Robust Frequency Estimation Research Articles

DC-AC Converter with Dynamic Voltage Restoring Ability Based on Self-Regulated Phase Estimator-DQ Algorithm: Practical Modeling and Performance Evaluation

A self-regulated phase estimator (SRPE)-based DQ algorithm for a DC-AC converter with dynamic voltage restoring (DVR) ability is presented in this paper. When compared to the conventional phase-locked loop (PLL), the provided controller can significantly reduce phase distortions and low-order harmonics from the load voltage while attaining quicker dynamic response. Furthermore, the fundamental attribute of the load voltage allows the integrated DC-AC converter to operate at a consistent frequency eliminating frequency oscillations. The SRPE is utilized primarily in the DQ control theory as the reference voltage generator which can compensate for the grid voltage. SRPE has good band-pass filtering properties and a mathematically simple structure that can thoroughly attenuate voltage imbalance and has quick dynamic response. The SRPE has been made to be frequency-adaptive using a damping factor and robust grid frequency estimation. The SRPE can maintain the fundamental frequency at 50 Hz and keep the total harmonic distortions (THD) within the 5% limit even during grid disruptions. The DC-AC converter and SRPE-DQ’s stability are thoroughly examined. The experiment is carried out to show the efficacy of the suggested complete control system. There are also comparative simulation studies to show the benefits of the suggested technique. The results reveal that the suggested approach can immediately identify and correct for any grid voltage imbalance while also assisting in maintaining the constant voltage at the load side despite voltage sag/swell and distortions.

Robust Frequency Estimation Under Additive Symmetric α-Stable Gaussian Mixture Noise

Here the estimating problem of a single sinusoidal signal in the additive symmetric α-stable Gaussian (ASαSG) noise is investigated. The ASαSG noise here is expressed as the additive of a Gaussian noise and a symmetric α-stable distributed variable. As the probability density function (PDF) of the ASαSG is complicated, traditional estimators cannot provide optimum estimates. Based on the Metropolis-Hastings (M-H) sampling scheme, a robust frequency estimator is proposed for ASαSG noise. Moreover, to accelerate the convergence rate of the developed algorithm, a new criterion of reconstructing the proposal covariance is derived, whose main idea is updating the proposal variance using several previous samples drawn in each iteration. The approximation PDF of the ASαSG noise, which is referred to the weighted sum of a Voigt function and a Gaussian PDF, is also employed to reduce the computational complexity. The computer simulations show that the performance of our method is better than the maximum likelihood and the -norm estimators.

Missing samples reconstruction using an efficient and robust instantaneous frequency estimation algorithm

In order to recover missing samples in a nonstationary signal, this paper employs a time-signal analysis and filtering method. The instantaneous frequency of a multicomponent signal is first estimated by employing a robust and computationally efficient method. Then the time-frequency filtering is performed using a dechirping operation to recover missing samples. These steps are repeated until convergence. The proposed method achieves better performance than the state of art methods both in terms of the accuracy of the recovered signal and computational efficiency.

Robust Frequency Estimation Under Additive Mixture Noise

In many applications such as multiuser radar communications and astrophysical imaging processing, the encountered noise is usually described by the finite sum of -stable variables. In this paper, a new parameter estimator is developed, in the presence of this new heavy-tailed noise. Since the closed-form PDF of the -stable variable does not exist except and , we take the sum of the Cauchy () and Gaussian () noise as an example, namely, additive Cauchy-Gaussian (ACG) noise. The probability density function (PDF) of the mixed random variable, can be calculated by the convolution of the Cauchy's PDF and Gaussian's PDF. Because of the complicated integral in the PDF expression of the ACG noise, traditional estimators, e.g., maximum likelihood, are analytically not tractable. To obtain the optimal estimates, a new robust frequency estimator is devised by employing the Metropolis-Hastings (M-H) algorithm. Meanwhile, to guarantee the fast convergence of the M-H chain, a new proposal covariance criterion is also devised, where the batch of previous samples are utilized to iteratively update the proposal covariance in each sampling process. Computer simulations are carried out to indicate the superiority of the developed scheme, when compared with several conventional estimators and the Cramér-Rao lower bound.

