Majlesi Journal of Electrical Engineering
Volume:17 Issue: 2, Jun 2023

The addition of renewable energy sources to the traditional distribution network has transformed the centralized unidirectional power source into two-way and multiple power source system. This will improve overall system's reliability and also reduces the down time that are associated within the radial distribution system. This paper investigates the impact of distributed energy sources like PV, Wind, Electric Storage, and Gas Turbine Generator on the reliability of distribution system. The Monte Carlo simulation method is used to test bus-2 of IEEE RBTS distribution network. The distribution network has been customized to incorporate the WT, PV, ESS, and GTG distributed generation. The WT and PV stochastic models were employed to replicate the unpredictability of these sources since speed of wind and solar radiation are both unpredictable. This work shows that the integration of distributed generation enhances the distribution system's reliability.

Single-Point Diamond Turning (SPDT) has been widely used in manufacturing of optical components for critical applications including military, defense, aerospace, computer science, electronics, medical, and dental. Magnetic Field (MF) assistance is a recent non-conventional assistive technique that could be used during SPDT process to improve the machining conditions. Recent experimental studies show that using MF assisted SPDT technique can improve the optical surface roughness compared to purely mechanical SPDT process. There are a few studies conducted on the application of magnetic manipulation technique during the SPDT process. The effects of applying a strong MF during diamond cutting on the outcome of the machining process, as well as the optimum MF parameters to be used in SPDT process, are not fully discovered yet. The purpose of this research is to design and develop an automatic MF assistance system that could be used in non-conventional MF assisted and hybrid SPDT processes. The proposed automatic system is capable of communicating with the hybrid controller and precisely set/modify the desired magnetic parameters, including the generated magnetic flux density, at the cutting zone. By using the proposed system, an automatic MF assistance system in non-conventional SPDT processes could be enabled and the optimum machining conditions could be determined.

A compact, wideband, low cost, high radiation efficiency and planar half mode substrate integrated waveguide (HMSIW) horn antenna is proposed in this work. For the first time, HMSIW is used to design a horn antenna. By using HMSIW waveguide structure instead of Substrate Integrated Waveguide (SIW), the impedance bandwidth of antenna is increased significantly. The use of this waveguide structure in the design of the horn and its feeding system has increased the impedance bandwidth of the antenna. In fact, leaking the electromagnetic waves gradually along the horn increases the bandwidth of antenna. By applying the design equations of the H-plane horn antenna and half mode substrate integrated waveguide, the initial dimensions of the antenna have been obtained, then the best possible solution has been extracted by parametric study and optimization. Simulation results exhibit an impedance bandwidth of 50% from 24GHz to 40GHz. Also, the proposed design shows a gain between 7dB and 10 dB together with radiation efficiency higher than 95% over the frequency band of 25 to 35GHz. The innovation of this work is in providing a new horn structure based on HMSIW with wide impedance bandwidth. In the previously presented works, which are mainly based on SIW, the antenna suffers from a narrow impedance bandwidth.

The use of solar energy has begun to be developed in PLTS, but the photovoltaic module electricity produced is not at maximum output power. To increase the efficiency of the photovoltaic module, Maximum PowerPoint Tracking (MPPT) technology is used. Differences in the level of solar energy irradiation can cause the output power of the solar panels to vary and will not be maximized. Changing temperature and irradiation can be maintained with a maximum voltage of 40Volt and according to what is desired. In this study, MPPT consists of a Boost Converter whose function is to regulate the output voltage, while the algorithm used is the fuzzy logic which works based on Error (E) and Change Error (CE) from changes in the voltage and current of the photovoltaic module. The results showed that after using MPPT when input was given a change in load resistance with irradiation of 1000 W/m2 and a temperature of 25 ℃ resulted in a difference in power under different conditions compared to a system without MPPT. The power generated without the use of MPPT has a significant change with the results of 227.7W, 114.4W, 76.5W, 57W, and 45.5W. Testing the system after installing the MPPT when given an input change in irradiation with a load resistance of 20 Ω and a temperature of 25°C, a more stable power is produced with a value of 0.008W. Then when the input changes to irradiation with a load resistance of 20 Ω and a temperature of 25 ℃, the maximum power produced for each of the highest irrigation is 746.9 W/m2, 779.4 W/m2, and 839.4 W/m2 of 38.88 W, 42.07 W, and 47.8 W and compared to the system without MPPT only 21.76W, 21.96W and 22.28W.

