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31,456 Article Results

Voltage collapse point evaluation considering the load dependence in a power system stability problem

10.11591/ijece.v10i1.pp61-71
Zaid Garcia Sanchez , Jose Antonio González , Gustavo Crespo , Hernan Hernandez Herrera , Jorge Iván Silva
Voltage Stability has emerged in recent decades as one of the most common phenomena, occurrence in Electrical Power Systems. Prior researches focused on the development of algorithm indices to solve the stability problem and in the determination of factors with most influence in voltage collapse to solve the stability problem. This paper evaluates the influence that the load dependence has with the voltage on the phenomenon of the voltage stability and especially on the characteristics the collapse point or instability point. Load modeling used is detailed and comparisons of the results obtained are made with those described in the bibliography and those obtained with commercial software. The results of the load margin are also compared when a constant load or a voltage-dependent load is considered as well as the values obtained at the maximum load point and the point of voltage instability.
Volume: 10
Issue: 1
Page: 61-71
Publish at: 2020-02-01

IoT–smart contracts in data trusted exchange supplied chain based on block chain

10.11591/ijece.v10i1.pp438-446
S. Ganesh Kumar , A. Murugan , B. Muruganantham , B. Sriman
Internet of Things (IoT) assumes a critical part in the advancement of different fields. The IoT data trusted exchange in recent year extend of uses influence an awesome request and increasing scale. In such a platform, exchange the data sets that they require and specialist organization can search. However, the enough trust as the third-party mediators for data exchange in centralized infrastructure cannot provide. This paper proposes a blockchain for IoT data trusted exchange based on decentralized solution. In particular, the fundamental standards of blockchain in verify manner, individuals can communicate with each other without a confided in mediator intermediary. Blockchain enable us to have a distributed, digital ledger. IoT (Internet of Things) sensor devices (zigbee) utilizing blockchain technology to assert public availability of temperature records, tracking location shipment, humidity, preventing damage, data immutability. The sensor devices looking the temperature, location, damage of each parcel during the shipment to completely guarantee directions. In blockchain all data is got moved from one position to another, where a smart contract assesses against the product attributes. Ethereum blockchain and smart contracts atlast it gets through knowledge a design to be copied and presents its decentralized distributed digital ledger, auditable, transparent, features visually.
Volume: 10
Issue: 1
Page: 438-446
Publish at: 2020-02-01

Stand-alone backup power system for electrical appliances with solar PV and grid options

10.11591/ijeecs.v17.i2.pp689-699
A. H. Sabre , Wan Zuha Wan Hasan , Farah Hani Nordin , Mohd Zainal Abidin Ab-Kadir
For a portable backup power system, there is an essential need to contribute to increasing the energy conversion efficiency between the source and the electrical household appliances to overcome the recent issues of global warming. This paper proposes a Backup power system (BPS) compatible with the capability to match with two primary power sources; Grid-Connected power as an AC and solar-PV as a DC power source. This system composes of a rechargeable battery bank of about 300V, while the voltage matching concept keeps maintaining the battery fully charged as long as the source power is available. Matlab-based simulation has performed to prove the concept of the adopted voltage-matching. A relay switch circuit controlled by the main power source is used to change-over the load/appliances between direct link through bypassing the main power and the storage battery power when both options of main power are unavailable. The result shows the excellent utilization of the traditional BPS losses, the proposed topology can achieve about 99% power efficiency as compared with the traditional one.
Volume: 17
Issue: 2
Page: 689-699
Publish at: 2020-02-01

Performance evaluation of random forest with feature selection methods in prediction of diabetes

10.11591/ijece.v10i1.pp353-359
Raghavendra S , Santosh Kumar J
Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the PIMA Indian Diabtes data set of UCI repository using machine learning algorithm like Random Forest along with feature selection methods such as forward selection and backward elimination based on entropy evaluation method using percentage split as test option. The experiment was conducted using R studio platform and we achieved classification accuracy of 84.1%. From results we can say that Random Forest predicts diabetes better than other techniques with less number of attributes so that one can avoid least important test for identifying diabetes.
Volume: 10
Issue: 1
Page: 353-359
Publish at: 2020-02-01

A new partial image encryption method for document images using variance based quad tree decomposition

10.11591/ijece.v10i1.pp786-800
C. R. Revanna , C. Keshavamurthy
The proposed method partially and completely encrypts the gray scale Document images. The complete image encryption is also performed to compare the performance with the existing encryption methods. The partial encryption is carried out by segmenting the image using the Quad-tree decomposition method based on the variance of the image block. The image blocks with uniform pixel levels are considered insignificant blocks and others the significant blocks. The pixels in the significant blocks are permuted by using 1D Skew tent chaotic map. The partially encrypted image blocks are further permuted using 2D Henon map to increase the security level and fed as input to complete encryption. The complete encryption is carried out by diffusing the partially encrypted image. Two levels of diffusion are performed. The first level simply modifies the pixels in the partially encrypted image with the Bernoulli’s chaotic map. The second level establishes the interdependency between rows and columns of the first level diffused image. The experiment is conducted for both partial and complete image encryption on the Document images. The proposed scheme yields better results for both partial and complete encryption on Speed, statistical and dynamical attacks. The results ensure better security when compared to existing encryption schemes.
Volume: 10
Issue: 1
Page: 786-800
Publish at: 2020-02-01

