Elektirik ve Elektronik Mühendisliği/Makale Koleksiyonu

Bu koleksiyon için kalıcı URI

https://hdl.handle.net/20.500.12395/39751

Listeleniyor 1 - 20 / 97

Sensorless control of a PMSM drive using EKF for wide speed range supplied by MPPT based solar PV system
(KAUNAS UNIV TECHNOLOGY, 2020) Anwer, Abbas Mahmood Oghor.; Omar, Fuad Alhaj.; Bakir, Hale.; Kulaksiz, Ahmet Afsin.
In photovoltaic (PV) applications, employing Surface-Mounted Permanent Magnet Synchronous Motor (SMPMSM) can be a suitable option, especially for solar pumping and Heating, Ventilation, and Air Conditioning (HVAC) applications. However, when the motor loads are supplied from varying and limited energy sources, such as solar PV, it is vital to determine operating behavior and provide a stable operation for a wide range of operating conditions. In this study, the operating stability of Permanent Magnet Synchronous Motor (PMSM) was improved by sensorless Field Oriented Control (FOC) based on Extended Kalman Filter (EKF). In order to achieve optimal operation of the PV system under various meteorological conditions and load variations, an incremental conductance approach based maximum power point tracking (MPPT) system was introduced. For estimation of the speed of PMSM in wide speed range, instead of using a hybrid estimation strategy, fixed d-axis current with EKF was applied to the low-speed regions of SMPMSM, while in the medium and high speed regions, the d-axis current was set to zero. The major contributions of this paper are to reduce complexity of the control method and testing the method in a photovoltaic system with MPPT operation. The complete system was modeled in a Matlab/Simulink environment and simulation results are shown according to a wide range of operating conditions.
Usage of T-resonator method at determination of dielectric constant of fabric materials for wearable antenna designs
(ELSEVIER, 2019) Bayraktar, Özen.; Uzer, Dilek.; Gültekin, S. Sinan.; Top, Rabia.
In this study, determination of dielectric constants of fabric materials accurately by using T resonator method is aimed. Dielectric constant is one of the most important parameters that have a large effect on micro strip antenna performance. Small shifting at dielectric constant can cause huge alterations on electrical parameters of the antenna, especially at high frequencies. For this reason, a T resonator is designed on the wool felt fabric as the lower layer material at constant frequencies and the resonator dimensions are calculated. Using these calculated values, the finite element method is simulated with a High Frequency Electromagnetic Field Simulation (HFSS) as a resonator. After some size optimizations, the dielectric constant of the fabric was obtained correctly from the simulations. In order to validate the accuracy of the study and the values obtained, T resonator design on woolen felt material is manufactured and measurements of the resonator is realized with a vector network analyzer. Measurements of this resonator are compared with results from simulations and calculations. As a result, it has been shown that the dielectric constant of any fabric can be accurately determined using an easy, fast and inexpensive method and can be used in wearable micro strip antenna designs (C) 2019 Elsevier Ltd. All rights reserved.
Rapid control prototyping based on 32-bit ARM cortex-M3 microcontroller for photovoltaic MPPT algorithms
(INT JOURNAL RENEWABLE ENERGY RESEARCH, 2019) Alhajomar, Fuad.; Gokkus, Goksel.; Kulaksiz, Ahmet Afsin.
Since the beginning of the war in Syria, most of the electricity infrastructure has been destroyed, leaving millions with unreliable energy. In such regions vulnerable to energy insecurity, an alternative means of electricity production is sought. As an attractive option, the interest is directed to solar energy. However, because of a lack of expertise in solar energy conversion and the high cost of smart technology in these regions, people have typically used photovoltaic systems in primitive ways, in which the efficiency of solar energy conversion is low. There is, therefore, a need for inexpensive, easy-to-implement, yet highly efficient and high performing solutions. STMicroelectronics 32-bit ARM as a maximum power point tracking (MPPT) controller offers a potential solution to the problem of low conversion efficiency in stand-alone solar systems. In this study, using Matlab-Simulink and STMicrelectronics-32 bit ARM board, simulation and practical test is set up to evaluate the performance of the Perturbation & Observation, Incremental Conductance, and Fuzzy Logic MPPT algorithms, in order to determine the most appropriate algorithm to use in small scale solar energy systems. Therefore, the objective of this study is to explore rapid control prototyping tools for saving time and effort to the experts in the implementation process of the proposed systems. The results indicate the effectiveness of the fuzzy logic algorithm to draw more energy, decrease oscillation and provide a fast response under variable weather conditions. Furthermore, the three algorithms were able to find and track MPP.
