Iraqi Journal for Electrical And Electronic Engineering

In this paper, a control strategy for a combination PV-BESS-SC hybrid system in islanded microgrid with a DC load is designed and analyzed using a new topology. Although Battery Energy Storage System (BESS) is employed to keep the DC bus voltage stable; however, it has a high energy density and a low power density. On the other hand, the Supercapacitor (SC) has a low energy density but a high-power density. As a result, combining a BESS and an SC is more efficient for power density and high energy. Integrating the many sources is more complicated. In order to integrate the SC and BESS and deliver continuous power to the load, a control strategy is required. A novel method for controlling the bus voltage and energy management will be proposed in this paper. The main advantage of the proposed system is that throughout the operation, the State of Charging (SOC), BESS current, and SC voltage and current are all kept within predetermined ranges. Additionally, SC balances fast-changing power surges, while BESS balances slow-changing power surges. Therefore, it enhances the life span and minimizes the current strains on BESS. To track the Maximum Power Point (MPP) or restrict power from the PV panel to the load, a unidirectional boost converter is utilized. Two buck converters coupled in parallel with a boost converter are proposed to charge the hybrid BESS-SC. Another two boost converters are used to manage the discharge operation of the BESS-SC storage in order to reduce losses. The simulation results show that the proposed control technique for rapid changes in load demand and PV generation is effective. In addition, the proposed technique control strategy is compared with a traditional control strategy.

In this paper, a new method based on the combination of the Teaching-learning-based-optimization (TLBO) and Black-hole (BH) algorithm has been proposed for the reconfiguration of distribution networks in order to reduce active power losses and improve voltage profile in the presence of distributed generation sources. The proposed method is applied to the IEEE 33-bus radial distribution system. The results show that the proposed method can be a very promising potential method for solving the reconfiguration problem in distribution systems and has a significant effect on loss reduction and voltage profile improvement.

The use of smart network applications based on the Internet of Things is increasing, which increases the attractiveness of malicious activities, leading to the need to increase the adequate security of these networks. In this paper, the latest recent breakthroughs in blockchain for the Internet of Things are examined in the context of electronic health (e-health), smart cities, smart transportation, and other applications in this article. Research gaps and possible solutions are discussed, such as security, connection, transparency, privacy, and the IoT’s blockchain regulatory challenges. In addition, the most important consensus algorithms used in the blockchain have been discussed, including Proof of Work, Proof of Stake, and Proof of Authority, each of which operates within certain rules.

This work focuses on the use of the Linear Quadratic Gaussian (LQG) technique to construct a reliable Static VAr Compensator (SVC), Thyristor Controlled Series Compensator (TCSC), and Excitation System controller for damping Subsynchronous Resonance ( SSR ) in a power system. There is only one quantifiable feedback signal used by the controller (generator speed deviation). It is also possible to purchase this controller in a reduced-order form. The findings of the robust control are contrasted with those of the “idealistic” full state optimal control. The LQG damping controller’s regulator robustness is then strengthened by the application of Loop Transfer Recovery (LTR). Nonlinear power system simulation is used to confirm the resilience of the planned controller and demonstrates how well the regulator dampens power system oscillations. The approach dampens all torsional oscillatory modes quickly while maintaining appropriate control actions, according to simulation results.

As a key type of mobile robot, the two-wheel mobile robot has been developed rapidly for varied domestic, health, and industrial applications due to human-like movement and balancing characteristics based on the inverted pendulum theory. This paper presents a developed Two-Wheel Self-Balanced Robot (TWSBR) model under road disturbance effects and simulated using MATLAB Simscape Multibody. The considered physical-mechanical structure of the proposed TWSBS is connected with a Simulink controller scheme by employing physical signal converters to describe the system dynamics efficiently. Through the Simscape environment, the TWSBR motion is visualized and effectively analyzed without the need for complicated analysis of the associated mathematical model. Besides, 3D visualization of real-time behavior for the implemented TWSBR plant model is displayed by Simulink Mechanics Explorer. Robot balancing and stability are achieved by utilizing Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR) controllers’ approaches considering specific control targets. A comparative study and evaluation of both controllers are conducted to verify the robustness and road disturbance rejection. The realized performance and robustness of developed controllers are observed by varying object-carrying loaded up on mechanical structure layers during robot motion. In particular, the objective weight is loaded on the robot layers (top, middle, and bottom) during disturbance situations. The achieved findings may have the potential to extend the deployment of using TWSBRs in the varied important application.