Design of power factor meter using internet of things for power factor improvement, remote monitoring and data logging

10.11591/ijeecs.v17.i2.pp700-709
Teddy Surya Gunawan , Muhamad Hadzir Anuar , Mira Kartiwi , Zuriati Janin
Nowadays, many residential and commercial buildings that used electricity needs to take care the power factor to avoid penalty from the utility companies. A power factor that is close to one provides a good indicator for the overall power quality. Therefore, power factor improvement plays a significant role to reduce electricity consumption and more efficient system operation. In this paper, the design of power factor meter using Internet of Things will be discussed. Voltage and current sensors outputs were interfaced to Arduino, in which the real power and apparent power were calculated to determine the power factor. Results showed the effectiveness of our proposed device in measuring power factor. Moreover, the measured data points were logged in an SD card and can be accessed by computer with Matlab graphical user interface (GUI). In addition, IoT framework analysis for smart meter which can provide power factor improvement, remote monitoring, and data logging was further discussed in this paper.
Volume: 17
Issue: 2
Page: 700-709
Publish at: 2020-02-01

Parameter estimation of three-phase linear induction motor by a DSP-based electric-drives system

10.11591/ijece.v10i1.pp626-636
Nicolás Toro García , Yeison Alberto Garcés Gómez , Fredy Edimer Hoyos Velasco
This work describes a method to characterize a three-phase linear induction motor in order to determine the various parameters used in its per-phase equivalent circuit by a DSP-based electric-drives system. In LIM (Linear Induction Motor), the air gap is very large compared with the RIMs (Rotary Induction Motors). Further, the secondary part normally does not have slotted structure. It is just made of aluminum and steel plates. Therefore, the effective air gap is larger than the physical air gap. High air gap makes a larger leakage inductance. It leads to lower efficiency and lower power factor. DC resistance test will be done to determine the value of Rs. The primary Inductance Ls will be calculated by running the LIM at synchronous speed. The secondary parameters i.e. Llr and Rr′ will be calculated by blocked-mover test. The experiment for no load test is shown and include a DC motor coupled to the LIM under test. Two methods to calculate the secondary parameters are described.
Volume: 10
Issue: 1
Page: 626-636
Publish at: 2020-02-01

Assistant robot through deep learning

10.11591/ijece.v10i1.pp1053-1062
Robinson Jiménez-Moreno , Javier Orlando Pinzón-Arenas , César Giovany Pachón-Suescún
This article presents a work oriented to assistive robotics, where a scenario is established for a robot to reach a tool in the hand of a user, when they have verbally requested it by his name. For this, three convolutional neural networks are trained, one for recognition of a group of tools, which obtained an accuracy of 98% identifying the tools established for the application, that are scalpel, screwdriver and scissors; one for speech recognition, trained with the names of the tools in Spanish language, where its validation accuracy reach a 97.5% in the recognition of the words; and another for recognition of the user's hand, taking in consideration the classification of 2 gestures: Open and Closed hand, where a 96.25% accuracy was achieved. With those networks, tests in real time are performed, presenting results in the delivery of each tool with a 100% of accuracy, i.e. the robot was able to identify correctly what the user requested, recognize correctly each tool and deliver the one need when the user opened their hand, taking an average time of 45 seconds in the execution of the application.
Volume: 10
Issue: 1
Page: 1053-1062
Publish at: 2020-02-01

The conceptual model of information confrontation of virtual communities in social networking services

10.11591/ijece.v10i1.pp1043-1052
Kateryna Molodetska , Yuriy Tymonin , Ihor Melnychuk
Social networking services are one of the most popular mass media and are used as an effective tool for information confrontation due to their functional characteristics. Existing models of information confrontation take into account the redistribution between conflict parties of only one kind of resource, although in the social networking services there is a need to consider additional factors that determine the effectiveness of virtual communities’ opposition. A conceptual model of information confrontation of virtual communities in social networking services has been developed, and it includes three-layer dynamics of the number of actors, growth of information resources of virtual communities and dynamics of spending resources for the confrontation conduct. The model also takes into account the peculiarities of the antagonistic conflict of virtual communities’ actors through the choice of a differential equation that corresponds to the type of its dynamics. The offered conceptual model formalizes the behavior of virtual communities’ actors in the conditions of antagonistic conflict. At the same time, it allows to investigate the peculiarities of using different strategies to carry out the information fight of virtual communities in social networking services, to choose optimal strategies, to predict the development of conflicts in the information space and to develop effective measures to counter threats to the state’s information security.
Volume: 10
Issue: 1
Page: 1043-1052
Publish at: 2020-02-01

Small-signal modeling of current-mode controlled modular DC-DC converters using the state-space algebraic approach