Patch antenna with multiple slits and circular shaped
(APPLIED COMPUTATIONAL ELECTROMAGNETICS SOC, 2019) Atalah, Furkan.; Imeci, Mustafa.; Gungor, Oguzhan.; Imeci, S. Taha.; Durak, Tahsin.
In this paper, a circular shaped ground-fed patch antenna is designed, simulated, built and tested. The operating antenna frequency is 14.6 GHz with -15.68 dB input and 8.14 dB gain. Furthermore, the antenna have multiple slits in a circular main body, and also supported with triangle and rectangle shapes. The measurements of the fabricated patch antenna matches the simulation results.
HIC-net: A deep convolutional neural network model for classification of histopathological breast images
(PERGAMON-ELSEVIER SCIENCE LTD, 2019) Öztürk, Şaban.; Akdemir, Bayram.
In this study, a convolutional neural network (CNN) model is presented to automatically identify cancerous areas on whole-slide histopathological images (WSI). The proposed WSI classification network (HIC-net) architecture performs window-based classification by dividing the WSI into a certain plane. In our method, an effective pre-processing step has been added for WSI for better predictability of image parts and faster training. A large dataset containing 30,656 images is used for the evaluation of the HIC-net algorithm. Of these images, 23,040 are used for training, 2560 are used for validation and 5056 are used for testing. HIC-net has more successful results than other state-of-art CNN algorithms with AUC score of 97.7%. If we evaluate the classification results of HIC-net using softmax function, HIC-net success rates have 96.71% sensitivity, 95.7% specificity, 96.21% accuracy, and are more successful than other state-of-the-art techniques which are used in cancer research. (C) 2019 Elsevier Ltd. All rights reserved.
Flower pollination-feedforward neural network for load flow forecasting in smart distribution grid
(SPRINGER LONDON LTD, 2019) Shehu, Gaddafi Sani.; Çetinkaya, Nurettin.
Nature-inspired population-based metaheuristic flower pollination algorithm is proposed in solving load flow forecasting problem in smart distribution grid environment. The efficient approach involves training a feedforward neural network (FNN) with a new flower pollination algorithm (FPA). The idea is to perform short-term load flow forecasting in smart distribution network, thus maintaining system security due to intermittency of renewable energy penetration and power flow demand. Application of optimization algorithms such as FPA in training neural network improves accuracy, overcomes generalization ability of neural network, requires less data and prevents premature convergence problem in artificial intelligence solutions due to nonlinearity of parameters. The real load flow data are collected through distribution management system of Konya Organized Industrial Zone. The result obtained indicates strong improvement in error reduction using flower pollination optimization algorithm in training FNN for short-term load flow forecasting in smart distribution grid; the model is compared against FNN model and efficient support vector regression.
Exploring the offshore wind energy potential of Turkey based on multi-criteria site selection
(ELSEVIER SCIENCE BV, 2019) Argin, Mehmet.; Yerci, Volkan.; Erdogan, Nuh.; Kucuksari, Sadik.; Cali, Umit.
Wind energy is the leading form of non-hydro renewable energy source in terms of installed capacity in Turkey. It is among the most promising option for Turkey to decrease the energy dependence of external primary energy resources such as national gas and oil that diversifies the domestic share of energy sources in the national energy mix. However, offshore wind energy deployment has not gained satisfactory attention even though the country is surrounded by seas on three of its sides. Exploring Turkey's offshore wind power potential becomes an important task to serve this energy policy. This study presents a methodological framework for finding the most suitable offshore wind farm locations, meeting various multi-layer site selection criteria. The offshore wind energy resource is first assessed using the wind energy potential for 55 coastal regions where the nearshore meteorological stations are available in Turkey. Following on this analysis, a multi-criteria site selection work is carried out to identify the most suitable areas for offshore wind development. Wind Atlas Analysis and Application Program (WAsP) is then used to conduct statistical analysis to identify the most promising offshore wind farm locations. According to the pre-processing step of the framework, Bozcaada, Bandirma, Gokceada, Inebolu, and Samandag coastlines are found to be the most suitable locations for offshore wind farm development. Finally, the offshore wind energy potential of Turkey is estimated by using the micro-sitting configuration of wind turbines, considering sea depth, main wind direction, and distance to shore for the most feasible project locations. It is found that total estimated offshore wind power capacity at the specified sites is 1,629 MW.
