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Showing papers in "International Journal of Electrical and Computer Engineering in 2021"


Journal ArticleDOI
TL;DR: The proposed approach uses title, abstract, and keywords of the paper, in addition to the categories topics to perform the classification process, and documents are classified and clustered into the primary categories based on the highest measure of cosine similarity between category weight and documents weights.
Abstract: Increasing progress in numerous research fields and information technologies, led to an increase in the publication of research papers. Therefore, researchers take a lot of time to find interesting research papers that are close to their field of specialization. Consequently, in this paper we have proposed documents classification approach that can cluster the text documents of research papers into the meaningful categories in which contain a similar scientific field. Our presented approach based on essential focus and scopes of the target categories, where each of these categories includes many topics. Accordingly, we extract word tokens from these topics that relate to a specific category, separately. The frequency of word tokens in documents impacts on weight of document that calculated by using a numerical statistic of term frequency-inverse document frequency (TF-IDF). The proposed approach uses title, abstract, and keywords of the paper, in addition to the categories topics to perform the classification process. Subsequently, documents are classified and clustered into the primary categories based on the highest measure of cosine similarity between category weight and documents weights.

28 citations


Journal ArticleDOI
TL;DR: The deep learning and image processing demonstrated high performance in early Covid-19 detection and shows to be an auxiliary detection way for clinical doctors and thus contribute to the control of the pandemic.
Abstract: COVID-19 disease has rapidly spread all over the world at the beginning of this year. The hospitals' reports have told that low sensitivity of RT-PCR tests in the infection early stage. At which point, a rapid and accurate diagnostic technique, is needed to detect the Covid-19. CT has been demonstrated to be a successful tool in the diagnosis of disease. A deep learning framework can be developed to aid in evaluating CT exams to provide diagnosis, thus saving time for disease control. In this work, a deep learning model was modified to Covid-19 detection via features extraction from chest X-ray and CT images. Initially, many transfer-learning models have applied and comparison it, then a VGG-19 model was tuned to get the best results that can be adopted in the disease diagnosis. Diagnostic performance was assessed for all models used via the dataset that included 1000 images. The VGG-19 model achieved the highest accuracy of 99%, sensitivity of 97.4%, and specificity of 99.4%. The deep learning and image processing demonstrated high performance in early Covid-19 detection. It shows to be an auxiliary detection way for clinical doctors and thus contribute to the control of the pandemic.

28 citations


Journal ArticleDOI
TL;DR: The experimental results showed the superiority of the proposed smart fire detection system in terms of affordability, effectiveness, and responsiveness as the system uses the Ubidots platform, which makes the data exchange faster and reliable.
Abstract: House combustion is one of the main concerns for builders, designers, and property residents Singular sensors were used for a long time in the event of detection of a fire, but these sensors can not measure the amount of fire to alert the emergency response units To address this problem, this study aims to implement a smart fire detection system that would not only detect the fire using integrated sensors but also alert property owners, emergency services, and local police stations to protect lives and valuable assets simultaneously The proposed model in this paper employs different integrated detectors, such as heat, smoke, and flame The signals from those detectors go through the system algorithm to check the fire's potentiality and then broadcast the predicted result to various parties using GSM modem associated with the system To get real-life data without putting human lives in danger, an IoT technology has been implemented to provide the fire department with the necessary data Finally, the main feature of the proposed system is to minimize false alarms, which, in turn, makes this system more reliable The experimental results showed the superiority of our model in terms of affordability, effectiveness, and responsiveness as the system uses the Ubidots platform, which makes the data exchange faster and reliable

28 citations


Journal ArticleDOI
TL;DR: A new method of detection COVID-19 fever symptoms depending on IoT cloud services to solve the higher time delay of checking the crowded clients that enter public or private agencies which can lead to a dangerous field to spread the disease.
Abstract: This paper presents a new method of detection COVID-19 fever symptoms depending on IoT cloud services to solve the higher time delay of checking the crowded clients that enter public or private agencies which can lead to a dangerous field to spread the disease. An automatically checking process is suggested using a practical experiment is developed using (ESP8266 Node MCU, Ultrasonic (SR-04), RFID (RC522), human body temperature (MAX30205) sensors, and ThingSpeak platform). Where Node MCU is open-source hardware used to transmit the received data (human temperature sensor) from the (MAX30205) to the cloud platform (ThingSpeak) then alert the monitoring manager user when the collected data reached a critical value that specified previously and automatically take action to solve this situation. At the same time, the cloud platform will provide a graphical representation of the received data to display it using different monitoring devices such as (computers, mobiles, and others).

