An Improved Harmonic Current Detection Method Based on Variable Forgetting Factor RLS Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 2459-2463
Author(s):  
Minh Guang Vu ◽  
Wei Han ◽  
Da Zhi Wang ◽  
Yun Lu Li
Keyword(s):  
Dynamic Process ◽  
Detection Algorithm ◽  
Recursive Least Squares ◽  
Forgetting Factor ◽  
Harmonic Current ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Variable Forgetting Factor ◽  
The Impact ◽  
Current Detection

The effects of compensating and restraining the power system harmonics using active power filter are determined by the detection precision and its dynamic response characters. To improve both of them, a harmonic current detection algorithm based on variable forgetting factor Recursive Least-Squares algorithm is presented. The occurrence of the dynamic process is identified firstly by the judgment condition which is given by the algorithm, and then the forgetting factor is assigned dynamically, so that the convergent speed is significantly improved. The algorithm overcomes the impact of low-pass filter of traditional p-q or ip-iq algorithm, and releases the contradiction cased by the conflicting requirements of forgetting factor value between steady process and dynamic process. So it has better dynamic performance. Simulation and experiments prove the validity and feasibility of the approaches.

Optimization on Tracking Performance of RLS Algorithm Using Transfer Function of Butterworth Low Pass Filter

2013 ◽  
Vol 850-851 ◽  
pp. 856-859
Author(s):  
Si Wei Tan ◽  
Zhi Liang Ren
Keyword(s):  
Signal Processing ◽  
Transfer Function ◽  
Interference Cancellation ◽  
Tracking Performance ◽  
Forgetting Factor ◽  
Rls Algorithm ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Variable Forgetting Factor

In order to get further optimizations on tracking performance of the RLS algorithm in nonstationarity signal processing, and to simplify the variable forgetting factor updating model, a novel variable forgetting factor updating model based on Butterworth low pass filter transfer function was proposed. The relationship between the variable forgetting factor and the tracking performance of RLS algorithm was analyzed. The updating model proposed in this paper was built based on the modification of the transfer function of Butterworth low pass filter. The model function fit the theoretical variation curve of the variable forgetting factor well. It also could be adjusted by parameters of the function order and the critical point according to the different applications. In addition, the computation of the updating model was simple and convenience. The RLS algorithm with the updating model was tested in an adaptive interference cancellation system. Some conclusions were drawn from the simulation results. The optimized RLS algorithm had a better tracking performance in nonstationarity signal processing as well as small stationary errors after convergence.

Based on the CO2 Gas Shielded Welding Molten Pool Image Edge Detection Algorithm

2013 ◽  
Vol 437 ◽  
pp. 840-844 ◽  
Author(s):  
Xiao Gang Liu ◽  
Bing Zhao
Keyword(s):  
Edge Detection ◽  
Molten Pool ◽  
High Speed ◽  
Vision System ◽  
Detection Algorithm ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Defect Recognition ◽  
Pool Image

This paper use the passive vision system through high-speed camera collects molten pool images; and then according to the frequency domain characteristics of the weld pool image Butterworth low-pass filter; gradient method for image enhancement obtained after pretreatment. Research Roberts, Sobel, Prewitt, Log, Zerocross, and Canny 6 both traditional differential operator edge detection processing results. Through comparison and analysis of choosing threshold for [0.1, 0. Canny operator can get the ideal molten pool edge character, for subsequent welding molten pool defect recognition provides favorable conditions.

scholarly journals Collision detection algorithm for collaborative robots considering joint friction

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878899 ◽  
Author(s):  
Juliang Xiao ◽  
Qiulong Zhang ◽  
Ying Hong ◽  
Guodong Wang ◽  
Fan Zeng
Keyword(s):  
Collision Detection ◽  
Angular Acceleration ◽  
Detection Algorithm ◽  
Friction Model ◽  
Frequency Noise ◽  
Robot Interaction ◽  
Pass Filter ◽  
Low Pass Filter ◽  
First Order ◽  
Different Response

This article proposes a collision detection algorithm without external sensors that can detect potential collisions in man–robot interaction. The algorithm is based on a modified first-order momentum deviation observer that also takes friction into account. The collision detection algorithm uses joint angles, angular velocities, and torques during the detection process, without any need to consider angular acceleration. The algorithm also uses an accurate friction model that is based on a Stribeck model with second-order Fourier series compensation. The friction model is applied in advance so that compensation can be made in real time during collision detection. Identification data are filtered through a first-order low-pass filter to reduce high-frequency noise. In order to verify the algorithm, a simulation and experiment were carried out using a collaborative robot experimental platform. The results confirmed that collisions can be detected by setting appropriate threshold values. Different possible responses can be implemented according to different response strategies, with the ultimate arbiter being that collision forces are kept strictly within ordinary human tolerances. This makes sure that safety can be preserved in man–robot interaction processes.

