Travelling-wave based fault distance estimation for series compensated transmission lines using ATP-EMTP.

In this paper, a traveling wave is used for determining fault distance of series compensated transmission lines using frequencies of fault generated and voltage harmonics

İletim Hatlarında Arıza Tipinin Belirlenmesine Yönelik Tamamlanan Çalışmalar

Günümüzde kısa süreli elektrik kesintileri bile büyük üretim kayıplarına yol açmaktadır. Aynı zamanda elektrik kesintileri gündelik yaşamı olumsuz etkilemektedir. Bu yüzden iletim hatlarında oluşabilecek kısa devre arızalarına olabildiğince hızlı şekilde müdahale etmek gerekmektedir. Teknolojideki gelişmeler ile iletim hatlarında arıza tipini belirlemeye yönelik birçok farklı yöntem uygulanmıştır. Bu çalışmada iletim hatlarında oluşan kısa devre arıza tiplerinin bulunmasına yönelik tamamlanan yöntemler incelenmiştir

Matlab / Simulik Simulation of Solar Energy System

Son zamanlarda artan nüfus ve ilerleyen teknoloji nedeniyle elektrik enerjisine olan talep her geçen gün artmaktadır. Fosil kaynaklarının sınırlı olması ve doğaya büyük zararlar vermesi yenilenebilir enerji kaynaklarını daha da önemli bir konu haline getirmektedir. Yenilenebilir enerji kaynaklarından biri ise güneş enerjisidir. Bu çalışmada Matlab/Simulink paket programı kullanarak sabit sıcaklık ve sabit ışınım katsayısında 2846 W’lık güneş paneli tasarlanmıştır. Bu güneş panelleri ile elde edilen DC gerilimi, AC gerilime çevirmek için Sinüzoidal Darbe Genişlik Modülasyonuna (SDGM) sahip evirici kullanılmıştır. Bu tasarım sonucunda sistemin gerilim, akım harmonik bozulumları ve yükün şebekeden çektiği güç gibi parametreler incelenmiştir

Classification of Short Circuit Faults Occurring in Transmission Lines by Using Transient Current Signals, J48 And Naïve Bayes Machine Learning Algorithms

In this study, a method has been used to classify the short circuit faults. In this method, the current values of three phases and zero, positive, and negative sequence current component values obtained through the application of modal transformation on the line currents have been used in order to obtain the classification features. In each case of fault, in order to ensure that the classification features remain within specific range of values, the current values of the three phases for one cycle after fault occurrence are reduced being divided by the biggest peak value among the current values of again these three phases. Similarly for each case of fault, the sequence current components for one cycle after fault occurrence are reduced being divided by the biggest peak value among the sequence current components. After the signals are reduced, the classification features are obtained using Root Mean Square (RMS) values of the three phase current signals, RMS values of the sequence current component signals and the proportions of these RMS values to each other. The classification features obtained are used with Naive Bayes and J48 machine learning methods to classify the short circuit faults occurring in transmission lines. While the Alternative Transients Program (ATP/EMTP) is used to model the transmission lines, the Waikato Environment for Knowledge Analysis (WEKA) program is used for Naive Bayes and J48 machine learning algorithms

Classification of Short Circuit Faults in Transmission Lines Using Different Machine Learning Algorithms and Transient Regime Current Signals

In this study a method was conducted to find fault types. One period three phase line currents and the sequence current components were used as data window. The transient signals were reduced by a certain method. To do this, in the event of each fault, the maximum peak value of the three phase currents was found, and then each of the line currents was divided by this value. A same procedure was applied to the sequence current signals. This method ensures that the greatest peak values of the signals remain within certain values. The RMS entropy values of transient signals and the ratios of RMS entropy values to each other were used to obtain classification features. Then these features were used together with Random Forest (RF), IBK, and PART to find fault type. The obtained simulation results indicate that the method used in this study is very effective method for the classification of short circuit faults