Majlesi Journal of Electrical Engineering
Volume:15 Issue: 3, Sep 2021

To moderate global warming, conventional fossil fuels are depleted. As the population increased with the rising standard of living and industrial growth which impairment of the global environment and the greenhouse occurrence, which are frequently exposed by unlimited use of fossil fuels. The generation of electric power loads increases the power demand on the basics of modern power technology development. Several benefits can be attained by installing the distribution generation with the quality and reliability of power delivered. But, the global energy problem can be resolved by renewable energy sources as an alternative energy generation. Technological developments in the last decade have increased the use of renewable energy sources. In worldwide, several renewable energy sources are used to attain their own power demand. The photovoltaic (PV) generation is the essential renewable energy source to serve the increasing electrical loads. The fastest-growing PV system has the naturally available energy sources of robust evolution with elegant benefits. The foremost objective of this paper is to examine the performance of the PV system with various maximum power point tracking (MPPT) algorithms. The solar irradiance and temperature make complex to track the MPPT of PV systems. This review is about various MPPT algorithms like online, offline, and hybrid methods. Selected of the algorithms from each discussion are simulated in MATLAB/Simulink environment to match their performance in footings of the dynamic response and efficiency of the PV system under the variations of solar irradiance and temperature. An explanation and discussion of the PV system are achieved with the study of different types of MPPT algorithms of PV systems.

Matched Field Processing (MFP) is one of the most famous algorithms for source detection and underwater localization. Traditional MFP relies on a match between the received signal at the hydrophone array and a replica signal, which is constructed using Green’s Function, then by scanning the space in range and depth to provide an estimation of source position in shallow water and deep water. Different environment models relying on Green’s function exist for constructing the replica signal; this includes normal modes in a shallow water waveguide, the Lloyd-Mirror Pattern, and the Image model. Using the proposed estimation algorithm, here, an analytical Lloyd-Mirror model is developed based on the reflection from the target surface for a case where a target is located in the source signal propagation path. So, in this paper, a new underwater acoustic target localization algorithm using the generalized Lloyd-Mirror Pattern is presented. This idea is verified using an acoustic data from a 2019 underwater communication trial in Grand Passage, Nova Scotia, Canada.

Cloud computing in the field of high-performance distributed computing has emerged as a new development in which the demand for access to resources via the Internet is presented in distributed servers that dynamically scale Are acceptable. One of the important research issues that must be considered to achieve efficient performance is fault tolerance. Fault tolerance is a way to find faults and failures in a system. Predicting and reducing errors play an important role in increasing the performance and popularity of cloud computing. In this study, an adaptive workflow scheduling approach is presented to increase fault tolerance in cloud computing. The present approach calculates the probability of failure for each resource according to the execution time of tasks on the resources. In the present method, a deadline is set for each of the tasks. If the task is not completed within the specified time, the probability of failure in the source increases and subsequent tasks are not sent to the desired source. The simulation results of the proposed method show that the proposed idea can work well on workflows and improve service quality factors.

Amongst the approaches proposed to estimate parameters of a chirp signal sequentially, i.e., the central frequency and the chirp rate, algorithms, such as discrete polynomial-phase transform (DPT) and promoted DPT, exhibit acceptable estimation accuracy. Algorithms intended to estimate phase parameters sequentially, diminish the order of polynomials in complex exponential power to lower-order polynomials, and then estimate these two parameters using the NLS method in a given single exponential mode. The NLS method, which uses FFT to decrease the computational load of frequency domain search, encounters predicaments. In this work, we assessed the bias of algorithms intended for estimating of phase parameters sequentially using the RBF method. The results of investigating the bias of estimators indicated the improved accuracy of the DPT and promoted DPT algorithms in estimation using the RBF method instead of NLS and also than DCFT method.

Analyzes of electric discharge are sometimes tedious and relatively expensive. To overcome this problem, some scientists are working on variance analysis projects. The article presents the results of an electric discharge experiment performed on silicone, porcelain and heat tempered glass insulators at triple junction (TJ). The objective of this study is to develop a polynomial and Gaussian simple regression model (Polynomial simple linear regression (SLR) model and Gaussian simple nonlinear regression model) considering different parameters by analyzing the observed quantitative data. The dependent variable or variable to be explained (discharge current) is a function of four independent variables (explanatory variables): voltage application time (t), solid insulator surface condition: net surface (t’), worn rubbed surface with sandpaper (t’’) and active electrode diameter (diam). Indeed, this study set up precise prediction models generating good estimates of the studied variables values. A polynomial SLR model capable of predicting electric discharge with an adjusted coefficient of determination (R2 adj) of 0.9774 for t and t’, 0.9773 for t" and 0.9945 for diam. While (R2 adj) for the Gaussian model reaches 0.9989 for t and t’, 0.9998 for t’’. By considering this, these models are strongly recommended to better understand and characterize the discharge and contribute to the improvement of the insulation and its design for better optimization and high performance.

