For interpolated discrete Fourier transform (IpDFT)-based phasor estimators, the out-of-band interference (OOBI) test is among the most challenging ones. The typical iterative-interpolated DFT (i-IpDFT) phasor estimator utilizes a two-step iterative framework to eliminate the effects of the negative frequency and OOBI. However, the speed of estimation is limited by the adopted frequency estimator and the redundant iterations. To this end, this article proposes a fast i-IpDFT (FiIpDFT) method for the phasor estimation of an OOBI contaminated signal, which utilizes the three-point IpDFT (I3pDFT) technique. The proposed method first applies a noniterative frequency, amplitude, and phase estimator to eliminate the negative frequency interference. Then, a straightforward formula and two-stop criterion are introduced to reduce the computational burden of the OOBI elimination process. The accuracy and effectiveness of the proposed FiIpDFT method are validated by simulations. These are designed, under steady and dynamic conditions, according to the requirements of the Standard IEC/IEEE 60255-118-1.

The interpolated discrete Fourier transform (IpDFT) is one of the most popular techniques to estimate the parameters of a damped real-valued sinusoidal signal (DRSS). However, its accuracy is affected by strong noise presence and short observation windows. To this end, this letter proposes a novel two-point IpDFT method, called I2pZDFT, for the parameter estimation of a DRSS. The proposed I2pZDFT uses the zero-padding technique to increase the sampling rate in the frequency domain. The conjugate symmetry and the parity of the zero-padded signal are utilized to eliminate the influence of the spectral leakage. Simulation results highlight that the proposed I2pZDFT outperforms the existing IpDFT-based methods in terms of noise immunity, especially in the case of observation windows as short as 0.5 ~ 1 cycles.

Out-of-band interference (OOBI) test is a bench-mark test for M-class phasor measurement units (PMUs) in IEEE/IEC 60255-118-1-2018 and C37.118.1a-2014. Because of the spectral interference produced by interharmonic, it is difficult for dynamic phasor estimators to satisfy the OOBI test requirements with a short observation time. To this end, this article proposed a Taylor weighted least squares (TWLS) method based on the matrix pencil for the dynamic phasor estimation of the OOBI contaminated signal. The proposed method first applies the matrix pencil algorithm to obtain rough estimations for the frequencies of the fundamental component and other components (i.e., harmonics, subharmonics, and interharmonics) within a few cycles. Then, the TWLS method was adopted to acquire more accurate estimations of the dynamic phasor and frequencies. The effectiveness of the proposed method is validated by simulations under steady and dynamic conditions that follow the requirements of the IEEE standard.

Tracking the frequency of power systems accurately and promptly is the cornerstone of power industry applications. The excellence of windowed interpolation discrete Fourier transform (DFT)–based methods has been proved in frequency estimation by suppressing both short- and long-range spectral leakage. However, due to the nonnegligible image component, their behavior deteriorates dramatically when short time windows are adopted. To this purpose, a novel frequency estimator based on two-point DFT and Hanning window is proposed for estimating distorted power signals. The method starts with an accurate spectral model that fully considers the effects of the image component. An iterative procedure is then designed to approach the minimum estimation error by using the golden section search. Simulation and experimental results demonstrate that the method offers fast and accurate estimation within a short time window (even as short as 0.5–1.5 cycles).

Phasor measurement units (PMUs) are the cornerstone of intelligent sensing and monitoring of power systems. The superiority of the Sinc least squares (SLS) method, a signal processing algorithm for PMUs, has been proven in harmonic phasor estimation. However, the SLS-based estimators suffer from spectral interferences and heavy computational burden. To this end, a real-valued sinc weighted least squares method (SWLS-R) is proposed to track the dynamic synchrophasor accurately and quickly. First, the weighted vector, i.e., window function, is introduced to enhance the SLS estimator’s behavior by mitigating the impact of spectral interference. Then, the complex-valued system matrix is transformed into the real-valued system matrix to lower computational burden by avoiding complex matrix multiplication. Finally, an explicit and fast solution is designed to calculate the pseudoinverse matrix of the SWLS-R, which aims to further improve computational efficiency. The accuracy and effectiveness of the proposed method are validated by simulations. These are designed, under steady and dynamic conditions, according to the M-class requirements of the Standard IEC/IEEE 60255-118-1.

Frequency estimation plays a crucial role in monitoring and protecting smart grids. The excellence of DFT-based frequency estimators has been well-known due to their efficiency and robustness. However, multi-component interference has become one of the biggest challenges of DFT-based estimators. To this end, we propose a novel DFT-based method for estimating multi-frequency real-valued sinusoidal signals, which consist of P unknown components. The proposed method first constructs a nonlinear equation to describe the relationship between the parameters of all components and their frequency spectrum. The nonlinear equation is then transformed into a linear equation with 3 P unknowns for estimating the frequency. The proposed method takes the side-lobe interference and spectrum leakage of all components into consideration. As a result, the impacts of multi-component interference can be completely avoided. Experiments and simulations demonstrate that the proposed method not only meets the limitations of Standard IEC/IEEE 60255-118-1, but is also highly competitive with existing frequency estimators.