23 citations


Journal ArticleDOI
TL;DR: The study successfully proposes an early cancer disease model based on five different supervised algorithms such as logistic regression, decision tree, decisionTree, random forest, and K-nearest neighbor based on a 10-fold cross-validation approach.
Abstract: One of the most critical issues of the mortality rate in the medical field in current times is breast cancer. Nowadays, a large number of men and women is facing cancer-related deaths due to the lack of early diagnosis systems and proper treatment per year. To tackle the issue, various data mining approaches have been analyzed to build an effective model that helps to identify the different stages of deadly cancers. The study successfully proposes an early cancer disease model based on five different supervised algorithms such as logistic regression (henceforth LR), decision tree (henceforth DT), random forest (henceforth RF), Support vector machine (henceforth SVM), and K-nearest neighbor (henceforth KNN). After an appropriate preprocessing of the dataset, least absolute shrinkage and selection operator (LASSO) was used for feature selection (FS) using a 10-fold cross-validation (CV) approach. Employing LASSO with 10-fold cross-validation has been a novel steps introduced in this research. Afterwards, different performance evaluation metrics were measured to show accurate predictions based on the proposed algorithms. The result indicated top accuracy was received from RF classifier, approximately 99.41% with the integration of LASSO. Finally, a comprehensive comparison was carried out on Wisconsin breast cancer (diagnostic) dataset (WBCD) together with some current works containing all features.

22 citations


Journal ArticleDOI
TL;DR: Simulation results, obtained using MATLAB/SIMULINK program, show that the MPPT techniques improve the lowest efficiency resulted without control, but CS provided significant advantages over others in view of low implementation cost, and fast computing time.
Abstract: Photovoltaic systems (PV) are one of the most important renewable energy resources (RER). It has limited energy efficiency leading to increasing the number of PV units required for certain input power i.e. to higher initial cost. To overcome this problem, maximum power point tracking (MPPT) controllers are used. This work introduces a comparative study of seven MPPT classical, artificial intelligence (AI), and bio-inspired (BI) techniques: perturb and observe (P&O), modified perturb and observe (M-P&O), incremental conductance (INC), fuzzy logic controller (FLC), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and cuckoo search (CS). Under the same climatic conditions, a comparison between these techniques in view of some criteria’s: efficiencies, tracking response, implementation cost, and others, will be performed. Simulation results, obtained using MATLAB/SIMULINK program, show that the MPPT techniques improve the lowest efficiency resulted without control. ANFIS is the highest efficiency, but it requires more sensors. CS and ANN produce the best performance, but CS provided significant advantages over others in view of low implementation cost, and fast computing time. P&O has the highest oscillation, but this drawback is eliminated using M-P&O. FLC has the longest computing time due to software complexity, but INC has the longest tracking time.

22 citations


Journal ArticleDOI
TL;DR: Two ways of conventional neural networks are used named Alex Net and Res Net models with and without data augmentation, which increases the number of training images in DL without the need to add new photos, it will appropriate in the case of small datasets.
Abstract: The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is very important. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This recognition can be completed without a human by utilizing plant leaf pictures. There are many methods employed for the classification and detection in machine learning (ML) models, but the combination of increasing advances in computer vision appears the deep learning (DL) area research to achieve a great potential in terms of increasing accuracy. In this paper, two ways of conventional neural networks are used named Alex Net and Res Net models with and without data augmentation involves the process of creating new data points by manipulating the original data. This process increases the number of training images in DL without the need to add new photos, it will appropriate in the case of small datasets. A self-dataset of 200 images of diseases and healthy citrus leaves are collected. The trained models with data augmentation give the best results with 95.83% and 97.92% for Res Net and Alex Net respectively.

