Least-squares Lattice Algorithm Research Articles

A Harmonic Detection Method of UPQC Based on LSL Algorithm in Photovoltaic Microgrids

Photovoltaic microgrids have discontinuity and intermittency issues that affect the load current amplitude and frequency, and this leads to disturbances. The unified power quality conditioner (UPQC) combines a series active power filter (SAPF) and a parallel active power filter (PAPF), and therefore, it has the advantages of SAPF and PAPF to treat power quality problems. Considering detection precision and response speed, and to meet the fast speed and accuracy of PAPF harmonic detection, the algorithm principle of adaptation and least-squares lattice (LSL) is analyzed. A new harmonic detection method of three-phase circuit based on LSL adaptive algorithm is proposed. The two other harmonic detection methods between least mean square and instantaneous power theory based are compared using the Simulink simulation platform. The compensation effect of LSL is more stable and better than the two other methods. The experimental results also indicate that the new method based on LSL can accurately detect and compensate the harmonic component in photovoltaic microgrids in real time. Therefore, this harmonic detection method based on LSL can be used in the practical engineering applications, and it provides a new method for harmonic detection of UPQC PAPF.

Recursive Least-Squares Lattice Algorithm Combined With Secondary-Path Innovation and Lattice-Order Decision Algorithms for Active Noise Control

This paper proposes a new active noise control (ANC) system based on a recursive least-squares lattice (RLSL) algorithm by designing secondary-path innovation (SPI) and lattice-order decision (LOD) algorithms. The SPI algorithm associated with the power spectral factorization of the secondary path is designed to apply the RLSL algorithm to the ANC system without the filtered-input structure. The SPI algorithm whitens the error microphone signals into the virtual error signals just ahead of the secondary path to construct the virtual desired signals corresponding to the outputs of the lattice filter. The LOD algorithm is designed to operate the ANC system without knowing the tap length of the optimal finite impulse response (FIR) filter. The decision algorithm using the initial and final estimation errors of the lattice filter estimates the tap length of the optimal FIR filter by increasing the lattice order one-by-one until the final estimation error reaches below a threshold value. Even in environments where the tap length of the optimal FIR filter is unknown, the proposed ANC system with low computational complexity shows almost the same performance as the ANC system based on a filtered-input recursive least squares algorithm.

Application of a Least Squares Lattice Algorithm to Active Noise Control for an Automobile

The purpose of this paper is to propose an alternative approach to active noise control (ANC) using the least squares lattice (LSL) algorithm. Typically, in ANC applications, the least-mean-square (LMS) algorithm has been used because of its simplicity. However, the LMS algorithm has the disadvantage of slow convergence speed in the case of broadband noise, such as the road noise present in the passenger compartment of automobiles traveling on rough road surfaces. In order to solve this problem, the LSL algorithm for ANC is considered. By computer simulation using actual car data, the LSL algorithm proves to be more effective than the LMS one.

A fixed-point implementation of the unnormalized least-squares lattice using scaled-integer arithmetic

The unnormalized least-squares lattice algorithm is modified for fixed-point hardware by appropriate scaling of the cross-correlation, covariance, and likelihood parameters. The scaling reveals the dependence of the unnormalized parameter magnitudes on the number of error signal observations used in their respective updates. Comparing fixed-point and floating-point simulations shows no significant loss of precision in the estimated PARCOR coefficients in the fixed-point environment with scaled parameters. Scaling requires seven more multiplies per lattice stage but provides a least-squares lattice algorithm suitable for a wide variety of fast low-power signal processing chips without square-root normalization.

Deconvolution of nonstationary seismic data using adaptive lattice filters

This paper examines the results of the application of two lattice algorithm to the problem of adaptive deconvolution on non-stationary seismic data. A comparative study of the deconvolution performance of the recently proposed gradient lattice and least-squares lattice algorithms is made with the help of experiments on simulated and real seismic data. We show that the gradient lattice algorithm is computationally superior, but it suffers from a possible slow rate of convergence, while the least-squares lattice has better convergence properties and is more robust numerically. We also show that both algorithms can yield equally good deconvolution results with a moderate amount of computation. Finally we indicate that a modified deconvolved output, derived as a linear combination of the forward and backward residuals, improves the performance without involving any additional computational burden.

Adaptive tracking of multiple sinusoids whose power levels are widely separated

The behavior of the gradient transversal filter (LMS), the gradient lattice (GL), and the least squares lattice (LSL) when used to track multiple sinusoidal (or narrow-band) components whose power levels are widely separated is investigated. These approaches to the realization of the pth-order one-step linear predictor of the time series are recursive in time. The lags of the instantaneous frequency estimates from their actual underlying values are of particular interest. The frequency tracking characteristics of the LMS, GL, and LSL algorithms are illustrated in several situations. Included are simulations of dual sinusoids undergoing a variety of frequency versus time dynamics and a formant tracking example of real speech.

Adaptive tracking of multiple sinusoids whose power levels are widely separated

The behavior of the gradient transversal filter (LMS), the gradient lattice (GL), and the least squares lattice (LSL) when used to track multiple sinusoidal (or narrow-band) components whose power levels are widely separated is investigated. These approaches to the realization of the pth-order one-step linear predictor of the time series are recursive in time. The lags of the instantaneous frequency estimates from their actual underlying values are of particular interest. The frequency tracking characteristics of the LMS, GL, and LSL algorithms are illustrated in several situations. Included are simulations of dual sinusoids undergoing a variety of frequency versus time dynamics and a formant tracking example of real speech.