Sürdürülebilir Kalkınma Amaçları
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- MakaleAçık Erişim2018Scopus SJRQ2A new embedded power quality event classification system based on the wavelet transform
Eristi, Belkis; Yildirim, Ozal; Eristi, Huseyin; Demir, Yakup
In this paper, a real time embedded intelligent recognition system is proposed for diagnosing the power quality problems. The intelligent recognition system has a structure capable of classifying and detecting the power quality problems in real time. Hardware applications of the wavelet transform and decision tree classifier are realized inside the recognition system using with field programmable gate array (FPGA) device. These methods operating as embedded in the FPGA environment provide real-time diagnosis of power quality problems. A new approach of this recognition system is its capability to simultaneously detect a power quality event in the power systems and power quality disturbances occurring on each phase following the event. In this paper, 2 different recognition systems that online and offline are presented. The online recognition system operates in the fields with signal processing and classification structures embedded. In the offline system, the distinctive features of the event signals obtained from the online system are used and these signals are classified in the computer environment by means of the least square support vector machines. A prototype model of the power system is created in the laboratory environment in order to test the FPGA-based online intelligent recognition system, determine accuracy rates and evaluate its performance. Power quality event types are created in wide parameter ranges on this model. Obtained results from both recognition systems indicated the hardware and software designs of our embedded systems are quite effective, fast, and have high success performance.
AtıfScopus48WoS35OpenAlex50
İncele - BildiriYalnız künye2016An Online Electric Power Quality Disturbance Detection System
Yildirim, Ozal; Eristi, Belkis; Eristi, Huseyin; Ünal, Sencer; Erol, Yavuz; Demir, Yakup
In this study, an FPGA based online monitoring system was developed for detection of power quality disturbances. The developed system has the capability to instantly detect commonly seen PQ disturbances such as voltage sag, voltage swell and interruption occurring on real-time three-phase voltage signals obtained from the electric power system. The data of the detected disturbance events are transferred to the visual software prepared in the computer environment by means of the UDP/IP communication module embedded in the FPGA. This PQ disturbance detection system was tested upon installation at three different measuring points across the campus of Tunceli University. It was seen from the test results examined that the detection system successfully identified the PQ disturbance events.
- BildiriYalnız künye2016A Real-Time Power Quality Disturbance Detection System Based On The Wavelet Transform
Eristi, Belkis; Yildirim, Ozal; Eristi, Huseyin; Demir, Yakup
Disturbances occurring on voltage signals following switching events or faults on power systems are defined as power quality problem. Power quality problems result in significant financial losses in the power systems in addition to faulty operation or breakdown of sensitive loads connected to the power system. Therefore, power quality problems should be swiftly detected and eliminated. This paper proposes a real-time embedded detection system for the purpose of identification of power quality disturbances. The detection system developed has been created using the wavelet transform in the Field Programmable Gate Array (FPGA) media. The general structure of FPGA-based detection system comprises two fundamental parts: hardware and software. Hardware structure comprises signal input card, FPGA and computer components. The software stage includes FPGA and graphical interface software. An experimental setup of the power system has been created in laboratory environment in order to test the FPGA-based detection system, determine accuracy rates and assess its success. Various power quality disturbances have been created over this model in wide parameter intervals. When the results obtained for the real-time power quality disturbance detection system developed under this study are examined, it has been seen that hardware and software designs are quite effective, fast and of high achievement performance.
- BildiriYalnız künye2015An FPGA Based Power Quality Monitoring System
Yildirim, Ozal; Eristi, Belkis; Eristi, Huseyin; Ünal, Sencer; Erol, Yavuz; Demir, Yakup
In this study, a new FPGA based monitoring system has been developed for monitoring the power quality. Designed system performs the calculations of power quality parameters on real-time voltages and currents datas that are obtained from the network. FPGA devices that have recently become more popular in digital signal processing field is used in the structure of monitoring system. Designed power quality monitoring system has been established on electrical distribution panels of Tunceli University Vocational Schools. When the obtained test results are evaluated, it has been observed that the proposed power quality monitoring system is successful.