This article proposes a method for optimizing the energy systems in a distribution micro-grid using particle swarm optimization. The method considers the optimal production planning of simultaneous production systems and takes into account the loss of electric energy transmission resulting from the concurrent production systems to the grid bus. The article establishes the correlation between the development of optimal load distribution methodology systems and the supply of electrical or thermal load. The advantages of the proposed algorithm have been demonstrated through numerical studies and comparisons, which showed a reduction in operational expenses, carbon dioxide emissions, and fuel consumption during both summer and winter seasons. The proposed method is an effective way of providing the required electrical energy of the sub-grid with minimal compliance requirements. The implementation of the proposed method during a single day and night in the summer season results in significant reductions of 17.5%, 13%, and 1% in operational expenses, carbon dioxide emissions, and fuel consumption, respectively. In the winter season, the pre-charge method also results in reductions of 10%, 7%, and 2% in operating cost, carbon dioxide emissions, and fuel consumption, respectively.

This paper proposes a novel approach based on the copula models for estimating Millimeter-Wave channel parameters with Rician fading in a correlated MIMO system. Even though the use of these waves faces numerous challenges, having an accurate estimation of their parameters would be a crucial notion for the improved performance of this channel. Since the considered system in this paper is a MIMO system with nearby transmitter antennas, the components of the received signals are correlated. However, a general model is not available for this kind of correlation. By taking into account the correlation, this model enables a precise Joint Probability Density Function (JPDF) by employing copula models for the received signals in the antennas. We also obtain more precise channel parameter estimates using this density function. The multi-path fading parameters in a MIMO system and the correlation coefficients between the transmitter and receiver antennas are both presented in this paper. Some simulations are employed to assess the validity and reliability of estimating Millimeter-Wave channel parameters in a MIMO system.

In this paper, we analyze the dispersion properties of photonic crystal fiber with the core replaced by a composite of 85% SiO2</sub>-15% GeO2</sub>. The air hole's radii of the layers in the cladding are designed differently to improve the dispersion and nonlinear properties of the fibers. Both anomalous and all-normal dispersions have been optimized. Based on numerical simulation results, two optimal structures (d</em>1</sub>/Ʌ</em> = 0.4, Ʌ</em> = 0.9 µm and d</em>1</sub>/Ʌ</em> = 0.45 and Ʌ </em>= 1.0 µm) are proposed with a very small dispersion value of 0.298 ps/ nm.km and −0.311 ps/nm.km at the pump wavelength of 1.53 µm and 0.985 µm, respectively. The high nonlinear coefficient, small effective mode area, and very low attenuation of about 10−7</sup> dB/m at the pump wavelength are also favorable conditions for the application of broad-spectrum supercontinuum with low peak power. The proposed fibers can be new supercontinuum sources that effectively replace glass core fibers.

The increasing number of Industrial Internet of Things (IIoT) devices presents hackers with a huge attack surface from which to conduct possibly more destructive assaults. Numerous of these assaults were successful as a consequence of the hackers' inventive and unique approaches. Due to the unpredictability of network technology and attack attempts, traditional Deep Learning (DL) approaches are made ineffective. The accuracy of DL algorithms has been shown across a range of scientific fields. The Convolutional Neural Network Model (CNN) technique is an ideal alternative for anomaly detection and classification since it can automatically classify incoming data and conduct calculations faster. We introduce Honeypot Early Intrusion Detection System (HEIDS) that detects anomalies and classifies intrusions in IIoT networks using DL methods. The model is designed to detect adversaries attempting to attack IIoT Industrial Control Systems (ICS). The suggested model is implemented using One-dimensional convolutional neural networks (CNN 1D). Due to the importance of industrial services, this system contributes to the enhancement of information security detection in the industrial domain. Finally, this research gives an assessment of the HEIDS datasets of IIoT, utilizing the CNN 1D technique. With this approach, the prediction accuracy of 1.0 was reached.