21 citations


Journal ArticleDOI
TL;DR: In this article, a decentralized IPFS and blockchain-based framework for the auto insurance sector is presented, which regulates the activities in terms of insurance claims for automobiles and automates payments.
Abstract: The advancing technology and industrial revolution have taken the automotive industry by storm in recent times. The auto sector’s constantly growing demand has paved the way for the automobile sector to embrace new technologies and disruptive innovations. The multi-trillion dollar, complex auto insurance sector is still stuck in the regulations of the past. Most of the customers still contact the insurance company by phone to buy new policies and process existing insurance claims. The customers still face the risk of fraudulent online brokers, as policies are mostly signed and processed on papers which often require human supervision, with a risk of error. The insurance sector faces a threat of failure due to losing and misconception of policies and information. We present a decentralized IPFS and blockchain-based framework for the auto insurance sector that regulates the activities in terms of insurance claims for automobiles and automates payments. This article also discusses how blockchain technology’s features can be useful for the decentralized autonomous vehicle’s ecosystem.

20 citations


Journal ArticleDOI
TL;DR: In this paper, features of fundamental frequency, energy, zero-crossing rate, fourier parameter, and various combinations of them are extracted from the data vector and the principal component analysis (PCA) algorithm is used to reduce the number of features.
Abstract: Recognizing the sense of speech is one of the most active research topics in speech processing and in human-computer interaction programs. Despite a wide range of studies in this scope, there is still a long gap among the natural feelings of humans and the perception of the computer. In general, a sensory recognition system from speech can be divided into three main sections: attribute extraction, feature selection, and classification. In this paper, features of fundamental frequency (FEZ) (F0), energy (E), zero-crossing rate (ZCR), fourier parameter (FP), and various combinations of them are extracted from the data vector, Then, the principal component analysis (PCA) algorithm is used to reduce the number of features. To evaluate the system performance. The fusion of each emotional state will be performed later using support vector machine (SVM), K-nearest neighbor (KNN), In terms of comparison, similar experiments have been performed on the emotional speech of the German language, English language, and significant results were obtained by these comparisons.

20 citations


Journal ArticleDOI
TL;DR: The results confirmed that NETPI provides flexibility to deal with several NODEMCU controllers in a single control framework and the proposed system shows its applicability in monitoring and controlling home appliances remotely.
Abstract: The developments of the internet of things (IoT) technologies fascinated the universe and provided great opportunities to introduce these innovations in smart house networks. Smart home automation is highly required these days. Smart home automation is a collection of electronic devices connected to monitor and control in the market home appliance remotely. However, it is still needed to design a friendly and reliable system since the system mainly depends on the devices used and the environment of the network. NETPI and BLYNK are IoT frameworks used for hardware-agnostic with smartphones, websites, private clouds, system security, data mining, and deep learning. The results confirmed that NETPI provides flexibility to deal with several NODEMCU controllers in a single control framework. The proposed system shows its applicability in monitoring and controlling home appliances remotely.

19 citations


Journal ArticleDOI
TL;DR: In this paper, three different passive cooling approaches are considered, namely phase change material (PCM), fin heat sink, and radiative cooling covering the discussions on the achieved cooling efficiency.
Abstract: The electrical output performance of photovoltaic (PV) modules are sensitive to temperature variations and the intensity of solar irradiance under prolonged exposure. Only 20% of solar irradiance is converted into useful electricity, and the remaining are dissipated as heat which in turns increases the module operating temperature. The increase in module operating temperature has an adverse impact on the open-circuit voltage (Voc), which results in the power conversion efficiency reduction and irreversible cell degradation rate. Hence, proper cooling methods are essential to maintain the module operating temperature within the standard test conditions (STC). This paper presents an overview of passive cooling methods for its feasibility and economic viability in comparison with active cooling. Three different passive cooling approaches are considered, namely phase change material (PCM), fin heat sink, and radiative cooling covering the discussions on the achieved cooling efficiency. The understanding of the above-mentioned state-of-the-art cooling technologies is vital for further modifications of existing PV modules to improve the efficiency of electrical output.

