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.

This paper presents a method to estimate the harmonic groups in voltage and current waveforms using the wavelet-packet transform. The wavelet-packet decomposition algorithm is selected to obtain output bands compatible with the harmonic groups defined in the harmonics measurement standards for power-supply systems. The method proposed is analyzed in the measurement of harmonic components, either synchronized or nonsynchronized, with the fundamental power frequency, comparing the results with the method proposed in the international standards.

Wavelet transform (WT) is one of the most useful digital signal processing tools for time-frequency analysis of power quality disturbances in power systems. This paper presents an extensive bibliographic review of applications of WTs in the measurement and analysis of harmonic distortion in power systems, discussing the performance of the different methods proposed in the technical literature.

This paper presents an automatic system for real-time detection and analysis of voltage events in power systems. The voltage events considered are those that cause a temporary increase or decrease in the rms voltage magnitude over the limits recommended in the international standards. The system proposed uses three Kalman filters to detect when a voltage event begins and to estimate the three-phase voltage supply during the event. The results obtained from real measurements in a low-voltage distribution network show that the system accurately detects and analyzes in real time different types of voltage events in power systems

This paper presents a new method for online real-time detection and classification of voltage events in power systems. The method proposed simultaneously uses the results obtained in the application of the discrete wavelet transform and an extended Kalman filtering to the voltage waveform. Wavelet analysis is used for detection and estimation of the time-related parameters of an event and the extended Kalman filtering is used for confirmation of the event and for computation of the voltage magnitude during the event. The paper presents different experimental results showing the improved performance of this combined approach.