In this paper we present real-time application for detecting, extracting and automatic classification of transient disturbances in electric power network. Proposed method was implemented on Texas Instruments C6713 DSK toolkit and tested with both laboratory-generated and real-world power network signals with good results. For each detected transient a set of parameters is evaluated including th...

Power quality (PQ) is an issue that is becoming increasingly important to electricity consumers at all levels of usage. Improvement of PQ has a positive impact on sustain profitability of the distribution utility on the one hand and customer satisfaction on the other. To improve the power quality problems, the detection of PQ disturbances should be carried out first. The PQ problems vary in a w...

This document proposes multiple chaos synchronization (CS) systems for power quality (PQ) disturbances classification in a power system. Chen-Lee based CS systems use multiple detectors to track the dynamic errors between the normal signal and the disturbance signal, including power harmonics, voltage fluctuation phenomena, and voltage interruptions. Multiple detectors are used to monitor the d...

PQ analysis for detecting power line disturbances in real-time is one of the major challenging techniques that are being explored by many researchers. In order to understand the causes of electronic equipment being damaged due to disturbances in power line, there is a need for a robust and reliable algorithm that can work online for PQ analysis. PQ signal analysis carried out using transformati...

Power quality (PQ) analysis has become imperative for utilities as well as for consumers due to huge cost burden of poor power quality. Accurate recognition of PQ disturbances is still a challenging task, whereas methods for its indexing are not much investigated yet. This paper expounds a system, which includes generation of unique patterns called signatures of various PQ disturbances using co...

Power quality signal feature selection is an effective method to improve the accuracy and efficiency of power quality (PQ) disturbance classification. In this paper, an entropy-importance (EnI)-based random forest (RF) model for PQ feature selection and disturbance classification is proposed. Firstly, 35 kinds of signal features extracted from S-transform (ST) with random noise are used as the ...

Aiming at the combined power quality +disturbance recognition, an automated recognition method based on wavelet packet entropy (WPE) and modified incomplete S-transform (MIST) is proposed in this paper. By combining wavelet packet Tsallis singular entropy, energy entropy and MIST, a 13-dimension vector of different power quality (PQ) disturbances including single disturbances and combined distu...

Extensive use of power electronic devices and non-linear loads in electrical power system cause problem of power quality (PQ). Renewable energy sources are also integrated to the grid through power electronics based equipment. So the power quality issues are drawing attention in recent years. PQ disturbance need to be detected accurately It is also essential to find out the cause of such an eve...

: In this study, a method based on Stockwell transform (ST), ReliefF feature selection and Multilayer Perceptron Algorithm (MPA) algorithm was developed for classification of Power Quality (PQ) disturbance signals. the method, firstly, ST applied to different PQ signals obtain features. A total 30 features were obtained by taking entropy values matrix after The use all causes be complicated tra...