Journal ArticleDOI
TL;DR: The main contribution of the proposed study is to review the literature about managing and monitoring T2D with daily PA through wearable devices and sensors and to highlight challenges and future trends.
Abstract: Globally, the aging and the lifestyle lead to rabidly increment of the number of type two diabetes (T2D) patients. Critically, T2D considers as one of the most challenging healthcare issue. Importantly, physical activity (PA) plays a vital role of improving glycemic control T2D. However, daily monitoring of T2D using wearable devices/ sensors have a crucial role to monitor glucose levels in the blood. Nowadays, daily physical activity (PA) and exercises have been used to manage T2D. The main contribution of the proposed study is to review the literature about managing and monitoring T2D with daily PA through wearable devices and sensors. Finally, challenges and future trends are also highlighted.

Journal ArticleDOI
TL;DR: This work found that SVM linear grid performs better than other SVM models and can be made to improve the productivity.
Abstract: Sentiment Analysis is a current research topic by many researches using supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments and social networks. The Twitter data set is analyzed using support vector machines (SVM) classifier with various parameters. The content of tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individual. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve the productivity. The performance of SVM radial kernel, SVM linear grid and SVM radial grid was compared and found that SVM linear grid performs better than other SVM models.

Journal ArticleDOI
TL;DR: An unsupervised feature learning method to improve the performance of various classifiers using a stacked sparse autoencoder (SSAE) was proposed and showed superior performance over other methods.
Abstract: Presently, the use of a credit card has become an integral part of contemporary banking and financial system. Predicting potential credit card defaulters or debtors is a crucial business opportunity for financial institutions. For now, some machine learning methods have been applied to achieve this task. However, with the dynamic and imbalanced nature of credit card default data, it is challenging for classical machine learning algorithms to proffer robust models with optimal performance. Research has shown that the performance of machine learning algorithms can be significantly improved when provided with optimal features. In this paper, we propose an unsupervised feature learning method to improve the performance of various classifiers using a stacked sparse autoencoder (SSAE). The SSAE was optimized to achieve improved performance. The proposed SSAE learned excellent feature representations that were used to train the classifiers. The performance of the proposed approach is compared with an instance where the classifiers were trained using the raw data. Also, a comparison is made with previous scholarly works, and the proposed approach showed superior performance over other methods.

Journal ArticleDOI
TL;DR: The pros and cons of implementation of autonomous vehicles are looked at and various attacks against the different type of sensors on-board an autonomous vehicle are covered.
Abstract: Autonomous vehicles have been invented to increase the safety of transportation users. These vehicles can sense their environment and make decisions without any external aid to produce an optimal route to reach a destination. Even though the idea sounds futuristic and if implemented successfully, many current issues related to transportation will be solved, care needs to be taken before implementing the solution. This paper will look at the pros and cons of implementation of autonomous vehicles. The vehicles depend highly on the sensors present on the vehicles and any tampering or manipulation of the data generated and transmitted by these can have disastrous consequences, as human lives are at stake here. Various attacks against the different type of sensors on-board an autonomous vehicle are covered.

Journal ArticleDOI
TL;DR: The calculated results on the 33 nodes test system have shown that ESFO has proficiency for determining the best location and size of DG with higher quality than SFO, and is a reliable approach for the DG optimization problem.
Abstract: Installation of distribution generation (DG) in the distribution system gains many technical benefits. To obtain more benefits, the location and size of DG must be selected with the appropriate values. This paper presents a method for optimizing location and size of DG in the distribution system based on enhanced sunflower optimization (ESFO) to minimize power loss of the system. In which, based on the operational mechanisms of the original sunflower optimization (SFO), a mutation technique is added for updating the best plant. The calculated results on the 33 nodes test system have shown that ESFO has proficiency for determining the best location and size of DG with higher quality than SFO. The compared results with the previous methods have also shown that ESFO outperforms to other methods in term of power loss reduction. As a result, ESFO is a reliable approach for the DG optimization problem.