10.11591/ijece.v10i1.pp139-150
Nuha M. Radaydeh , M. R. D. Al-Mothafar
Small-signal models are useful tools to preliminary understand the dynamics of interconnected systems like modular dc-dc converters which find a wide range of industrial applications. This work proposes a state-space-based averaged small-signal model in symbolic form for a peak current-mode controlled parallel-input/parallel-output buck converter operating in the continuous-conduction mode. In modeling the converter power-stage each module is independently represented. For modeling the current-mode control the state-space algebraic approach is used to incorporate the current-mode control-law into the power-stage equations. For each module two parasitic elements in addition to the current-loop sampling action are included in the derivation. Furthermore, the control-to-output voltage transfer functions are presented in symbolic form for two cases of interest: the first when the converter has two non-identical modules to study the effect of inductor mismatch, and the second when the converter is composed of n-connected identical modules to assess the effect of varying the number of modules. All responses from PSIM cycle-by-cycle simulations are in good agreement with the mathematical model predictions up to half the switching frequency.
Volume: 10
Issue: 1
Page: 139-150
Publish at: 2020-02-01

Alienor method applied to induction machine parameters identification

10.11591/ijece.v10i1.pp223-232
Latifa Khemici , M’hamed Bounekhla , Elghalia Boudissa
This paper presents an identification method to estimate simultaneously the electrical and mechanical induction machine (IM) parameters by using only the measured current and the corresponding phase voltage. This identification method is based on the output error and uses the multidimensional Alienor global optimization method as a minimization technique. Alienor method is essentially based on converting multivariable problem to monovariable one. To improve the Alienor method performance, the reducing transformation is proposed and compared with the genetic algorithm (GA). Firstly, the identification method is verified using the simulated data. Secondly, the validation is then confirmed by measured data from one machine. The corresponding computed transient and steady state currents agree well with the measured data. The results obtained show the superiority of the proposed Alienor method versus GA in terms of computing time.
Volume: 10
Issue: 1
Page: 223-232
Publish at: 2020-02-01

Reducing the hygroscopic swelling in MEMS sensor using different mold materials

10.11591/ijece.v10i1.pp494-499
Pinki Kumari , Kuldeep Singh , Anuj Singal
Today, Hygroscopic swelling is one of the biggest challenging problem of Epoxy mold compound (EMC) in packaging with Microelectromechanical system (MEMS) devices. To overcome this hygroscopic swelling problem of EMC and guard the devices, MEMS devices are molded in this paper with different Mold Compound (MC) i.e. titanium and ceramic etc. during their interconnection with the board. Also, a comparatively performance analysis of this various mold compound with MEMS pressure sensor has been studied in this paper at 60% humidity, 140 mol/m3 saturation concentration and 25 oC. It was observed that hygroscopic swelling does not take place in the titanium mold compound. But, titanium is very costly so we have to consider something cheaper material i.e. ceramic in this paper. The Hygroscopic swelling in Ceramic Mold Compound after 1 year is nearly 0.05mm which is very less than epoxy.
Volume: 10
Issue: 1
Page: 494-499
Publish at: 2020-02-01

Detecting malicious URLs using binary classification through adaboost algorithm

10.11591/ijece.v10i1.pp997-1005
Firoz Khan , Jinesh Ahamed , Seifedine Kadry , Lakshmana Kumar Ramasamy
Malicious Uniform Resource Locator (URL) is a frequent and severe menace to cybersecurity. Malicious URLs are used to extract unsolicited information and trick inexperienced end users as a sufferer of scams and create losses of billions of money each year. It is crucial to identify and appropriately respond to such URLs. Usually, this discovery is made by the practice and use of blacklists in the cyber world. However, blacklists cannot be exhaustive, and cannot recognize zero-day malicious URLs. So to increase the observation of malicious URL indicators, machine learning procedures should be incorporated. This study aims to discuss the exposure of malicious URLs as a binary classification problem using machine learning through an AdaBoost algorithm.
Volume: 10
Issue: 1
Page: 997-1005
Publish at: 2020-02-01

A new model for iris data set classification based on linear support vector machine parameter's optimization

10.11591/ijece.v10i1.pp1079-1084
Zahraa Faiz Hussain , Hind Raad Ibraheem , Mohammad Alsajri , Ahmed Hussein Ali , Mohd Arfian Ismail , Shahreen Kasim , Tole Sutikno
Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.
Volume: 10
Issue: 1
Page: 1079-1084
Publish at: 2020-02-01

Real time observer-based stator fault diagnosis for IM

10.11591/ijece.v10i1.pp210-222
Khadidja El Merraoui , Abdellaziz Ferdjouni , M’hamed Bounekhla
This paper proposes a delta connected IM model that takes the Stator winding Inter-Turn Short Circuit (SITSC) fault into account. In order to detect the fault and evaluate its severity, an observer based FDI method is suggested. It allows the generation of residual using extended Kalman filter (EKF). To overcome the problem of the EKF initialization, the cyclic optimization method is applied to determine its tuning parameters. The advantage of the proposed approach is the real-time quantification of the fault severity and the quick fault detection. Using numerical simulation under both the healthy and the faulty conditions, the proposed IM model and EKF-based FDI approach are confirmed. Experimental results obtained by a real-time implementation on test-bench validate the simulated results.
Volume: 10
Issue: 1
Page: 210-222
Publish at: 2020-02-01
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