Wavelet-based technique for identification of harmonic source in distribution system

Abstract

SUMMARYA new method to identify the location and nature of the harmonic generating sources in a distribution system has been proposed. The method is based on wavelet decomposition of voltage and current signals at the point of measurement. Detail reactive power at level 1 of wavelet decomposition has been used. A harmonic generating load is identified by extracting its characterizing harmonics in the power system signals. The pseudo-frequency for the wavelet decomposition at level 1 is set at the characteristic frequency of the load, and the sampling frequency is so chosen that the desired harmonic information can be extracted at the detail level 1. Power systems signals are, however, captured at a higher sampling frequency. For the identification of the disturbing source, the required sampling frequency for level 1 decomposition is obtained using downsampling on the captured data. Applicability of the method has been demonstrated for both radial and non-radial power system networks.

Review of Signal Processing Techniques and Machine Learning Algorithms for Power Quality Analysis

“…In ref. [50], WT based technique is developed which is event specific and can identify and classify harmonics and transients. The application specific voltage dips are considered by authors to avoid false alarms in electrical networks.…”

A novel discrete wavelet transform‐based graphical language classifier for identification of high‐impedance fault in distribution power system

“…In this work, the feature extraction has been carried out using Fast Fourier Transform (FFT) to classify the fault . Similar approach, to identify and classify the type of fault based on either signal processing techniques namely Fourier Transform, Prony Analysis, S‐Transform, wavelet transform (WT) and Phase Reconstruction or with combination of intelligence method such as support vector machine (SVM), Particle Swarm Optimization (PSO)‐based Artificial Neural Network (ANN), adaptive neuro fuzzy interference system (ANFIS), fuzzy logic system and Fault index method is presented in . The extraction of feature for all these methods to locate the fault is obtained by sampling either the fault current or voltage waveform.…”

Harmonic separation from grid voltage using ensemble empirical-mode decomposition and independent component analysis

“…In extreme conditions, only a single channel recording needs to be separated. Some researchers have separated the harmonic and subharmonic signals from single‐channel grid voltage signals by constructing virtual channels of grid voltage based on ICA called single‐channel ICA . This method requires prior knowledge of the harmonics and subharmonics of the voltage to construct the virtual channel signals.…”

“…Some researchers 9,10 have separated the harmonic and subharmonic signals from single-channel grid voltage signals by constructing virtual channels of grid voltage based on ICA called single-channel ICA. [11][12][13] This method requires prior knowledge of the harmonics and subharmonics of the voltage to construct the virtual channel signals. Moreover, if the number of frequencies of harmonics is N, the number of virtual channel signals needs to be 2 × N. Meng 14,15 proposed eigenvalue decomposition (EVD)-FastICA and method based on improved FastICA-least squares measurement (LSM) to separate harmonics in grid voltage.…”