Due to their vital applications in many real-world situations, researchers are still presenting bunches of methods for better analysis of motor imagery (MI) electroencephalograph (EEG) signals. However, in general, EEG signals are complex because of their nonstationary and high-dimensionality properties. Therefore, high consideration needs to be taken in both feature extraction and classification. In this paper, several hybrid classification models are built and their performance is compared. Three famous wavelet mother functions are used for generating scalograms from the raw signals. The scalograms are used for transfer learning of the well-known VGG-16 deep network. Then, one of six classifiers is used to determine the class of the input signal. The performance of different combinations of mother functions and classifiers are compared on two MI EEG datasets. Several evaluation metrics show that a model of VGG-16 feature extractor with a neural network classifier using the Amor mother wavelet function has outperformed the results of state-of-the-art studies.

The tremendous development in the field of communications is derived from the increasing demand for fast transmission and processing of huge amounts of data. The non-orthogonal multiple access (NOMA) system was proposed to increase spectral efficiency (SE) and improve energy efficiency (EE) as well as contribute to preserving the environment and reducing pollution. In the NOMA system, a user may be considered as a relay to the others that support the coverage area based on adopting the reuse of the frequency technique. This cooperation enhances the spectral efficiency, however, in the cell, there are other users that may affect the spectral allocation that should be taken into consideration. Therefore, this paper is conducted to analyze the case when three users are available to play as relies upon. The analyses are performed in terms of the transmitted power allocation in a fair manner, and the system’s performance is analyzed using the achievable data rates and the probability of an outage. The results showed an improvement in throughputs for the second and third users, as its value ranged from 7.57 bps/Hz to 12.55 bps/Hz for the second user and a quasi-fixed value of 1,292 bps/Hz for the third user at the transmitted power ranging from zero to 30 dBm.

Preserving privacy and security plays a key role in allowing each component in the healthcare system to access control and gain privileges for services and resources. Over recent years, there have been several role-based access control and authentication schemes, but we noticed some drawbacks in target schemes such as failing to resist well-known attacks, leaking privacy-related information, and operational cost. To defeat the weakness, this paper proposes a secure electronic healthcare record scheme based on Schnorr Signcryption, crypto hash function, and Distributed Global Database (DGDB) for the healthcare system. Based on security theories and the Canetti-Krawczyk model (CK), we notice that the proposed scheme has suitable matrices such as scalability, privacy preservation, and mutual authentication. Furthermore, findings from comparisons with comparable schemes reveal that the suggested approach provides greater privacy and security characteristics than the other schemes and has enough efficiency in computational and communicational aspects.

Agriculture is the primary food source for humans and livestock in the world and the primary source for the economy of many countries. The majority of the country’s population and the world depend on agriculture. Still, at present, farmers are facing difficulty in dealing with the requirements of agriculture. Due to many reasons, including different and extreme weather conditions, the abundance of water quality, etc. This paper applied the Internet of Things and deep learning system to establish a smart farming system to monitor the environmental conditions that affect tomato plants using a mobile phone. Through deep learning networks, trained the dataset taken from PlantVillage and collected from google images to classify tomato diseases, and obtained a test accuracy of 97%, which led to the publication of the model to the mobile application for classification for its high accuracy. Using the IoT, a monitoring system and automatic irrigation were built that were controlled through the mobile remote to monitor the environmental conditions surrounding the plant, such as air temperature and humidity, soil moisture, water quality, and carbon dioxide gas percentage. The designed system has proven its efficiency when tested in terms of disease classification, remote irrigation, and monitoring of the environmental conditions surrounding the plant. And giving alerts when the values of the sensors exceed the minimum or higher values causing damage to the plant. The farmer can take the appropriate action at the right time to prevent any damage to the plant and thus obtain a high-quality product.

Technology and digital communications have advanced so that digital photos, videos, or text may be easily manipulated by those not authorized to do so. In addition, the availability of specialized picture editing programs like Photoshop has simplified the process of altering photographs. At first glance, there may seem to be no problem, especially when an image editing method is necessary to delete or add a certain scene that increases the picture’s beauty. But what about personal images or images with copyright? Attempts are constantly made to spoof these images using different approaches. Therefore, measures to reduce the likelihood of counterfeiting in digital and printed forms of media are required. The proposed approach aims to detect a counterfeit in images using a unique generator that conceals the data represented by the embedded watermark utilizing modern visual cryptography and hash algorithms. Image extractions may easily be analyzed for signs of forgery. As a result, our approach will detect and validate phony documents and images.