Design and application of a smart diagnostic system for parkinson's patients using machine learning
(SCIENCE & INFORMATION SAI ORGANIZATION LTD, 2019) Channa, Asma.; Baqai, Attiya.; Ceylan, Rahime.
For analysis of Parkinson illness gait disabilities detection is essential. The only motivation behind this examination is to equitably and consequently differentiate among sound subjects and the one who is forbearing the Parkinson, utilizing IOT based indicative framework. In this examination absolute, 16 distinctive force sensors being attached with the shoes of subjects which documented the Multisignal Vertical Ground Reaction Force (VGRF). Overall sensors signals utilizing 1024 window estimate around the raw signals, utilizing the Packet wavelet change (PWT) five diverse characteristics that includes entropy, energy, variance, standard deviation and waveform length were derived and support vector machine (SVM) is to recognize Parkinson patients and healthy subjects. SVM is trained on 85% of the dataset and tested on 15% dataset. Preparation accomplice relies upon 93 patients with idiopathic PD (mean age: 66.3 years; 63% men and 37% ladies), and 73 healthy controls (mean age: 66.3 years; 55% men and 45% ladies). IOT framework included all 16 sensors, from which 8 compel sensors were appended to left side foot of subject and the rest of the 8 on the right side foot. The outcomes demonstrate that fifth sensor worn on a Medial part of the dorsum of right foot highlighted by R5 gives 903% accuracy. Henceforth this examination gives the knowledge to utilize single wearable force sensor. Hence, this examination deduce that a solitary sensor might help in differentiation amongst Parkinson and healthy subjects.
An efficient pipeline for abdomen segmentation in CT images
(SPRINGER, 2018) Koyuncu, Hasan.; Koyuncu, Hasan.; Sivri, Mesut.; Erdogan, Hasan.
Computed tomography (CT) scans usually include some disadvantages due to the nature of the imaging procedure, and these handicaps prevent accurate abdomen segmentation. Discontinuous abdomen edges, bed section of CT, patient information, closeness between the edges of the abdomen and CT, poor contrast, and a narrow histogram can be regarded as the most important handicaps that occur in abdominal CT scans. Currently, one or more handicaps can arise and prevent technicians obtaining abdomen images through simple segmentation techniques. In other words, CT scans can include the bed section of CT, a patient's diagnostic information, low-quality abdomen edges, low-level contrast, and narrow histogram, all in one scan. These phenomena constitute a challenge, and an efficient pipeline that is unaffected by handicaps is required. In addition, analysis such as segmentation, feature selection, and classification has meaning for a real-time diagnosis system in cases where the abdomen section is directly used with a specific size. A statistical pipeline is designed in this study that is unaffected by the handicaps mentioned above. Intensity-based approaches, morphological processes, and histogram-based procedures are utilized to design an efficient structure. Performance evaluation is realized in experiments on 58 CT images (16 training, 16 test, and 26 validation) that include the abdomen and one or more disadvantage(s). The first part of the data (16 training images) is used to detect the pipeline's optimum parameters, while the second and third parts are utilized to evaluate and to confirm the segmentation performance. The segmentation results are presented as the means of six performance metrics. Thus, the proposed method achieves remarkable average rates for training/test/validation of 98.95/99.36/99.57% (jaccard), 99.47/99.67/99.79% (dice), 100/99.91/99.91% (sensitivity), 98.47/99.23/99.85% (specificity), 99.38/99.63/99.87% (classification accuracy), and 98.98/99.45/99.66% (precision). In summary, a statistical pipeline performing the task of abdomen segmentation is achieved that is not affected by the disadvantages, and the most detailed abdomen segmentation study is performed for the use before organ and tumor segmentation, feature extraction, and classification.