Journal ArticleDOI
TL;DR: The findings of this paper reveal that blockchain can meet the privacy and security requirements of fog computing; however, there are several limitations of blockchain that should be further investigated in the context of Fog computing.
Abstract: Due to the expansion growth of the IoT devices, Fog computing was proposed to enhance the low latency IoT applications and meet the distribution nature of these devices. However, Fog computing was criticized for several privacy and security vulnerabilities. This paper aims to identify and discuss the security challenges for Fog computing. It also discusses blockchain technology as a complementary mechanism associated with Fog computing to mitigate the impact of these issues. The findings of this paper reveal that blockchain can meet the privacy and security requirements of fog computing; however, there are several limitations of blockchain that should be further investigated in the context of Fog computing.

Journal ArticleDOI
TL;DR: The present problem and challenges of incident detection in VANET technology are discussed and the recently proposed methods for early incident techniques are reviewed and studies them.
Abstract: As a component of intelligent transport systems (ITS), vehicular ad hoc network (VANET), which is a subform of manet, has been identified It is established on the roads based on available vehicles and supporting road infrastructure, such as base stations An accident can be defined as any activity in the environment that may be harmful to human life or dangerous to human life In terms of early detection, and broadcast delay VANET has shown various problems The available technologies for incident detection and the corresponding algorithms for processing The present problem and challenges of incident detection in VANET technology are discussed in this paper The paper also reviews the recently proposed methods for early incident techniques and studies them

Journal ArticleDOI
TL;DR: A precise writing survey on sequence-to-sequence learning with neural network and its models and followed a methodology that shows the potential of applying these models to real-world applications.
Abstract: We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.

Journal ArticleDOI
TL;DR: This study posits intelligent systems via the use of machine learning frameworks to detect malicious attacks from data traffic through the employment of deep learning neural network to effectively differentiate between acceptable and non-acceptable data packets on a network data traffic.
Abstract: Today’s popularity of the internet has since proven an effective and efficient means of information sharing. However, this has consequently advanced the proliferation of adversaries who aim at unauthorized access to information being shared over the internet medium. These are achieved via various means one of which is the distributed denial of service attacks-which has become a major threat to the electronic society. These are carefully crafted attacks of large magnitude that possess the capability to wreak havoc at very high levels and national infrastructures. This study posits intelligent systems via the use of machine learning frameworks to detect such. We employ the deep learning approach to distinguish between benign exchange of data and malicious attacks from data traffic. Results shows consequent success in the employment of deep learning neural network to effectively differentiate between acceptable and non-acceptable data packets (intrusion) on a network data traffic.

Journal ArticleDOI
TL;DR: In this paper, the authors proposed an experimental study to analyze and evaluate the power efficiency of a PV system installed in Baghdad city, Iraq, which is equipped with an automatic-sprayer cleaning system that is powered by the same PV system.
Abstract: One of the challenges facing investment in photovoltaic (PV) energy is the accumulation of dust on the surface of the PV panels due to frequent dust storms in many countries, including Iraq. Surface dust particles reduce solar irradiance which declining the electrical performance of the PV solar systems. Therefore, this paper proposes an experimental study to analyze and evaluate the power efficiency of a PV system installed in Baghdad city, Iraq. The performance of dusty solar PV array is compared with that of the clean array of the same PV system. The clean solar array is equipped with an automatic-sprayer cleaning system that is powered by the PV system. The automatic cleaning system utilized in the test system reduces human effort by cleaning the PV array using closed-cycle water with low energy consumption (less than 10 Wh). The PV array under test is part of a 15 kW grid-tied PV system. The experimental results show significant improvement in the performance parameters of efficiency, performance ratio, and the energy gain compared to the clean array. Furthermore, the experimental study contributes to a reduction in CO2 emission, which is substantial for the Iraqi environment that suffers from predominate fossil-fuel power plants.