AtıfWoS1İncele - MakaleAçık Erişim2015Scopus SJRQ2Automatic classification of harmonic data using k-means and least square support vector machine
Eristi, Huseyin; Tümen, Vedat; Yildirim, Ozal; Eristi, Belkis; Demir, Yakup
In this paper, an effective classification approach to classify harmonic data has been proposed. In the proposed classifier approach, harmonic data obtained through a 3-phase system have been classified by using k-means and least square support vector machine (LS-SVM) models. In order to obtain class details regarding harmonic data, a k-means clustering algorithm has been applied to these data first. The training of the LS-SVM model has been realized with the class details obtained through the k-means algorithm. To increase the efficiency of the LS-SVM model, the regularization and kernel parameters of this model have been determined with a grid search method and the training phase has been realized. Backpropagation neural network and J48 decision tree classifiers have been applied to the same data and results have been obtained for the purpose of comparing the performance of the LS-SVM model. The real data obtained from the output of distribution system have been used to assess the performance of the proposed classifier system. The obtained results and comparisons suggest that the proposed classifier system approach is quite efficient at classifying harmonic data.
AtıfWoS1OpenAlex3İncele - MakaleAçık Erişim2014Scopus SJRQ1Automatic recognition system of underlying causes of power quality disturbances based on S-Transform and Extreme Learning Machine
Eristi, Huseyin; Yildirim, Ozal; Eristi, Belkis; Demir, Yakup
In this paper, a new S-Transform and Extreme Learning Machine (ST-ELM)-based event recognition approach for the purpose of classifying power quality (PQ) event signals automatically has been proposed. In this approach, the distinctive features of the PQ event signals have been obtained with the S-Transform-based feature extraction. The feature vector obtained with feature extraction has been applied as input to the ELM classifier. Ten different classification procedures were determined within the framework of this study to assess the performance of the ELM classifier on PQ event data. Real PQ event data and synthetic PQ event data obtained from MATIAB/Simulink environment have been used in these procedures. Also, three different PQ event data sets, which are formed by adding noises of 20, 30 and 50 dB to the synthetic PQ event data respectively, have been used in order to assess the performance of the proposed approach on noisy conditions. According to the results of performance evaluations, the proposed ST-ELM-based PQ event recognition system has a very high performance of recognizing PQ event data. Besides, classification of noisy data showed that the proposed approach is robust at recognizing noisy data. The performance of the ST-ELM-based recognition system on PQ data shows that this approach has an effective recognition structure that can be used in real power systems. (C) 2014 Elsevier Ltd. All rights reserved.
AtıfWoS63OpenAlex93İncele - MakaleAçık Erişim2013Scopus SJRQ1Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines
Eristi, Huseyin; Yildirim, Ozal; Eristi, Belkis; Demir, Yakup
In this paper, a new optimal feature selection based power quality event recognition system is proposed for the classification of power quality events. While Apriori algorithm is capable of processing categorical data, an effective feature vector, which represents distinctive features of digital power quality event data, has been obtained by means of the proposed k-means based Apriori algorithm feature selection approach. The proposed k-means based Apriori algorithm feature selection approach is presented with a power quality event recognition system. In the power quality event recognition system, normalization and segmentation processes have been applied to three-phase event voltage signals. Using 9-level multiresolution analysis, wavelet transform coefficients of the event signals have been obtained. By applying nine different feature extraction processes to these coefficients, a 90 dimensional feature vector belonging to three-phase event voltage signals has been extracted. Optimal feature vector has been obtained by applying the k-means based Apriori algorithm feature selection approach to the obtained feature vector, which has been applied as the last step to the input of the least squares support vector machine classifier and recognition performance results have been obtained. Real power quality event data have been used to evaluate the performance of the proposed feature selection approach and power quality event recognition system. According to the results, the proposed k-means based Apriori algorithm feature selection approach and power quality event recognition system are efficient, reliable and applicable and classify three-phase event types with a high degree of accuracy. (C) 2013 Elsevier Ltd. All rights reserved.
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