Exact Frequency Estimation in the i.i.d. Noise via KL Divergence of Accumulated Power

To solve the frequency estimation deterioration caused by independent and identically distributed (i.i.d.) noise, this letter proposes an exact and robust frequency estimation algorithm based on the accumulated phase-difference power on Kullback-Leibler divergence (APP-KLD). First, the frequency estimation problem is modeled by the accumulated power of phase-difference between the receiving signal and Fourier basis function. Furthermore, the true frequency is estimated iteratively by maximizing the Kullback–Leibler divergence, which measures the distance between the accumulated power and a constant reference vector. Simulations demonstrate the robust performance of the APP-KLD algorithm at various levels of heavy-tailed noise and sample numbers.

Joint spectrum sensing and DOA estimation with sub-Nyquist sampling

This paper addresses the problem of joint spectrum sensing and Direction-of-Arrival (DOA) estimation under array receiver based on modulated wideband converter (MWC) with sub-Nyquist sampling. To simplify the structure of MWC, we connect one identical branch of MWC on each antenna of array receiver, reducing the overhead of hardware resources. Two joint spectrum sensing and DOA estimation (JSS-DOA) algorithms, referred to as CP- and MUSIC-based algorithms, are proposed to improve the robustness of estimation. The proposed approaches utilize Fourier series coefficients of a periodic signal provided by MWC to determine the subband positions where sources are located, and combine the recovered subband spectrum information to obtain estimates of frequencies. This strategy ensures robust frequency estimation accuracy under certain conditions and contributes to a robust DOA estimation under high noise level. We further provide a theoretical analysis of the working conditions for proposed algorithms, based on which the rationale of parameter setting is also reported. Finally, the performance of proposed algorithms is assessed via simulation experiments concerning multiple unknown sources under our receiver with sub-Nyquist sampling.

Robust three-Phase distribution system frequency measurement using a variable step-size LMS

Frequency estimation is an important power quality parameter that is also useful for the protection and control of distributed generators. This paper proposes a robust three-phase distribution system frequency estimation algorithm based on the widely linear complex least mean square using a variable step-size to provide accurate frequency estimation and fast convergence during islanding of distributed generation (DG) and faults. The performance of the proposed method was evaluated in islanding events of DG. Besides, unbalanced analytical signals in amplitude and phase were evaluated as well as the impacts of the sampling rate and noise. The proposed algorithm was compared to other least mean square-based methods and good results were achieved.

Enhanced Third-Order Generalized Integrator-Based Grid Synchronization Technique for DC-Offset Rejection and Precise Frequency Estimation

An enhanced third-order generalized integrator (TOGI) method has been proposed for the estimation of grid frequency and the synchronizing signals. The proposed method precisely estimates grid frequency and synchronizing signals in the presence of high harmonic content, phase change, frequency change, noise, and DC-offset in the grid voltage. A robust frequency estimator based on three consecutive sampling method has been integrated with TOGI algorithm for accurate frequency estimation. The proposed technique gives zero steady-state error in case of frequency estimation under different grid voltage conditions. This paper demonstrates the developed enhanced TOGI technique for synchronization in a single-phase system. The efficacy of the proposed algorithm has been tested and analyzed in MATLAB/Simulink and verified with experimental results.

Multi-consecutive-samples based frequency estimation for single-phase systems under odd-, even-order harmonics and DC offsets

This paper proposes a multi-consecutive-samples (MCSs) based method to estimate the grid frequency with strong robustness against odd-, even-order harmonics and DC offsets. Analyzing the relationship among the distorted grid voltage and its multiple consecutive sample signals, a MCS-model is established. Accordingly, the robust frequency estimation is proposed based on the MCS-model. Then, the stability analysis and parameter tuning are provided. Compared with the traditional 3CS-based frequency estimation, the proposed method enhances the disturbance suppression capability, especially in DC offsets and even-order harmonics cases. The experimental results further confirm that the proposed method has strong robustness against odd-, even-order harmonics and DC offsets.

Missing samples reconstruction using an efficient and robust instantaneous frequency estimation algorithm

Missing samples reconstruction using an efficient and robust instantaneous frequency estimation algorithm

A robust and efficient instantaneous frequency estimator of multi-component signals with intersecting time-frequency signatures

An algorithm for multi-component instantaneous frequency (IF) estimation of signals with intersecting signatures in the time-frequency domain is proposed. The proposed algorithm exploits the slow variation in the IF and the direction of the IF curve to accurately track the IF without computing the complete time-frequency representation. The performance of the algorithm is illustrated using both synthetic and real-life signals. The proposed algorithm outperforms the state of art, such as Viterbi and adaptive directional time-frequency distribution based methods, in terms of the accuracy of IF estimates and computational complexity.