Journal ArticleDOI
TL;DR: This work presents the physical and mathematical groundings of rail electrical parameters (dc and ac resistance, ac internal and external inductance) and experimental results available in the literature, discussing variability and reliability for each interface.
Abstract: Running rails in electrified transportation systems are the interface element for several phenomena related to system performance, electromagnetic compatibility and safety: useful voltage at rolling stock, short circuit current, induced voltage, stray current, and track circuit operation. This work presents the physical and mathematical groundings of rail electrical parameters (dc and ac resistance, ac internal and external inductance) and experimental results available in the literature, discussing variability and reliability for each interface. The results consist thus of the identification of the relevant rails longitudinal electrical parameters, the presentation of a set of reliable experimental values, and the discussion of the best approach to manage their variability and uncertainty.

Journal ArticleDOI
TL;DR: In this paper, a battery technologies overview for energy storage applications in power systems is given, including lead-acid, lithium-ion, nickel-cadmium, nickelmetal hydride, sodium-sulfur and vanadium-redox flow batteries.
Abstract: Battery technologies overview for energy storage applications in power systems is given. Lead-acid, lithium-ion, nickel-cadmium, nickel-metal hydride, sodium-sulfur and vanadium-redox flow batteries are overviewed. Description, graphical representation, advantages and disadvantages as well as technical characteristics are given for all technologies. Differences and similarities between different battery technologies are perceived. Battery technologies are considered with respect to peak shaving, load leveling, power reserve, integration of renewable energy, voltage and frequency regulation and uninterruptible power supply applications. According to technical characteristics for overviewed technologies, comparison between battery storage technologies is given through diagrams which are uniformed. Comparison is done according to specific power, specific energy, power density, energy density, power cost, energy cost, lifetime, lifetime cycles, cell voltage and battery technology efficiency.

Journal ArticleDOI
TL;DR: It will be shown that the electronic control of the permeability of the ferrite material of the antenna leads effectively to a significant shift in its resonant frequency, and hence to an overall improvement in the performance of the communication system.
Abstract: The antenna is considered as one of the most fundamental elements in wireless communication systems, especially in mobile devices. Desirable specifications of antennas include covering wide range of operating frequencies, while maintaining high quality of system performance over the whole range of operating frequencies. Therefore, the ability of tuning the resonant frequency of the antenna without altering its physical dimensions would be highly recommended in up-and-coming designs of antennas in mobile devices. This research work proposes a model for tuning the operating frequency of the inverted F-antenna over a reasonably wide range of frequencies, via altering the electromagnetic properties of its ferrite material. In this proposed model, it will be shown that the electronic control of the permeability of the ferrite material of the antenna leads effectively to a significant shift in its resonant frequency, and hence to an overall improvement in the performance of the communication system.

Journal ArticleDOI
TL;DR: The k-NN process has been developed for the sake of running sensitivity by performing normalized distance using normalized Euclidean distance so that in this paper, it is able to forecast and become a future model and apply it to Business Intelligence and analysis.
Abstract: Forecasting involves all areas in predicting future events. Many problems can be solved by using a forecasting approach to become a study in the field of data science. Forecasting that learns through data in the light age is able to solve problems with large-scale data or big data. With the big data, the performance of the k-Nearest Neighbor (k-NN) method can be tested with several accuracy measurements. Generally, accuracy measurement uses MAPE so it is necessary to conduct sensitivity on MAPE by combining it with the detection rate which is the difference technique. In addition, the k-NN process has been developed for the sake of running sensitivity by performing normalized distance using normalized Euclidean distance so that in this paper using the crude palm oil (CPO) price dataset, it is able to forecast and become a future model and apply it to Business Intelligence and analysis. In the final stage of this paper, the accuracy value in doing big data forecasting on CPO prices with MAPE is 0.013526% and MAPE sensitivity combined with a detection rate of 0.000361% so that future processes using different methods need to involve detection rates.

Journal ArticleDOI
TL;DR: Camel behavior search algorithm is proposed as a new method for estimating the five different parameters for single diode model of PV solar module and has an acceptable accuracy in obtaining the five estimated parameters.
Abstract: Finding accurate mathematical model of electrical equivalent circuit of solar photovoltaic (PV) cell is crucial to achieve and improve maximum power point, simulation design and efficiency computations for solar energy system. Due to the nonlinearity of the characteristic of solar PV cell, optimization methods are the best for estimating the electrical model parameters which lead to accurate estimating I-V curve. In this paper, camel behavior search algorithm is proposed as a new method for estimating the five different parameters for single diode model of PV solar module. This is tested on multicrystalline KC 200GT PV module. A measurement data of the module is used to verify and test the consistency of accurately estimating the set of parameters that govern the characteristics I-V relationship of solar cell. The simulation results show that the current-voltage characteristic and power-voltage curve obtained are matching to the measured experimental data set. For performance evaluation, the proposed method is simple, fast in convergence response and has an acceptable accuracy in obtaining the five estimated parameters.