Current automatic writing feedback systems cannot distinguish between different discourse elements in students’ writing. This is a problem because, without this ability, the guidance provided by these systems is too general for what students want to achieve on arrival. This is cause for concern because automated writing feedback systems are a great tool for combating student writing declines. According to the National Assessment of Educational Progress, less than 30 percent of high school graduates are gifted writers. If we can improve the automatic writing feedback system, we can improve the quality of student writing and stop the decline of skilled writers among students. Solutions to this problem have been proposed, the most popular being the fine-tuning of bidirectional encoder representations from Transformers models that recognize various utterance elements in student written assignments. However, these methods have their drawbacks. For example, these methods do not compare the strengths and weaknesses of different models, and these solutions encourage training models over sequences (sentences) rather than entire articles. In this article, I’m redesigning the Persuasive Essays for Rating, Selecting, and Understanding Argumentative and Discourse Elements corpus so that models can be trained for the entire article, and I’ve included Transformers, the Long Document Transformer’s bidirectional encoder representation, and the Generative Improving a pre trained Transformer 2 model for utterance classification in the context of a named entity recognition token classification problem. Overall, the bi-directional encoder representation of the Transformers model railway using my sequence-merging preprocessing method outperforms the standard model by 17% and 41% in overall accuracy. I also found that the Long Document Transformer model performed the best in utterance classification with an overall f-1 score of 54%. However, the increase in validation loss from 0.54 to 0.79 indicates that the model is overfitting. Some improvements can still be made due to model overfittings, such as B. Implementation of early stopping techniques and further examples of rare utterance elements during training.

The demand for a secured web storage system is increasing daily for its reliability which ensures data privacy and confidentiality. The proposed paper aims to find the most secure ways to maintain integrity and protect privacy and security in healthcare management systems. The Advanced Encryption Standard (AES) algorithm is used to encrypt data transferred by providing a means to check the integrity of information transmitted and make it more immune to cyberattack techniques, this was implemented by using Keyed-Hash Message Authentication Code (HMAC) and Secured Hash Algorithm-256 (SHA-256). The risk of exposure to attackers can be avoided by using honeypot systems combined with Intrusion detection systems (IDSs) as a firewall system is not effective against such attacks alone. The experimental results evaluate the proposed security health information management system by comparing the performance of the encryption algorithm based on encryption time, memory and CPU usage, and entropy for different plaintext lengths. In addition, it can be seen that when changing the AES key size, more memory and time are required the longer the key size is used. The 128 bits AES key is therefore advised if the system must operate in hard real-time.

Due to the changing flow conditions during the pipeline’s operation, several locations of erosion, damage, and failure occur. Leak prevention and early leak detection techniques are the best pipeline risk mitigation measures. To reduce detection time, pipeline models that can simulate these breaches are essential. In this study, numerical modeling using COMSOL Multiphysics is suggested for different fluid types, velocities, pressure distributions, and temperature distributions. The system consists of 12 meters of 8-inch pipe. A movable ball with a diameter of 5 inches is placed within. The findings show that dead zones happen more often in oil than in gas. Pipe insulation is facilitated by the gas phase’s thermal inefficiency (thermal conductivity). The fluid mixing is improved by 2.5 m/s when the temperature is the lowest. More than water and gas, oil viscosity and dead zones lower maximum pressure. Pressure decreases with maximum velocity and vice versa. The acquired oil data set is utilized to calibrate the Support Vector Machine and Decision Tree techniques using MATLAB R2021a, ensuring the precision of the measurement. The classification result reveals that the Support Vector Machine (SVM) and Decision Tree (DT) models have the best average accuracy, which is 98.8%, and 99.87 %, respectively.

This paper proposes a new design of compact coplanar waveguide (CPW) fed -super ultra-wideband (S-UWB) MIMO antenna with a bandwidth of 3.6 to 40 GHz. The proposed antenna is composed of two orthogonal sector-shape monopoles (SSM) antenna elements to perform polarization diversity. In addition, a matched L-shaped common ground element is attached for more efficient coupling. The FR-4 substrate of the structure with a size of 23 × 45 × 1.6 mm3 and a dielectric constant of 4.3 is considered. The proposed design is simulated by using CST Microwave Studio commercial software. The simulation shows that the antenna has low mutual coupling (|S21| < -20 dB) with |S11|<−10 dB, ranging from 3.6 to 40 GHz. Envelope correlation coefficient (ECC) is less than 0.008, diversity gain (DG) is more than 9.99, mean effective gain (MEG) is below – 3 dB, and total active reflection coefficient (TARC) is less than -6 dB over the whole response band is reported. The proposed MIMO antenna is expected efficiently cover the broadest range of frequencies for contemporary communications applications.

Multi-level inverters, as a result of the significant contributions they have made to the fields of high voltage and renewable energy applications, MLI has earned a prestigious place in the field of industrial electronics applications. The use of MLI makes it possible to generate an alternating voltage from a DC voltage or from voltages that are continuously applied thanks to this capability. The quality of the produced wave depends on minimizing the level of total harmonic distortion (THD) in the ensuing output voltage. Increasing the total number of levels is required in order to bring down the THD. The bigger the number of layers, the lower the THD. On the other hand, this necessitates an increase in the number of power switches that are utilized, in addition to an increase in the number of DC sources for certain types. A greater number of levels are achieved in this work with a reduced number of switches, and the DC source necessitates the use of specialized control over the switches as well as the grading of the DC source values. In order to demonstrate that the suggested converter achieves the needed outcomes, the MATLAB simulator is utilized.