Widely linear least mean kurtosis‐based frequency estimation of three‐phase power system

We propose a widely linear (augmented) least mean kurtosis (WL-LMK) algorithm for robust frequency estimation of three-phase power system. The negated kurtosis-based algorithms are most celebrated for their computational efficiency and strong robustness against wide range of noise signals which can overcome the inherent performance degradation faced by the well-known minimum mean square error-based algorithms in noisy environments. The proposed widely linear LMK estimation technique utilises all second-order statistical information in the complex domain C for processing of non-circular complex-valued signals. The three-phase power system signal, modelled through Clarke's αβ transformation, is circular for balanced and non-circular for unbalanced systems, based on which, the proposed WL-LMK algorithm is able to achieve improved frequency estimation under unbalanced and other abnormal system conditions. Its estimation performance is evaluated for several cases that encounter in the day-to-day operation of power system. It is observed from simulation studies of synthetic and real-world power system data that the proposed WL-LMK algorithm exhibits superior estimation performance as compared to the standard linear complex LMK (CLMK) and the widely linear least mean square (WL-LMS) algorithms.

Multiple Interacting Narrowband Interferences Suppression Algorithm for OFDM Systems

The OFDM technology is widely used in wireless communication systems, but it is highly sensitive to narrowband interference (NBI). The existing NBI suppression algorithms are mainly for single NBI or multiple NBIs with an assumption that the frequency distance of the multiple NBIs is large enough. However, in reality, there are more and more multiple interacting NBIs with small frequency distance, such as NB-IoT. Unfortunately, due to the interaction of multiple NBIs, the performance of existing NBI suppression algorithms is degraded. Aiming at this problem, we propose a multiple interacting NBIs suppression algorithm jointed time-frequency domain for OFDM systems. First, in order to achieve a high-precision and low-complexity frequency estimate for a single NBI, an interference frequency estimation method based on chirp-z transform (CZT-IFE) is studied. Then, for multiple interacting NBIs, based on the analysis of NBI frequency distance and spectral aliasing, a highly robust interference frequency estimation method combining IIR notch filter and CZT-IFE (NF-CZT-IFE) is given. It eliminates the interaction of multiple NBIs and obtains the accurate frequency estimates of the NBIs. Finally, based on NF-CZT-IFE, the multiple interacting NBIs are suppressed by using IIR notch filters. Simulation results demonstrate that the proposed multiple interacting NBIs suppression algorithm has better interference suppression performance on the NBIs with different frequency distances and different interference numbers. The proposed algorithm improves the robustness of OFDM systems in complex NBI environments.

Separable property-based super-resolution of lousy image data

This paper presents a novel wavelet-based approach for single-image super-resolution. Our technique integrates wavelet transform and the learned locally regularized anchored neighborhood regression model for more robust frequency estimation and image restoration. First, we decomposed the low-resolution input image into four frequency sub-bands by applying discrete wavelet transform and then processed these frequency sub-bands based on separable property of neighborhood filtering to achieve a fast parallel and vectorized operation by reducing computational burden resulting from computing the weighted function of every pixel for each pixel in an image. We then applied inverse discrete wavelet transform to reconstruct the original image. Super-resolution is achieved using the learned model to predict the high-resolution image features. Lastly, we explicitly unified both the locality structure and nonlocal self-similarity properties in natural image and incorporated them into our super-resolution framework to regularize the nonlinear correlation between low-resolution and high-resolution space and improve the reconstructed results. Experiments on standard images validate the effectiveness of our proposed method for effective denoising, deblurring and super-resolution reconstruction tasks compared to other top performing state-of-the-art methods both quantitatively and qualitatively.

Robust Frequency, Phase, and Amplitude Estimation in Power Systems Considering Harmonics

Efficient and accurate estimation of voltages and currents is essential for the control of electrical grids, particularly microgrids. In this paper, we present a novel two-step framework to estimate these parameters for both single-phase and three-phase power systems including harmonics and interharmonics. After employing a harmonic version of the Aboutanios and Mulgrew (HAM) estimator, the fundamental frequency and phase estimates are further refined by a weighted least squares (WLS) estimator. The WLS refinement step takes advantage of the harmonic and interharmonic relationships to further improve the parameter estimation from the HAM step. Consequently, the proposed HAM-WLS method maintains accuracy even in electrically-noisy environments that are also contaminated with both harmonics and interharmonics. Results from both simulated and real data under various power system conditions verify that the HAM-WLS estimator outperforms state-of-the-art estimation methods in terms of accuracy, complexity and convergence.

