Sarita Nanda

A fast hybrid adaptive filter for parameter estimation of non stationary sinusoid under noise

This paper presents a modified combined approach using Taylor series expansion and Extended Kalman filter for accurate estimation of parameters and harmonic components of a time-varying signal embedded in noise with low signal-to-noise ratio. The signal is modelled using a dynamic model with time varying parameters. The power signal with a changing envelope has been expressed using a second order Taylor expansion. EKF algorithm is used for computing the parameters of such signal model. The algorithm using a linear model approach is presented which also reduces the computational complexity. Moreover this approach is also immune to noise, harmonic contaminations and also shows better convergence properties.

A Gauss–Newton ADALINE for Dynamic Phasor Estimation of Power Signals and Its FPGA Implementation

This paper presents a new hybrid adaptive filter based on modified Gauss–Newton adaptive linear element (MGNA) for estimating the fundamental and harmonic phasors along with the frequency change of nonstationary power system signals useful in many application areas that include system control, digital relaying, state estimation, and also wide area systems. The proposed approach is used to minimize an objective function based on weighted square of the error using the MGNA. Moreover, the inverse of the Hessian matrix is computed assuming certain approximations to reduce the computational load and time consumption. Furthermore, it also uses recursive formulation using the estimated values from the previous time instant unlike the nonrecursive approaches, thereby exhibiting better performance in terms of accuracy and convergence. Besides, its simple structure makes it more suitable for real-time applications. In addition, the filter has been implemented on a field programmable gate array hardware and Xilinx 14.2 with the Sysgen software for the estimation of frequency, fundamental, and harmonic phasors of single and three-phase time-varying power system signals.

An adaptive filtering technique with self-adaptive PSO for estimation of non-stationary signals

A hybrid filter using TaylorK Kalman modelling with self-adaptive PSO tuning is presented in this paper for accurately estimating the parameters and harmonic components of a time varying sinusoidal signal in a noisy environment. Second or higher order Taylor expansion modelling of the changing envelope of the power signal and Kalman Filter algorithm for estimating the parameters of the signal model leads us to the design of robust filters referred to as TaylorK Kalman Filters. The proposed algorithm is linear, has less computational complexity and is easier to be implemented on a hardware platform. The use of self-adaptive PSO tune the filter parameters makes the proposed algorithm sensitive to any change in the signal dynamics, immune to noise and harmonic contaminations.

A new Taylor-LMS adaptive filter for parameter estimation of power signals including distributed generation systems

Abstract Frequency and phasor measurement of time-varying signals has assumed great importance for phasor measurement units for wide area power networks in micro and smart grid environments. It is well known that the power signals undergo dynamic transitions during power quality and fault type disturbances. Also accurate estimation of amplitude, phase and frequency of a sinusoid in the presence of harmonics/inter harmonics and noise plays an important role in wide variety of power system applications, like protection, control and state monitoring. One of the widely used approach like the Fourier linear combiners becomes ineffective to track the sudden changes in the power signal amplitude, phase, and frequency in the presence noise and distortions. This paper, therefore, presents a new adaptive filter for estimating the frequency and phasors of sinusoids mixed with decaying dc and harmonic components undergoing sudden transitions. The filter is based on a quadratic polynomial signal model whose parameters are estimated using a variable step continuous p-norm LMS algorithm. Extensive computer simulations have been carried out with synthetic signals to demonstrate the viability of this new approach. Further signals obtained from a distributed generation system in MATLAB/SIMULINK environment are used for frequency, fundamental and harmonic phasor estimation.

A two stage hybrid algorithm for estimation of non-stationary sinusoidal signal parameter

In this paper we consider the problem of estimation of amplitude, phase and frequency of a time-varying sinusoidal signal corrupted by noise. The frequency is estimated using Linear Predictor Approach and amplitude, phase is estimated using ADALINE approach. It is a two stage algorithm, estimated frequency of first stage is used to estimate amplitude and phase in second stage. Moreover, the learning parameter is tuned iteratively for faster convergence. From the proposed method a numerically robust and low complexity algorithm is derived for tracking the parameters of real sinusoid. Simulation results illustrate the good performance of the proposed method.

