This paper proposes a new algorithm based on the wavelet-packet transform for the analysis of harmonics in power systems. The proposed algorithm decomposes the voltage/current waveforms into the uniform frequency bands corresponding to the odd-harmonic components of the signal and uses a method to reduce the spectral leakage due to the imperfect frequency response of the used wavelet filter bank. This paper studies the selection of the mother wavelet, the sampling frequency, and the frequency characteristics of the wavelet filter bank for the two most common wavelet functions used for harmonic analysis and compares the performance of the proposed method with the results obtained using the discrete Fourier transform (DFT) analysis and the harmonic-group concept introduced by the International Electrotechnical Commission (IEC) under different measurement conditions.

Analysis of Harmonics in Power Systems Using the Wavelet-Packet Transform

The wavelet transform, the S-transform, the Gabor transform, and the Wigner distribution function are popular techniques for power quality (PQ) analysis in electrical power systems. They are mainly used to identify power harmonics and power disturbances and to estimate power quantities in the presence of nonstationary power components such as root-mean-square values and total harmonic distortions. Recently, the Hilbert-Huang transform has been also used in PQ analysis. These techniques have proven to be useful in PQ analysis; however, their performances depend on the types of PQ events. In this paper, a novel frequency-shifting wavelet decomposition via the Hilbert transform is introduced for PQ analysis. The proposed algorithm overcomes the spectra leakage problem in the discrete wavelet packet transform and can be used for estimating power quantities accurately and for detecting flickers. The effectiveness of the proposed algorithm was verified by computer simulations and experimental tests.

Hybrid Wavelet and Hilbert Transform With Frequency-Shifting Decomposition for Power Quality Analysis

Complex power quality disturbances classification via curvelet transform and deep learning

Power components, power factors, and pollution factor are defined according to the IEEE standard 1459-2000 based on the fast Fourier transform (FFT). However, the FFT in the presence of nonstationary power quality (PQ) disturbances results in inaccurate values due to its sensitivity to the spectral leakage problem. In this paper, a new perspective for the IEEE standard 1459-2000 definitions is introduced using the stationary wavelet transform (SWT). As a time-frequency transform, the SWT can provide variable frequency resolution while preserving time information without spectral leakage as the FFT. Moreover, unlike other time-frequency transforms, such as discrete wavelet transform (DWT), SWT possesses the time-invariance property that keeps the time and frequency characteristics throughout all of the decomposition levels. Results of different case studies including stationary, nonstationary of synthetic, and real PQ disturbances proves the effectiveness of applying the SWT over FFT or DWT.

A New Perspective for the IEEE Standard 1459-2000 Via Stationary Wavelet Transform in the Presence of Nonstationary Power Quality Disturbance

Due to an increase of power-electronic equipment installation and other harmonic sources, the identification and estimation of harmonic loads are of concern in electric power transmission and distribution systems. Conventional harmonic state estimation requires a redundant number of expensive harmonic measurements. In this paper, we explore the use of a statistical signal-processing technique, known as independent component analysis (ICA) for harmonic source identification and estimation. If the harmonic currents are statistically independent, ICA is able to estimate the currents using a limited number of harmonic voltage measurements and without any knowledge of the system admittances or topology. The results are presented with computer simulations for the modified IEEE 30-bus test system.

Harmonic Load Identification Using Complex Independent Component Analysis

A method is presented for eliminating the effect of imperfect frequency response of the filters in wavelet transform (WT) filter banks, and a WT-based algorithm for harmonic analysis of nonstationary power system waveforms. The method recovers the signal magnitude, which is distorted due to the imperfect response of filters. The WT-based algorithm is then developed. The algorithm is incorporated with the above antidistortion method to give accurate harmonic magnitudes. The power and practicality of the algorithm are illustrated through its application for the diagnosis of a motor-starting problem, which occurred in the Western Australia power system.

Antidistortion method for wavelet transform filter banks and nonstationary power system waveform harmonic analysis

This paper presents the application of a wavelet transform-based algorithm to the harmonic analysis of nonstationary power system waveforms. The waveforms are first decomposed into sub-bands of smaller frequency spectra using modified discrete wavelet packet transform filter banks. Harmonic trends are obtained from the decomposed outputs. A continuous wavelet transform is then applied to sections of the decomposed sub-bands to evaluate the harmonic frequencies, amplitudes and phases. The results of analysis of field-test capacitor bank switching waveforms show that the approach is excellent for harmonic analysis of nonstationary power system waveforms. It gives both the harmonic trends and the harmonic amplitude, phase and frequency values.

Analysing power system waveforms using wavelet-transform approach

A method of utilising non-source measurements for harmonic state estimation

This paper presents an approach for system-wide sub-harmonic estimation using state estimation (SE) algorithm together with the wavelet-transform (WT)-based algorithm. The WT-based algorithm is used to identify sub-harmonics in the measured power system waveforms. SE is then performed to estimate the sub-harmonic levels for the whole system based on a few selected measurements. Symbolic observability analysis (OA) method is applied to determine the system solvability for the SE algorithm from a set of measurements. Location and type of sub-harmonic sources are then determined from the estimated results. The approach is illustrated using a real power system and real field-test waveforms. The results show the accuracy of the method and its potential for analysis of noninteger harmonics.

Van Long Pham

Papers

Antidistortion method for wavelet transform filter banks and nonstationary power system waveform harmonic analysis

Analysing power system waveforms using wavelet-transform approach

A method of utilising non-source measurements for harmonic state estimation

Sub-harmonic state estimation in power systems

Application of Wavelet-Transform-Based Algorithm to Harmonic Analysis of Power System Waveforms