A Robust Frequency Estimation Method for Aircraft Grids Under Distorted Conditions

In aircraft grids, the voltage frequency is much higher than that in utility power systems and it challenges the dynamics of frequency estimations. In this letter, a fast and robust frequency estimation method is proposed for aircraft grids even when the grid voltage is distorted. In detail, a mathematical model is established based on the relationship among the fundamental grid voltage, harmonics and their time-delay signals. Accordingly, a robust parameter identification method is proposed to achieve grid frequency estimation and harmonic immunity. Compared with the frequency estimations for utility power systems, the proposed method, so-called robust frequency estimation (RFE), has faster dynamics and stronger harmonic suppression capability. Hence, it takes advantages in the applications of aircraft grids. Experimental results also indicate that the proposed RFE has good performance in terms of fast dynamics and strong robustness.

A Robust Online Identification of Sustained Low Frequency Oscillation in Steady-State Power Systems

For sustained low frequency oscillations in steady-state power systems, an algorithm is proposed for precise online identification of oscillation frequency, oscillation amplitude, and fundamental amplitude. The algorithm consists of a robust low frequency estimator and a notch filter in parallel. The asymptotical convergence property is analyzed by slow integral manifold, averaging method, and Lyapunov stability theorem sequentially. The steady-state antinoise property is investigated by perturbed system theorem. The robust advantages of the proposed algorithm are embodied in the following aspects: the fundamental amplitude identification is little influenced by oscillation frequency and oscillation amplitude, both oscillation frequency identification and oscillation amplitude identification have small steady-state errors under high order harmonics or bounded noises, the ramp variations of both fundamental amplitude and oscillation amplitude are also significantly tracked, and three design parameters have different effects on identification performance of oscillation frequency, oscillation amplitude, and fundamental amplitude, respectively. Simulation results verify the validity.

A Robust Adaptive Cancellation of Unknown Sinusoidal Disturbance

A robust adaptive controller that consists of a well-known PI controller and a robust frequency estimation in parallel was presented to cancel unknown sinusoidal disturbance in a stable linear time invariant system. The convergence of frequency estimation, the accurate cancellation of disturbance and the asymptotic stability of the overall feedback control system are analyzed by slow integral manifold and averaging method. The key advantages include asymptotically convergent cancellation for disturbance with much low frequency, the asymptotic stability of the whole error feedback system independent to the parameters of the plant and robust applicability in engineering. Simulation results validate the transient convergence performance and the steady-state anti-noise performance as well as the action of parameters of the proposed algorithm.

Robust frequency estimation of unbalanced power system using a phase angle error based least mean kurtosis algorithm

In this paper, an augmented (widely linear) complex Least Mean Kurtosis (WL-LMK) algorithm that uses the negated kurtosis of the phase angle error instead of the conventional magnitude error is proposed for robust frequency estimation of three-phase power systems. The motivation of the proposed method is based on two underlying facts. First, it is primarily the phase rather than the amplitude that conveys useful information in frequency estimation. Second, the negated kurtosis based cost function is computationally efficient and most celebrated in applications where the noise contamination degrades the performance of the classical adaptive filtering algorithms. To further achieve robust frequency estimation for both balanced and unbalanced conditions, the proposed method makes use of the full second-order information within the complex-valued system voltage through the Clarke’s αβ transformation and the concept of augmented complex statistics and widely linear modelling. The estimation performance of the proposed method is evaluated for several critical cases that often arise in power system. Simulation results of both synthetic cases and experimental studies show that the proposed method achieves more accurate frequency estimates than its developed widely linear counterparts in the literature.

Magnitude‐cum‐phase angle error‐based WL adaptation for frequency estimation of three‐phase power system

Motivated by the need for robust frequency estimation for both balanced and unbalanced power system conditions and the fact that most of the existing techniques perform poorly in the presence of external interferences, a more noise-resilient widely linear least mean kurtosis (WL-LMK) algorithm is proposed that uses both the magnitude and phase angle errors for online estimation of three-phase power system frequency. The proposed method makes use of the full second-order information within the complex-valued system voltage through Clarke's α β transformation and the concept of augmented complex statistics and WL modelling. The effectiveness of the proposed scheme is verified through simulation studies of synthetic signals and experimental data.