Improved ADALINE Based Algorithm for Power System Frequency Estimation

This paper intends to present an adaptive algorithm for estimating the frequency, amplitude, and phase of a sinusoid under non stationary condition present in time-varying power signals. The proposed algorithm estimates precisely the frequency variation, phase variation, and the amplitude and shows accuracy in estimation even in the presence of harmonic and inter harmonic as noise. This method uses Taylor series expansion of the signal to cope with the sudden changes. Then a modified ADALINE is used because of its low computational complexity, for which it can be implemented in real time. The performance of the proposed algorithm has been extensively tested and demonstrated.

An integrated approach for identification of exon locations using recursive Gauss Newton tuned adaptive Kaiser window

Identification of exon location in a DNA sequence has been considered as the most demanding and challenging research topic in the field of Bioinformatics. This work proposes a robust approach combining the Trigonometric mapping with Adaptive tuned Kaiser Windowing approach for locating the protein coding regions (EXONS) in a genetic sequence. For better convergence as well as improved accurateness, the side lobe height control parameter (β) of Kaiser Window in the proposed algorithm is made adaptive to track the changing dynamics of the genetic sequence. This yields better tracking potential of the anticipated Adaptive Kaiser algorithm as it uses the recursive Gauss Newton tuning which in turn utilizes the covariance of the error signal to tune the β factor which has been shown through numerous simulation results under a variety of practical test conditions. A detailed comparative analysis with the existing mapping schemes, windowing techniques, and other signal processing methods like SVD, AN, DFT, STDFT, WT, and ST has also been included in the paper to focus on the strength and efficiency of the proposed approach. Moreover, some critical performance parameters have been computed using the proposed approach to investigate the effectiveness and robustness of the algorithm. In addition to this, the proposed approach has also been successfully applied on a number of benchmark gene sets like Musmusculus, Homosapiens, and C. elegans, etc., where the proposed approach revealed efficient prediction of exon location in contrast to the other existing mapping methods.

An adaptive signal parameter estimation technique using quadratic polynomial signal model and Least square techniques

This paper proposes a new modified approach using Least square algorithm associated with a ridge regression factor and Taylor expansion for estimation of fundamental frequency and phasor of the fundamental frequency component with time varying envelope. The signal considered comprises of decaying dc component and fundamental frequency component which are expressed in terms of Taylor coefficients upto second order. The modified Least square technique is then used to estimate the Taylor coefficients using which the time varying amplitude, phase, fundamental frequency and dc component can be accurately evaluated. This approach shows excellent tracking capability of the signal parameters of a time variant sinusoidal signal even under extreme noise conditions with low SNR value.

A fast adaptive notch smoother for parameter estimation of complex sinusoid under noise

This paper presents an adaptive notch smoother for detection and extraction of a time varying complex sinusoidal signal buried in noise. Adaptive notch smoother is found to have better signal estimation accuracy than that obtained from adaptive notch filters (ANF). The ANS algorithm provides estimated frequency, amplitude and phase of the signal. But the basic problem of ANS is, for the estimation of smoothed amplitude parameter, the algorithm needs the value of smoothed frequency component. Hence the estimation of amplitude component is not simultaneous. To overcome this problem the proposed method combines adaptive Gauss-Newton algorithm with the ANS algorithm, for the simultaneous estimation of all the signal parameters.

Multi-harmonic frequency identification through multistage adaptive notch filter and least squares

The identification and accurate measurement of multi-harmonic frequency components is often required for characterization of various signals and systems. This paper proposes a new method for Multi-harmonic frequency identification based on adaptive notch filtering and least squares fitting. The adaptive notch filtering algorithm is employed for estimation of coarse frequency components. From the initial estimates of the frequency components a least square fitting method is applied to accurately identify the amplitude, phase and frequency estimates.