Feature Selection using FFS and PCA in biomedical data classification with AdaBoost-SVM
(2018) Ceylan, Rahime; Barstugan, Mucahid
Recently, there has been an increasing trend to propose computer aided diagnosis systems for biomedical pattern recognition. A computer aided diagnosis method, which aims higher classification accuracy, is developed to classify the biomedical dataset. This process includes two types of machine learning algorithms: feature selection and classification. In this method, firstly, features were extracted from biomedical dataset, then the extracted features were classified by hybrid AdaBoost-Support Vector Machines (SVM) classifier structure. For feature selection, Forward Feature Selection (FFS) and Principal Component Analysis (PCA) algorithms were used, and the performance of the feature selection algorithms was tested by AdaBoost-SVM classifier. Following it, advantages and disadvantages of these algorithms were evaluated. Wisconsin Breast Cancer (WBC), Pima Diabetes (PD), Heart (Statlog) biomedical datasets were taken from UCI database and Electrocardiogram (ECG) signals were taken from Physionet ECG Database, and were used to test the proposed hybrid structure. The used two hybrid structures and other studies in the literature were compared with our findings. The obtained results show that the proposed hybrid structure has high classification accuracy for biomedical data classification
Computer aied control of cutting error in textile products
(2017) Çevik, Kerim Kürşat; Koçer, Hasan Erdinç
Günümüzde tekstil (deri, kumaş vb.) ürünleri kesim hataları ile ilgili denetimler şablon vasıtasıyla insan tarafından gözle yapılmaktadır. Hassas ölçüm gerektiren bu denetimlerin gözle yapılması, hem çok uzun zaman almakta hem de hata oluşma riskini artırmaktadır. Bu makalede tekstil parçalarının kesim hatalarını otomatik olarak tespit eden ve hatalı/hatasız parça ayrımı yapabilen görüntü işleme tabanlı endüstriyel kalite kontrol sistemi anlatılmıştır. Sistem insan denetiminden kaynaklanan hatayı en aza indirmekte ve birim zamanda kontrol edilen parça sayısını artırmaktadır. Gerçekleştirilen sistem, Panel PC, çizgi tarama kamerası, yürüyen bant sistemi, sepet kontrol ünitesi, görüntü işleme yazılımı ve kullanıcı kontrol ara yüzünden oluşmaktadır. Denetimi yapılacak kesilmiş tekstil parçaları yürüyen bant üzerinde kamera ve aydınlatma ünitesinin bulunduğu kısma gelir ve görüntü yakalanır. Yakalanan görüntü Panel PC'ye gönderilir ve görüntü işleme yazılımı vasıtasıyla kesim hatası olup olmadığı denetlenir. Denetim sonucuna göre yürüyen bandın sonunda yer alan sepet sistemi, pnömatik olarak ileri/geri hareket ettirilerek parçanın istenen sepete düşmesi sağlanır. 5 farklı şablona sahip 50 adet deri parçası için yapılan 150 denemeden 149 unda (%99.33 başarı oranı) doğru olarak hatalı/hatasız ayrımı yapılarak belirlenen sepete otomatik olarak düşürüldüğü görülmüştür.
Classification of Cervical Disc Herniation Disease using Muscle Fatigue based surface EMG signals by Artificial Neural Networks
(2017) Ozmen, Guzin; Ekmekcı, Ahmet Hakan
This study presents the classification of cervical disc herniationpatientsand healthy persons by using muscle fatigue information. Cervical disc herniationpatients suffer from neck pain and muscle fatigue in the neck increases these aches.Neck pain is the most common pain type encountered after back pain. The discomforts that occur in the neck region affect the daily quality of life, so the number of researches done in this area is increasing. In this studysurface Electromyography (EMG) signals wereused to examine muscle fatigue. EMG signals wereobtained from Trapezius and Sternocleidomastoid(SCM)muscles in the cervical region of 10 control subject and10 cervical disc herniation patients. Surface EMG waspreferred because it is a noninvasive method. In the first step of this study, EMG signals were filtered and adapted for analysis. In the second step, muscle fatigue wasdetermined using Median and Mean frequency values obtained by Fourier Transform and Welch methods.Feature extraction wasthe third step which was performed byShort Time Fourier Transform (STFT), Discrete Wavelet Transform (DWT) and Autoregressive method (AR). Finally, Artificial Neural Network (ANN)was used for classification. Training and test data werecreated by using feature vectors to classify patients with ANN. According to the results, the superior feature extraction method was investigatedon patient classification using muscle fatigue information.The best results were obtained by ARmethodwith %99 classification accuracy.Also, the best resultswereobtained by DWT with %100 classification accuracyforSCMmuscle. This study has contributed that AR and DWT are a suitable feature extraction methods for surface EMGsignals by providinghigh accuracyclassification with artificial intelligence methods forcervical disc herniationdisease. Besides, it is shown that muscle fatigue distinguishescervical disc herniationpatientsfrom healthy people
