Muammer Ozdemir

Comparison of statistical methods and wavelet energy coefficients for determining two common PQ disturbances: Sag and swell

This paper presents statistical methods and wavelet based effective feature extraction method for power quality (PQ) disturbance classification problem. The PQ signals used in this study are two common types named as swell and sag. First, the signals consisting of sag and swell are determined by using statistical methods. In the previous studies, validation of PQ disturbances for obtaining skewness and kurtosis coefficients were created at the zero crossing points of the voltage signal. In practice, occurrence of disturbances at these points is not guaranteed. So in this paper, disturbances are constituted in eight different points (0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°) having different characteristics. Skewness and kurtosis coefficients of the constituted signals are calculated in local frames. These coefficients are obtained during one period long sliding frame. It has been observed that in swell and sag events this statistical method gives different results depending on moment occurrence of disturbances. So another method is needed. Multi-resolution analysis (MRA) technique of discrete wavelet technique (DWT) and Parsevals theorem are employed to extract the energy distribution features of sag and swell signals constituted in eight different points (0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°).

Classification of two common power quality disturbances using wavelet based SVM

Development of technology increased the attention of the research community on power quality (PQ) disturbance classification problem. This paper presents wavelet based effective feature extraction method and support vector machines (SVM) for PQ disturbance classification problem. Two common kinds of power quality disturbances, voltage sag and swell, are considered in this paper. After multi-resolution signal decomposition of PQ disturbances, feature vector can be obtained. Multi-resolution analysis (MRA) technique of discrete wavelet technique (DWT) and Parsevals theorem are employed to extract the energy distribution features of sag and swell signals. SVM are used to classify these feature vectors of PQ disturbances. Performance of two kinds of method used in SVM is compared aspect of training time and training error.

An online parameter monitoring method for power transformers based on DEA

Transformers are the most prominent elements of power systems. In some cases, it may be desired to monitor their equivalent circuit parameters in order to establish the electrical models of them or to determine some of the potential failures in advance. In this work, a novel method that provides online monitoring of equivalent circuit parameters of transformer is presented. This method is based on differential equation algorithm (DEA) that uses a small number of voltage and current samples. The effectiveness of proposed method is shown by computer simulations.

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.

A Novel Parameter Identification Method for Single-Phase Transformers by Using Real-Time Data

Equivalent circuit parameters of transformers are related to the condition of their windings. Information concerning winding deformations, failures, and temperature can be acquired by monitoring these parameters. Methods used for the determination of electrical parameters generally require disconnection of the transformer from the power system. In this paper, a novel method which uses real-time data of the transformer to determine its parameters is presented. Therefore, this method eliminates the need for the disconnection of the transformer from the power system. In the method, winding parameters are obtained by applying the differential equation algorithm to the fundamental frequency components of transformer data. Fundamental frequency components of the currents and voltages are computed by using the discrete cosine transform. Transformer core parameters are also computed via core losses and the polynomial curve-fitting method with the least squares error method. The proposed method has been tested and validated by simulations and experiments.

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)

A new wind power measurement algorithm: A sample calculation for OMU-Dedebuzagi hill

In this work, both unscented transform (UT) and frequency analysis techniques are used for calculating potential wind power of OMU - Dedebuzagi hill based on a year wind data. UT technique reduces process time since it uses only a few samples of probability density function of random variables into system. Weibull distribution with three parameters is used for predicting wind power. The wind power values for measurement site are then compared to another proposed method called frequency analysis.