One of the most significant handicaps and disadvantages for the proper operation of the polymer membrane in a PEMFC fuel cell energy system is the distribution of water. In this paper, we propose a mathematical model for defining the static and dynamic characteristics of energy behaviour (voltage, electricity, and relative humidity) for various input operating parameters (hydrogen, oxygen, water flow rates, temperature and current). This energy phenomenon is used in a wide range of operating conditions to ensure the exploitation of the energy produced, which will be modeled by a recent practicable and achievable graphical formalism, the macroscopic energy representation (MER), which is used because of its simplicity which feasibility, and is based on the action/reaction principle and controlled by a simple inversion method. This behavior is designed to deduce and recommend an energy management plan for the PEMFC system that takes into account the various states of flooding and drought and contributes to an optimal humidity level for the system's implementation. The simulation results show that for this model to operate correctly, the Relative Humidity must be in the neighborhood of 100% for the device to be effective.

Today, due to the advent of the powerful photo editing software packages, it has become relatively easy to create forgery images. Recognizing the correctness of digital images becomes important when those images are used as evidence in legal, forensic, industrial, and military applications. One of the most common ways to forge images is copy move forgery, in which one part of the image is copied and pasted in another part of the same image. So far, various methods have been proposed for detecting copy move forgery, but these methods are not able to detect copy move forgery with different challenges of noise, rotation, scale, and detection of symmetrical images with high accuracy. In this paper, presents an enhanced hybrid method based on local and frequency feature extraction for image copy move forgery detection, which has a very high resistance to above challenges, both individually and simultaneously and has provided good identification accuracy. In this method, the combination of Discrete Wavelet Transform, Scale Invariant Feature Transform and Local Binary Pattern are used simultaneously. The forged area is chosen in such a way that at least both methods used in this proposed method have consensus about the forgery of that area. Various experiments and analyses on the MICC database show that the proposed methods, despite the above challenges and we have reached the accuracy 98.81% both separately and simultaneously, which has improved significantly compared to other methods used in this field.

DGs and capacitor banks are installed to optimize the performance of many distribution networks. Typically, the problem of optimizing the overall performance of the distribution network is examined with multi-objective purposes. Network optimization purposes are usually varied and sometimes contradictory. Therefore, the problem search space is very large due to the variety of purposes. This paper presents a modified Pareto local search function for optimal placement of DGs and capacitor banks. To limit the search space and find Pareto points, a new combination method including Pareto chart and a weight function has been used. The optimal operation of the distribution network is performed by three single objective functions related to the voltage stability index, voltage profile of buses and power loss. In this method, a modified per-unit system is presented to align single objective functions and their weighting coefficients. The network is studied with three different loads. So that, the network is examined in the final stage by increasing the load and reaching bus voltage stability margins. The particle swarm optimization method is applied to solving placement problems. In addition, locating and sizing DG and capacitor banks, tap setting of on load tap changer transformer is adjusted by the proposed method. To show the effectiveness of the purposed method, simulations are applied to 69 bus radial system. The results indicated the favorable advantage of the proposed method to improve the overall performance of the distribution network.

The 4-moduli set residue number system (RNS), , with a wide dynamic range, has recently been proposed as a balanced 4-moduli set for utilizing the cases that demand fast calculations such as deep learning and implementation of asymmetric cryptographic algorithms. Up to now, only an unsigned reverse converter has been designed for this moduli set. Thus, there is a need for two separate units, a sign detection circuit, and a comparator to use this set in cases requiring sign and comparison. Nevertheless, the existence of these components demands high hardware that makes the implementation of the RNS impractical. Therefore, this paper presents the design of a sign detection circuit and a signed reverse converter that can overcome this problem by reusing the hardware. To achieve an integrated hardware design, first, we optimized the previous unsigned reverse converter for this 4-moduli set and next, we derived an approach from the structure of the reverse convertor for detecting signs and recognizing comparators. Finally, using the sign signals extracted from the reverse converter, we change reverse convertor into a unit that perform sign detection and comparison. The simulation has been conducted using ISE Design Suite 14.7 tool and the Spartan6 family technology. Empirical results show that, the proposed multifunctional unit has an approximately identical performance with respect to delay and area compared to the previous reverse converter. Besides, the proposed signed reverse converter relies on a 46% and 28% reduction in area and delay compared to the previous unsigned reverse converter which uses a comparator and also a multiplexer to detect a sign in the output.

In recent years, exponential growth of communication devices in Internet of Things (IoT) has become an emerging technology which facilitates heterogeneous devices to connect with each other in heterogeneous networks. This communication requires different level of Quality-of-Service (QoS) and policies depending on the device type and location. To provide a specific level of QoS, we can utilize emerging new technological concepts in IoT infrastructure, software-defined network (SDN) and, machine learning algorithms. We use deep reinforcement learning in the process of resource management and allocation in control plane. We present an algorithm that aims to optimize resource allocation. Simulation results show that the proposed algorithm improved network performances in terms of QoS parameters, including delay and throughput compared to Random and Round Robin methods. Compared to similar methods the performance of the proposed method is also as good as the fuzzy and predictive methods.