The classification of diseased trees by using kNN and MLP classification models according to the satellite imagery
(2016) Unlersen, Muhammed Fahri; Sabanci, Kadir
In this study, the Japanese Oak and Pine Wilt in forested areas of Japan was classified into two group as diseased trees and all other land cover area according to the 6 attributes in the spectral data set of the forest. The Wilt Data Set which was obtained from UCI machine learning repository database was used. Weka (Waikato Environment for Knowledge Analysis) software was used for classification of areas in the forests. The classification success rates and error values were calculated and presented for classification data mining algorithms just as Multilayer Perceptron (MLP) and k-Nearest Neighbor (kNN). In MLP neural networks the classification performance for various numbers of neurons in the hidden layer was presented. The highest success rate was obtained as 86.4% when the number of neurons in the hidden layer was 10. The classification performance of kNN method was calculated for various counts of neighborhood. The highest success rate was obtained as 72% when the count of neighborhood number was 2
Classification of Wheat Types by Artificial Neural Network
(2016) Yasar, Ali; Kaya, Esra; Sarıtas, Ismail
In this study, the types of wheat seeds are classified using present data with artificial neural network (ANN) approach. Seven inputs, one hidden layer with 10 neurons and one output has been used for the ANN in our system. All of these parameters were real-valued continuous. The wheat varieties, Kama, Rosa and Canadian, characterized by measurement of main grain geometric features obtained by X-ray technique, have been analyzed. Results indicate that the proposed method is expected to be an effective method for recognizing wheat varieties. These seven input parameters reaches the 10-neurons hidden layer of the network and they are processed and then classified with an output. The classification process of 210 units of data using ANN is determined to make a successful classification as much as the actual data set. The regression results of the classification process is quite high. It is determined that the training regression R is 0,9999, testing regression is 0,99785 and the validation regression is 0,9947, respectively. Based on these results, classification process using ANN has been seen to achieve outstanding success
Enerji nakil hatları güzergâh tespiti ve proje çiziminin otomasyonu
(2015) Eroğlu, Hasan; Aydın, Musa
Geçmişten günümüze kadar sürekli artış gösteren enerji talebini karşılayabilmek için yeni Enerji nakil hatlarına (ENH) ihtiyaç vardır. Yeni ENHler için en uygun güzergâhın tespiti ve projesinin çizimi, mühendisler için oldukça zaman alan ve karmaşık bir problemdir. Literatürde ENHlerin en uygun güzergâhının tespiti için birçok çalışma yapılmıştır. Ancak tüm bu işlemleri bir araya getirerek güzergâh tespitini kolaylaştıracak ve belirlenen bu güzergâhta hattın profilini çizerek projelendirmesini yapabilecek bir yazılım ihtiyacı olduğu görülmektedir. Bu çalışmada, ENHlerin en uygun güzergâh tespitinin yapılabilmesi için Coğrafi bilgi sistemlerini (CBS) kullanan bir ara yüz tasarlanmış, belirlenen güzergâh için ENH projesinin profilini çizebilen ve diğer tüm proje işlemlerini gerçekleştirebilen bir çizim programı yazılmıştır. Ayrıca ENH projelerinin görsel olarak üç boyutlu arazi modeli ve uydu fotoğrafı üzerinde gösterimi yapılarak kullanıcıların projeyi gerçek arazi modeli üzerinde kontrol edebilmeleri sağlanmıştır.
Judging Primary School Classroom Spaces Via Artificial Neural Networks Model
(2012) Arslan, H. Derya; Ceylan, Murat
An experimental study was conducted with 2nd grade students at primary schools in Turkey as part of an attempt to describe the ideal classroom space for primary education students. In the study, photographs of 20 different primary education classrooms were evaluated by 189 students. The students evaluated the images by means of surveys in which they were asked questions on four concepts: belonging, like (partiality), learning and safety. Reliability analyses of numeric data obtained from student surveys were made and they were subjected to various statistical analyses. In the second part of the study, the students' preferences for the classroom spaces were evaluated by means of Artificial Neural Networks (ANN) method, by using numeric data obtained from the student about concepts as well as the classroom space photos. Numeric data were treated and test procedures were performed to ensure that ANN makes decisions in the name of 2nd grade students. This is the first study in which numeric survey results and photographic characteristics have been used together. The ANN results were very similar to the students' evaluation of the ideal classroom space, particularly in terms of belonging and like (partiality) concepts.