This research article describes the frequency regulation of an interconnected power system that includes wind energy systems and thermal non-reheat systems, with a proportional integral derivative (PID) controller optimized using metaheuristic algorithms such as Genetic-Algorithm (GA), Harmony-Search-Algorithm (HSA), Bat-Algorithm (BA), and Flower-Pollination-Algorithm (FPA). With the demand for precisely efficient energy systems growing, system engineers are increasingly looking for the finely optimized control solution that also has the benefit of faster convergence and avoids entrapment in local minimal. To minimize the fitness function which is based on ITAE (Integral of Time multiplied Absolute Error) criteria composed of frequency and tie-line power changes, we have obtained an optimum solution in terms of PID controller gain values using the metaheuristics optimizing techniques. Change in frequency in area 1, deviation in tie-line power, and change in frequency in area 2 obtained from different techniques are compared. The results obtained by simulating MATLAB/Simulink convey that PID controller gain values optimized using the HSA technique provide better dynamic performance compared to BA, FPA and GA techniques. The simulation results have been experimentally validated using hardware-in-loop (HIL) on a real-time simulator based on field-programmable gate arrays (FPGA). The HSA optimized PID controller is used to investigate the robustness of the system by Step-Load-Perturbation (SLP) and Random Step Load Pattern (RSLP). Results obtained by running simulation also show that the HSA optimized PID controller for the same optimized gain value can withstand the SLP and RSLP variation made in the system.

To fabricate ultra-fine grain structures containing high strength, accumulative roll bonding is a kind of severe plastic deformation (SPD). In the present study, magnetic Al/ Fe2</sub>O3</sub> composites reinforced with 0, 2.5% and 5Wt.% of Fe2</sub>O3 </sub>particles have been manufactured via ARB. A uniform distribution of Iron oxide particles was achieved after the 8th</sup> cycles of ARB. Mechanical properties include (hardness, elongation, toughness) and also, magnetic and electrical properties such as magnetic saturation, magnetic permeability and magnetic residuals were studied. The presence of Fe2</sub>O3 </sub>particles improved the mechanical, electrical and magnetic properties of this kind of laminated composites. Vibrating sample magnetometer was utilized to study the magnetic properties of samples vs No. of steps.

In this article, a multiple input multiple output quadrate frequency bands modified PIFA antenna is introduced for 4G LTE and WLAN applications. The quadrate proposed antenna covers LTE 1800 (1.71-1.88 GHz), LTE 1900 (1.85-1.99 GHz), UMTS (1.92-2.17 GHz), LTE 2300 (2.305-2.400 GHz), LTE 2500(2.50-2.69 GHz), WLAN (2.400-2.484 GHz- 5.15-5.75 GHz) specified by IEEE 802.11a/b/g/n/ac and WiMAX (3.3–3.6 GHz). Then, the PIFA antenna is used as 2×1 MIMO antenna as a radiation element.  The overall dimensions of the single antenna are 50×35×0.8 mm3</sup> and the MIMO antenna is 50×70×0.8 mm3</sup> while both are realized on FR4-epoxy substrate. To reduce mutual coupling between PIFA antennas at MIMO configuration, Defected Ground Structure (DGS) along with parasitic double split ring resonator is used. The antenna performance is confirmed by the construction of the antenna prototype and measuring its characteristics in terms of reflection loss coefficient, gain, and radiation properties. The extracted outcomes show a good correlation between the simulation and experimental results that candidate it to satisfy mobile communication requirements.

In this study, the combination of Gray Wolf Optimization and Artificial neural networks (GWO-ANN) algorithm was applied to predict the long-term electricity demand in Iraq, considering the nonlinear trend and uncertainties in the variables affecting it. The results indicate that the population and gross domestic product are significant explanatory variables for long-term energy demand, consistent with previous studies. Compared to other intelligent methods, the GWO-ANN algorithm requires less data for modeling and optimally designs the ANN structure. The modeling and forecasting model outperform the ANN in simulating and predicting the long-term energy demand. Based on the most likely scenario, the predicted electricity demand in Iraq will reach approximately 415 GWh. Electricity is a critical factor in the development of societies and is utilized in various economic sectors.