Journal ArticleDOI
TL;DR: The grid-connected PV system combined with reserve battery storage can effectively improve the stability of the system and reduce the cost of power generation.
Abstract: With the increasing demand for solar energy as a renewable source has brought up new challenges in the field of energy. However, one of the main advantages of photovoltaic (PV) power generation technology is that it can be directly connected to the grid power generation system and meet the demand of increasing energy consumption. Large-scale PV grid-connected power generation system put forward new challenges on the stability and control of the power grid and the grid-tied photovoltaic system with an energy storage system. To overcome these problems, the PV grid-tied system consisted of 8 kW PV array with energy storage system is designed, and in this system, the battery components can be coupled with the power grid by AC or DC mode. In addition, the feasibility and flexibility of the maximum power point tracking (MPPT) charge controller are verified through the dynamic model built in the residential solar PV system. Through the feasibility verification of the model control mode and the strategy control, the grid-connected PV system combined with reserve battery storage can effectively improve the stability of the system and reduce the cost of power generation. To analyze the performance of the grid-tied system, some real-time simulations are performed with the help of the system advisor model (SAM) that ensures the satisfactory working of the designed PV grid-tied System.

Journal ArticleDOI
TL;DR: In this article, the authors introduced the fractional order to the HIV-1 infection of CD4 T-cells which consists of a system of ordinary differential equations with certain initial conditions.
Abstract: In this paper, We introduce the fractional order to the HIV-1 infection of CD4 T-cells which consists of a system of ordinary differential equations with certain initial conditions. We study the changing effect of many parameters. The fractional derivative is described in the Caputo sense. The Adomian decomposition Method (Shortly, ADM) have been studied to find the approximate solution of the proposed system. The nonlinear term is dealt with the help of Adomian polynomials. Numerical results are presented with graphical justifications to show the accuracy of the proposed methods.

Journal ArticleDOI
TL;DR: An intelligent control strategy for a microgrid system consisting of Photovoltaic panels, grid-connected, and li-ion battery energy storage systems proposed by integrating artificial neural network (ANN) for the estimation of the battery state of charge (SOC) and for the control of bidirectional converter.
Abstract: In this paper, an intelligent control strategy for a microgrid system consisting of Photovoltaic panels, grid-connected, and li-ion battery energy storage systems proposed. The energy management based on the managing of battery charging and discharging by integration of a smart controller for DC/DC bidirectional converter. The main novelty of this solution are the integration of artificial neural network (ANN) for the estimation of the battery state of charge (SOC) and for the control of bidirectional converter. The simulation results obtained in the MATLAB/Simulink environment explain the performance and the robust of the proposed control technique.

Journal ArticleDOI
TL;DR: It is deduced that the proposed control system produces better results than the classical DTC, while better quality steady-state performance is produced in sensorless implementation for a wide speed range.
Abstract: By using the direct torque control (DTC), robust response in ac drives can be produced. Ripples of currents, torque and flux are oberved in steady state. space vector modulation (SVM) applied in DTC and used for a sensorless induction motor (IM) with fuzzy sliding mode speed controller (FSMSC) is studied in this paper. This control can minimize the torque, flux, current and speed pulsations in steady state. To estimate the rotor speed and stator flux the model reference adaptive system (MRAS) is used that is designed from identified voltages and currents. The FSMSC is used to enhance the efficiency and the robustness of the presented system. The DTC transient advantage are maintained, while better quality steady-state performance is produced in sensorless implementation for a wide speed range. The drive system performances have been checked by using Matlab Simultaion, and successful results have been obtained. It is deduced that the proposed control system produces better results than the classical DTC.