A Filter Bank and a Self-Tuning Adaptive Filter for the Harmonic and Interharmonic Estimation in Power Signals

Online Detection of Fundamental and Interharmonics in AC Mains for Parallel Operation of Multiple Grid-Connected Power Converters

“…Due to the complexity, high-resolution methods are suitable only for off-line analysis and benchmark. Another significant category of interharmonic detection methods involves the adaptive signal processing techniques based on a filter bank with adaptive parameter estimation in the time domain rather than the frequency domain [29]. More specifically, by linking n phase-locked loop (PLL) units within one "external" loop, the architecture devised in [20] succeeds in extracting the harmonics and inter-harmonics from a multi-sinusoidal measurement.…”

Event detection method for the PMUs synchrophasor data

“…Recently, various signal processing algorithms have been introduced for analyzing electric power data. This includes the system modeling algorithm based on all pole, all zero and autoregressive moving average (ARMA) [3], [4], [5], the singular point method based on residual modeling algorithm [4], [6], [7] and the frequency analysis method based on Fourier transform or filter bank [8], [9], [10], [11]. The system modeling algorithms are simple and powerful for analyzing electric power data, but they require the inverse matrix operation for every iteration.…”

“…STFT analyzes the dynamic nature of time-varying signals and thus the performance of these techniques depends on the size of the window. Wavelet and filter banks are introduced to solve the weakness of STFT [11], but require a sufficient sampling rate [8].…”

Exact model order ESPRIT technique for frequency and power estimation

“…In power quality (PQ) disturbance analysis, an accurate description of the variation in the power supply necessitates the use of signal processing. Signal processing techniques for PQ variations and PQ events can be classified as either stationary or nonstationary, depending on the signal statistics [2]. In both of these techniques, parametric and nonparametric methods can be used to estimate the power and frequency of the fundamental, harmonic components of power signals.…”

“…Some of the recent literature on parametric methods application to the power system includes: [3]- [4] for transient analysis, [5] for fault location estimation, [6] for islanding detection, [7] and [8] for analysis of low frequency oscillations, [9] for harmonics estimation, etc. However, these parametric methods requires prespecified information about the frequency of the power signal, and these algorithm measured signals captured from monitoring devices are invariably contaminated by some amount of noise, which affect the accuracy of parameters estimation [1] [2].…”