and compared with other modern techniques to validate its performance. The suggested scheme is exercised by studying the impact of the RDGs integration for 33 and 69 nodes of IEEE distribution grids, in addition to one of the Egyptian radial distribution networks as a practical case study within 24 h at different load levels. The numerical results confirmed the importance and usefulness of incorporating electing the efficient objective function and superior optimization algorithm in the power system to achieve a successful global optimum solution to ensure the power quality through enhanced voltage levels, while minifying the system power losses and the operating prices.

The recent years have witnessed a dramatic shift in the way of biometric identification, authentication, and security processes. Among the essential challenges that face these processes are the online verification and authentication. These challenges lie in the complexity of such processes, the necessity of the personal real-time identifiable information, and the methodology to capture temporal information. In this paper, we present an integrated biometric recognition method to jointly recognize face, iris, palm print, fingerprint and ear biometrics. The proposed method is based on the integration of the extracted deep-learned features together with the hand-crafted ones by using a fusion network. Also, we propose a novel convolutional neural network (CNN)-based model for deep feature extraction. In addition, several techniques are exploited to extract the hand-crafted features such as histogram of oriented gradients (HOG), oriented rotated brief (ORB), local binary patterns (LBPs), scale-invariant feature transform (SIFT), and speeded-up robust features (SURF). Furthermore, for dimensional consistency between the combined features, the dimensions of the hand-crafted features are reduced using independent component analysis (ICA) or principal component analysis (PCA). The core of this paper is the template protection via a cancelable biometric scheme without significantly affecting the recognition performance. Specifically, we have used the bio-convolving approach to enhance the user’s privacy and ensure the robustness against spoof attacks. Additionally, various CNN hyper-parameters with their impact on the proposed model performance are studied. Our experiments on various datasets revealed that the proposed method achieves 96.69%, 95.59%, 97.34%, 96.11% and 99.22% recognition accuracies for face, iris, fingerprint, palm print and ear recognition, respectively.

In the original version of the article, the article title was published incorrectly.

In the IAEA documentations focus on management, for example the recently issued IAEA Safety Requirements publication Leadership and Management for Safety (IAEA Safety Standards Series No. GSR Part 2), it is understood that the organization information and knowledge (both tacit and explicit) should be managed as a resource for effective implementation of the management system. Also, lack of a knowledge management program is one of the warning signs of a decline in safety culture. This paper focuses on enhancing the Knowledge Management performance as a main step to achieve the goal of applying the Integrated Management System to improve safety, operation efficiency and performance. The Egyptian Atomic Energy Authority (EAEA) is a governmental research Authority that owns the Egypt Second Research Reactor (ETRR 2) complex. This Complex contains three nuclear installations (ETRR2 Reactor, Fuel Manufacturing Pilot Plant-FMPP and Radioisotopes Production Facility-RPF) and Administrative and engineering building (headquarter of the complex), which have a fairly adequate knowledge base. The paper introduces a proposal of Knowledge Management Program design for the ETRR2 complex. The objective of the proposed plan is first to evaluate the existing Knowledge base and then implement effective Knowledge Management (KM) program in order to avoid risks to overall operations such as 'Risk of Knowledge Loss' and lower safety and productivity.

Power quality monitoring and investigation are necessary to maintain perfect and safe operation of sensitive equipment especially for nuclear facilities. In this work, a case study about harmonic analysis of an electrical power system at a Nuclear Research Reactor (NRR) has been done to evaluate the current state from the PQ point of view. Firstly, a study is given separately for different types of individual loads in NRR and their impact on the PQ of the electrical system. Then, an investigation of practical measurements was carried out for the NRR electrical system during certain condition of operation. These measurements were concerned with harmonic distortion in voltage, current and power of the electrical system of the NRR facility. Generally, the measurement results showed that the harmonic distortions in the electrical system are within the standard limits. Finally, modeling and simulation for the NRR electrical system has been built using Electrical Transient Analysis Program (ETAP) software to be used for simulations at different operating scenarios. The simulation results are compared with the measurements for verification and credibility. From the study it is clearly concluded that results of the program have a very slight difference with measured results and the impact of current state non-linear loads of NRR on PQ is accepted form the side of harmonics.

Iris recognition is one of the automated processes of verifying individuals’ identity based on their iris characteristics. Apparently, the random nature of the iris texture, which is unique for each individual, makes it an exclusive trait for biometric recognition even for the case of identical twins’ authentication. Recently, the improvement in deep learning and computer vision indicated that the extracted features using convolutional neural networks (CNNs) are suitable to describe the complex image patterns. But, how to protect the biometric data and provide users’ privacy is a main concern, nowadays. In this paper, we study the performance of pre-trained CNNs to successfully classify cancelable iris features when taking the feature vector from each fully connected layer. We show that these pre-trained CNNs, while originally learned for classifying generic objects, are also extremely good for representing iris images for recognition. The performance metrics are evaluated on three datasets: CASIA-IrisV3, IITD and Palacky iris databases. The obtained results achieve promising cancelable iris recognition and also ensure the robustness and effectiveness of the proposed approach.

This paper introduces an analytical derivation to evaluate the flow of electric energy in power networks. This derivation is based on the paper "New Notions Suggested to power theory development - part 1: analytical derivation" published in IEEE/PES general meeting 2008. It is proven that the true energy that totally transfers to (or from) the system is the energy which belongs to the average true power only i. e. active power produced from voltage and current of the same harmonic order. All other kinds of energy are fluctuating sinusoidaly and the net transferred energy belongs to these sinusoidal power components is nil. It is a theoretical attempt to prove that the new definitions of power components suggested in paper mentioned above are connected with their associated energies and to find the physical essence of the power components under sinusoidal and non- sinusoidal conditions.

A new approach is introduced to determine the harmonic contributions between the customer and utility at the point of common coupling. This approach is based on new definitions of power components suggested by the same authors in [1]. It can determine the quantity and direction of power for each harmonic order. This method can be implemented in any power quality measurement device. Also, it will be useful in arriving at equitable ways of settling customer complaints, sharing the cost of harmonic distortion through rate structure and penalties, etc.