Algorithm for Simultaneous Parameter Estimation of A Multiharmonic Signal

Abstract Estimating the fundamental frequency and harmonic parameters is basic for signal modelling in a power supply system. Differing from the existing parameter estimation algorithms either in power quality monitoring or in harmonic compensation, the proposed algorithm enables a simultaneous estimation of the fundamental frequency, the amplitudes and phases of harmonic waves. A pure sinusoid is obtained from an input multiharmonic input signal by finite-impulse-response (FIR) comb filters. Proposed algorithm is based on the use of partial derivatives of the processed signal and the weighted estimation procedure to estimate the fundamental frequency, the amplitude and the phase of a multi-sinusoidal signal. The proposed algorithm can be applied in signal reconstruction, spectral estimation, system identification, as well as in other important signal processing problems. The simulation results verify the effectiveness of the proposed algorithm.

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NEW METHOD FOR ESTIMATION OF MULTI-HARMONIC POWER SIGNAL PARAMETERS

Journal on Processing and Energy in Agriculture ◽

10.5937/jpea25-35058 ◽

2021 ◽

Vol 25 (4) ◽

Author(s):

Predrag Petrovic

Keyword(s):

Fundamental Frequency ◽

High Performance ◽

Finite Impulse Response ◽

Signal Reconstruction ◽

Harmonic Signal ◽

Estimation Procedure ◽

Simultaneous Estimation ◽

Spectral Processing ◽

Unknown Parameters ◽

Comb Filters

A systematic analytical procedure for simultaneous estimation of the fundamental frequency, the amplitudes and phases of harmonic waves was proposed in this paper. In order to reduce complexity in the calculation of unknown parameters, a completely new reduced analytical expression is derived, which enabled fast and precise estimation with a small numerical error. Individual sinusoidal components stand out from the input complex-harmonic signal with the filter with a finite-impulse response (FIR) comb filters. The algorithm that is proposed in the operation is based on the application of partial derivate of the processed and filtered input signal, after which it is performed weighted estimation procedure to better estimate the values size of the fundamental frequency, amplitude and the multi-sinusoid signal phase. The proposed algorithm can be used in the signal reconstruction and estimation procedures, spectral processing, in procedures for the identification of the system that is observed, as well as other important signal processing areas. Through the simulation check, the effectiveness of the proposed algorithm was assessed, which confirmed its high performance.

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Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part I: Sensitivity Analysis and Parameter Identifiability

Monthly Weather Review ◽

10.1175/2007mwr2070.1 ◽

2008 ◽

Vol 136 (5) ◽

pp. 1630-1648 ◽

Cited By ~ 86

Author(s):

Mingjing Tong ◽

Ming Xue

Keyword(s):

Parameter Estimation ◽

Estimation Procedure ◽

Radar Data ◽

Simultaneous Estimation ◽

Square Root ◽

Wind Fields ◽

Fundamental Parameters ◽

Multiple Parameters ◽

Microphysical Parameters ◽

True Values

Abstract The possibility of estimating fundamental parameters common in single-moment ice microphysics schemes using radar observations is investigated for a model-simulated supercell storm by examining parameter sensitivity and identifiability. These parameters include the intercept parameters for rain, snow, and hail/graupel, and the bulk densities of snow and hail/graupel. These parameters are closely involved in the definition of drop/particle size distributions of microphysical species but often assume highly uncertain specified values. The sensitivity of model forecast within data assimilation cycles to the parameter values, and the issue of solution uniqueness of the estimation problem, are examined. The ensemble square root filter (EnSRF) is employed for model state estimation. Sensitivity experiments show that the errors in the microphysical parameters have a larger impact on model microphysical fields than on wind fields; radar reflectivity observations are therefore preferred over those of radial velocity for microphysical parameter estimation. The model response time to errors in individual parameters are also investigated. The results suggest that radar data should be used at about 5-min intervals for parameter estimation. The response functions calculated from ensemble mean forecasts for all five individual parameters show concave shapes, with unique minima occurring at or very close to the true values; therefore, true values of these parameters can be retrieved at least in those cases where only one parameter contains error. The identifiability of multiple parameters together is evaluated from their correlations with forecast reflectivity. Significant levels of correlation are found that can be interpreted physically. As the number of uncertain parameters increases, both the level and the area coverage of significant correlations decrease, implying increased difficulties with multiple-parameter estimation. The details of the estimation procedure and the results of a complete set of estimation experiments are presented in Part II of this paper.

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THE REALIZATION OF THE ITERATIVE METHOD OF THE LEAST SQUARES FOR THE ESTIMATION OF STATIC OBJECT PARAMETERS IN MATLAB ENVIRONMENT

VESTNIK OF ASTRAKHAN STATE TECHNICAL UNIVERSITY SERIES MANAGEMENT COMPUTER SCIENCE AND INFORMATICS ◽

10.24143/2072-9502-2017-1-28-36 ◽

2017 ◽

pp. 28-36

Author(s):

Galina Vasil’evna Troshina ◽

Alexander Aleksandrovich Voevoda

Keyword(s):

Parameter Estimation ◽

Iterative Method ◽

Least Squares ◽

Input Signal ◽

Layer Structure ◽

Estimation Procedure ◽

Parameter Estimation Procedure ◽

Noise Simulation ◽

Static Object ◽

Object Parameters

It was suggested to use the system model working in real time for an iterative method of the parameter estimation. It gives the chance to select a suitable input signal, and also to carry out the setup of the object parameters. The object modeling for a case when the system isn't affected by the measurement noises, and also for a case when an object is under the gaussian noise was executed in the MatLab environment. The superposition of two meanders with different periods and single amplitude is used as an input signal. The model represents the three-layer structure in the MatLab environment. On the most upper layer there are units corresponding to the simulation of an input signal, directly the object, the unit of the noise simulation and the unit for the parameter estimation. The second and the third layers correspond to the simulation of the iterative method of the least squares. The diagrams of the input and the output signals in the absence of noise and in the presence of noise are shown. The results of parameter estimation of a static object are given. According to the results of modeling, the algorithm works well even in the presence of significant measurement noise. To verify the correctness of the work of an algorithm the auxiliary computations have been performed and the diagrams of the gain behavior amount which is used in the parameter estimation procedure have been constructed. The entry conditions which are necessary for the work of an iterative method of the least squares are specified. The understanding of this algorithm functioning principles is a basis for its subsequent use for the parameter estimation of the multi-channel dynamic objects.

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