Design Optimization of Pmsm by Particle Swarm Optimization and Genetic Algorithm
(2012) Mutluer, Mümtaz; Bilgin, Osman
One of the electric power researches is the design optimization studies of permanent magnet synchronous motors. The main advantages of design optimizations of permanent magnet synchronous motors are to contribute to comfort, cost, and especially to energy savings. Although absence of rotor windings affects efficiencies of permanent magnet synchronous motors, stringent selection of values of geometrical design parameters affects the efficiency. Artificial intelligence techniques are satisfactory in choosing of design parameters of electric motors. This study aims to provide the design optimization of surface mounted permanent magnet synchronous motor thus. First of all geometrical design parameters of the motor were identified and then preliminary analytical design and design optimization by using genetic algorithm and particle swarm algorithm were studied. The obtained efficiency results were compared with each others and the results is satisfactory. © 2012 IEEE.
Design and Performance Comparison of Variable Parameter Nonlinear Pid Controller and Genetic Algorithm Based Pid Controller
(2012) Korkmaz, Mehmet; Aydoğdu, Ömer; Doğan, Hüseyin
In this study, design and performance comparison of variable parameter nonlinear PID (NL-PID) and Genetic Algorithm (GA) based PID controller are achieved. To begin with the proposed method, an error function depending on the system input and output are defined to determine variable coefficients of the nonlinear PID controller. A new type non linear PID controller is designed by using defined error function. By this way, the nonlinear PID controller changes its own parameters over time according to the output response. Secondly, genetic algorithm based PID controller are defined to performance comparison of the proposed NL-PID controller and Ziegler-Nichols PID controller. Simulation results show that the effects of the PID controllers; nonlinear, GA based and Ziegler-Nichols. © 2012 IEEE.
Comparison of Artificial Immune Clustering with Fuzzy C-Means Clustering in the Sleep Stage Classification Problem
(2012) Dursun, M.; Güneş, S.; Özşen, S.; Yosunkaya, S.
Automatic Sleep Staging is an active field of research in sleep staging community. Many methods have been applied to get rid of the cumbersome of manual staging process. Even though very effective results were taken in some of these methods with respect to the classification accuracy, they are restricted either with their limited classification data or with lower number of classified stages like wake, sleepy and deep sleep. The accuracies obtained with methods for the classification of whole sleep stages are very low to apply in real sleep staging. One reason for this is the class imbalance in training data. Approximately half of one-night sleep consists of Non-REM2 stage while Wake, Non-REM1 and Non-REM3 stages are comparatively short duration. So, the used systems can converge to the characteristics of Non-REM2 stage. Taking equal amounts of data from each stage in training can be a solution for this but in this time a question arises: which samples should be picked from the each stage. Clustering schemes can play their roles for this question. In this study, we realized this clustering process with two methods: Fuzzy C-means Clustering (FCM) and Artificial Immune Clustering (AIC). We used 55 features that extracted from the sleep EEG, EOG and EMG signals of 8 subjects. We took a total of 300 data from each stage using FCM and AIC and classified these data with Artificial Neural Networks. The performances of the used clustering methods were compared on different number of features for which PCA was applied. The results showed that AIC was over-performed to FCM by obtaining a classification accuracy of 80.62% while this accuracy was 72.16% with FCM method used. © 2012 IEEE.
Clonal Selection Algorithm to Reduce Parallel Resonance Effect in Electrical Networks
(2012) Akbal, Bahadır; Ürkmez, Abdullah
Parallel resonance is significant problem in the electric networks. During parallel resonance, system impedance increase extremely. Therefore system voltage may be increase extremely. Due to the parallel resonance occurs between system impedance and capacitors of reactive power compensation, total capacitor power should be less than the parallel resonance power. In this study, parallel resonance point and power of transformer of the electric network, which was measured harmonic distortion, were determined by using search mechanism of CSA. Thus parallel resonance power of low voltage electric network can be calculated and maximum capacitor of compensation system can be determined. Coefficient of cloning in CSA was increased to obtain high affinity valuable results. But, in this case, the elapsed time increased. Increasing value of the elapsed time is undesirable case. To decrease value of the elapsed time, some changes in CSA were made to obtain high affinity value at short computational time. © 2012 IEEE.

Güncel Gönderiler