Solar energy serves as one of the promising resources of renewable energy due to its abundance availability and clean nature. Climatic data such as irradiation level and temperature are essential as input variables while designing the photovoltaic-based system. This research paper presents an extensive comparative analysis of modeling approaches of photovoltaic modules using a 1-diode and 2-diode solar models studied under varying irradiation levels and temperature values. 8 different cases of solar models based on 1-diode and 2-diode models have been incorporated. Its effect on 10 performance indexes of the photovoltaic module is comprehensively investigated. Based on these parameters, these solar models are compared in detail. MATLAB/Simulink tool is applied to explore the results. Extensive results have been taken under various operating conditions. The results show that the 2-diode model exhibits excellent performance as it maximizes the efficiency by 5.41% against the 1-diode model. Also, the 2-diode model has a greater performance ratio of 41.70% than the 1-diode model. Hence, it is revealed that the performance of 2-diode models in terms of smooth output curves, increased maximum power, fill factor, and efficiency are much closer to the actual performance as compared to the 1-diode model.

A Harmonic Control Circuit (HCC) is one of the most important blocks in the Class-F power amplifiers, which should pass the even harmonics and suppress the odd harmonics. Long open stubs are usually used to suppress odd harmonics in the conventional Class-F power amplifiers, which resulted in the large size of the amplifiers. In this work, two Class-F amplifiers are designed, a simple amplifier with traditional HCC and proposed PA with a proposed HCC. The proposed HCC suppresses third, fifth and seventh harmonics and easily pass second, fourth and sixth harmonics. In the proposed amplifier with proposed HCC, the design parameters are improved compared to the simple Class-F with traditional HCC. The power added efficiency (PAE), drain efficiency (DE) and gain parameters are increased from 76%, 79% and 19.4 dB to 79.2%, 82.2% and 21.2 dB, respectively. The proposed PA is fabricated, measured and results show that the proposed PA correctly works at 0.9 GHz with 0.12 GHz bandwidth.

A multidimensional approach to technology breakthroughs, including innovations in sensors, artificial intelligence and cloud technology, is driving automation. In past few years, people have started for using electronic machines more frequently than traditional ones. As a result, this study suggests a technique wherein moisture sensors are positioned beneath plants or trees. The microcontroller activates the motor and reuses the water flow after receiving sensor data from the gateway unit. The irrigation system makes use of the Internet of Things (IoT), which is composed of two microcontrollers: the Arduino Uno for controlling relay that acts as switch for water flow and the NodeMCU with Wifi module for cloud computing. It also includes two sensors: the DHT11 and Soil Moisture. In order to monitor the state of the land, this should automatically irrigate dependent on the soil moisture, ambient temperature, and humidity. It will also communicate data to the ThingSpeak program as a cloud server. A water pump unit is also a component of the system, and it will be used to spray water on the land depending on weather and soil conditions read by sensors.This method can be used to create smart farming practices and smart lives in the future.

Fuel shortages and environmental issues have made optimal and effective management of various resources one of the most pressing issues in the current situation. There is often a separation between the heating and gas network and the power system, which is not wise when considering the economics and effectiveness of the entire system. Examining the results shows that the installed capacity has grown significantly in the last few years. This research has investigated the microgrid including wind-solar turbine, along with energy storage (battery). This energy can be used as an alternative to fossil power plants because it is readily available and renewable. This paper proposes a multi-dimensional framework for a multi carrier energy supply with a solar-wind and biogas hybrid renewable system. In this study, the amount of power exchange of sources in a microgrid independent of the distribution network and power distribution between resources and load supply have been simulated in the HOMER environment, and the output results show the optimization of power management in this network. The proposed method is completely tested and programmed in an independent microgrid during a 24-hour period.

An Internet of Things-based security system and OpenCV technology have been developed to improve the efficiency and ease of monitoring video footage from CCTV. The face detection process is carried out using the Haar Cascade method, while facial recognition is carried out using the Local Binary Pattern Histogram algorithm. The test results show that light intensity has a significant influence on system accuracy, but this system provides convenience in monitoring CCTV video in real-time through a webserver and improves security, especially in rooms by utilizing Internet of Things technology. The current facial recognition success rate is 72%. Therefore, for the subsequent development of the system, it is recommended to increase the success rate of facial recognition and also implement the File Transfer Protocol to ensure better and better system performance.