Turgay Yalcin

Feature vector extraction by using empirical mode decomposition for power quality disturbances

This work presents a relatively new method known as empirical mode decomposition (EMD) for power quality disturbances. In a comprehensive and wider range of approaches and engineering activities, there is a increasing concern for power system disturbances monitoring techniques. The need of increasing performances in terms of accuracy and computation speed is permanently demanding new efficient processing techniques on power system visualization. For system monitoring, feature extraction of a disturbed power signal provides information that helps to detect and diagnose the responsible fault for power quality disturbance. Traditionally, monitoring spectral and harmonic analysis of dynamic systems is based on Fourier based transforms and the wavelets. The Fourier transform usually has been used in the past for analysis of stationary and periodic signals. Qualification to providing a more accurate ‘real-time’ demonstration of a signal without any artifacts imposed by the non-locally adaptive limitations of the fast Fourier transform (FFT) and wavelet processing. In this work, the first step of Hilbert-Huang transform (HHT), EMD, has been regarded as a powerful tool for adaptive analysis of non-linear and non-stationary signals.

Pattern recognition method for identifying smart grid power quality disturbance

Pattern recognition of power quality (PQ) disturbances in electrical power distribution system especially in smart grids has developed into crucial topic for system equipment and end-user. Methodically analyzing the PQ disturbances can develop and maintain smart grids effectiveness. This study presents signal processing method Hilbert Huang Transform and computational intelligence methods such as Support Vector Machines, C4.5 Decision tree for automatic detection and classification of voltage sag in power grid. In this study based on experimental studies, Hilbert Huang based pattern recognition technique was used to investigate power signal to diagnose voltage sag in power grid. SVM and Decision Tree (C4.5) were operated and their achievements were matched for calculation error and CPU time. According to these analysis, decision tree algorithm produces the best solution.

Noise cancellation and feature generation of voltage disturbance for identification smart grid faults

Identification of system disturbances and detection of them guarantee smart grids power quality system reliability and long lasting life of the power system. The key goal of this study is to generate non - time consuming features for CPU, for recognizing different types of non-stationary and non-linear smart grid faults based on signal processing techniques. This paper proposes a new solution for real time power system monitoring against power quality faults focusing on voltage sag and noise. EEMD is used for noise reduction with first intrinsic mode function (imf1). Hilbert Huang Transform (HHT) is used for generating instantaneous amplitude (IA) and instantaneous frequency (IF) feature of real time voltage sag power signal. PQube, power quality and energy monitor was used to acquire the distortions, several other parameters such as Total Harmonic Distortion (THD). The proposed power system monitoring system is able to detect power system voltage sag disturbances and capable of recognize and remove EMI (Electromagnetic Interference)-Noise.

Computational Intelligence Methods for Identifying Voltage Sag in Smart Grid

Turgay Yalcin, Ondokuz Mayis University, Electrical&Electronic Engineering Faculty, 55139, Samsun, TURKEY, +903623121919,turgay.yalcin@omu.edu.tr Advances in Science, Technology and Engineering Systems Journal Vol. 2, No. 3, 412-419 (2017)

An investigation about blackout for power systems

In this article, it is studied about power blackout which is caused by the chaotic situations. In the introduction section, the main effects that trigger the system to go blackout are defined. Then, it is mentioned about the system modeling methods to understand the blackout event better and prevent it before it happens. First, to study blackout dynamics in the power transmission grid, the OPA model is applied to the real time North America blackout data. Secondly, a cascade model is composed of connecting probability cascade failures and dealt with mathematical equations. Finally, the nose curves occurring between load current and load voltage are calculated when load is increased and power factor of system is at a steady level.