detection & classification of power quality (pq) disturbances are the most important problems in distribution systems. in this paper, a new approach for the detection and classification of single and combined pq disturbances is proposed which utilizes fuzzy logic and particle swarm optimization (pso) algorithms. in this approach, first suitable features of the waveform of pq disturbances ar...

In this paper, a SVM classifier based on S-Transform is presented for power quality disturbances classification. Firstly, seven types of PQ events are created using Matlab simulation. These signals are analyzed to detect and localize PQ events via S-Transform by visual inspection. Then five significant features of the PQ disturbances are extracted from the S-Transform output. Afterwards, PQ dis...

In this paper a new power quality monitoring (PQM) system for detection and classification of power quality voltage disturbances is presented. The implementation of the proposed PQM system in a graphical user interface (GUI) is also presented. The wavelet transform (WT) is used for de-noising and detection of the disturbances on power voltage line. Detected disturbances are separated using bina...

This paper presents a wavelet transform and Support Vector Machine (SVM) based algorithm for classification of power quality (PQ) disturbances. The features extracted through the wavelet transform are trained by a SVM for classification of power quality disturbances. Five types of disturbances are considered for the classification problem. The simulation results reveal that the combination of w...

The power quality (PQ) signals are traditionally analyzed in the time-domain by skilled engineers. However, PQ disturbances may not always be obvious in the original time-domain signal. Fourier analysis transforms signals into frequency domain, but has the disadvantage that time characteristics will become unobvious. Wavelet analysis, which provides both time and frequency information, can over...

The paper presents an S-Transform based multilayer perceptron neural network (MLP) classifier for the identification of power quality (PQ) disturbances.The proposed method is used to extract the three input features (Standard deviation, peak value and variances) from the distorted voltage waveforms simulated using parametric equations. The features extracted through S-transform are trained by a...

Electric power quality (PQ) problems are very important aspects due to the increase in the number of loads which are sensitive to power disturbances. One of the important issues in the PQ problems is to detect and classify disturbance waveforms automatically in an efficient approach, because the possible solutions can be determined after the disturbance types are detected. This paper proposes a...

Abstract— This paper considers two important classification algorithms for to classify several power quality disturbances. Artificial Neural Network (ANN) and support vector machine (SVM). The last one is a novel algorithm that has shown good performance in general patterns classification. Nevertheless, Multilayer Perceptron Artificial Neural Network (MLPANN) is the most popular and most widely...

In this paper some patterns based on discrete Wavelet transform are studied for detection and identification of both, low frequency disturbances, like flicker and harmonics, and high frequency disturbances, such as transient and sags. Daubichies4 Wavelet function is used as a base function to detect and identify due to its frequency response and time localization information properties. Based o...

Power Quality is defined as the study of the quality of electric power lines. The detection and classification of the different disturbances which cause power quality problems is a difficult task which requires a high level of engineering expertise. Thus, neural networks are usually a good choice for the detection and classification of these disturbances. This paper describes a powerful tool, d...