scholarly journals Impact of the Incremental Analysis Updates on a Real-Time System of the North Atlantic Ocean

2008 ◽  
Vol 25 (11) ◽  
pp. 2055-2073 ◽  
Author(s):  
M. Benkiran ◽  
E. Greiner
Keyword(s):  
Real Time ◽  
North Atlantic ◽  
Weather Forecasting ◽  
Incremental Analysis ◽  
Real Time System ◽  
Pass Filter ◽  
Low Pass Filter ◽  
The North ◽  
The North Atlantic ◽  
The Impact

Abstract Incremental analysis updates (IAUs) are a procedure by which analysis increments can be incorporated into a model hindcast and forecast in a smooth manner. It is similar to nudging but has a better response, particularly in regions of missing data. The IAU procedure was popular in the late 1990s in weather forecasting centers, because it acts as a low-pass filter. The impact of the IAU is examined in the context of a real-time, eddy-permitting ocean forecasting system in the North Atlantic from Mercator Océan. Forecast scores and ocean physics are compared for the following three companion runs: a forced mode, a sequential analysis, and IAU. These comparisons confirm that the IAU is beneficial because it removes spinup effects such as spurious waves and tropical convective cells. Forecast scores are also slightly improved. In addition, contrary to the weather forecasting case where the model and data are fairly unbiased, the IAU has the advantage of correcting the systematic biases in the ocean data assimilation system.

Research on Improved Power Harmonic Current Detection Methods

2012 ◽  
Vol 241-244 ◽  
pp. 1889-1895
Author(s):  
Jin Yu Wang ◽  
Zhi Hui Shen ◽  
Yu Long Zhou ◽  
Pei Jiang Zhang
Keyword(s):  
Coordinate Transformation ◽  
Reactive Power ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Matlab Simulation ◽  
Harmonic Current ◽  
Two Phase ◽  
Three Phase ◽  
Filtering Effect ◽  
Current Detection

Although Active Power Filter can achieve good filtering effect and accurately compensate harmonic current, it dependents on the proposed current detection algorithm. The paper first discusses the method which is based on transient reactive power theory; the detection method is based on coordinate transformation. After the coordinate transformation is analyzed, the paper advances an harmonic method, this method can remove three-phase to two-phase and two-phase to three-phase coordinate transformation, and effectively reduce the computation, improve the detection speed. At last, the feasibility and validity of the new algorithm have been showed by MATLAB simulation

scholarly journals A New Simple Procedure for Extracting Coastline from SAR Image Based on Low Pass Filter and Edge Detection Algorithm

2021 ◽  
Vol 12 (3) ◽  
pp. 175
Author(s):  
Ni Nyoman Pujianiki ◽  
I Nyoman Sudi Parwata ◽  
Takahiro Osawa
Keyword(s):  
Simple Procedure ◽  
Detection Algorithm ◽  
Sar Image ◽  
Dual Polarization ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Edge Detection Algorithm ◽  
Low Pass ◽  
Pixel Value ◽  
Single Polarization

This study proposes a new simple procedure for extracting coastline from Synthetic Aperture Radar (SAR) images by utilizing a low-pass filter and edge detection algorithm. The low-pass filter is used to improve the histogram of the pixel value of the SAR image. It provides better distribution of pixel value and makes it easy to separate between sea and land surfaces. This study provides the processing steps using open-source software, i.e., SNAP SAR processor and QGIS application. This procedure has been tested using dual polarization Sentinel-1 (10x10 meters resolution) and single polarization ALOS-2 (3x3 meters resolution) dataset. The results show that using Sentinel-1 with dual polarization (VH) provides a better result than single polarization (VV). In the ALOS-2 case, only single polarization (HH) is available. However, even using only HH polarization, ALOS-2 provides a good result. In terms of resolution, ALOS-2 provides a better coastline than Sentinel-1 data due to ALOS-2 has better resolution. This procedure is expected to be helpful to detect coastline changes and for coastal area management.

scholarly journals HARMONIC COMPONENTS ESTIMATION IN POWER SYSTEM USING BACTERIAL FORAGING OPTIMIZATION ALGORITHM AND STOCHASTIC GRADIENT ALGORITHM WITH VARIABLE FORGETTING FACTOR

2016 ◽  
Vol 17 (1) ◽  
pp. 127-146
Author(s):  
Ahmad Mohammadzadeh ◽  
Jalil Sadati ◽  
Behrooz Rezaie
Keyword(s):  
Power System ◽  
Optimization Algorithm ◽  
Convergence Speed ◽  
Stochastic Gradient ◽  
Recursive Least Squares ◽  
Forgetting Factor ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Variable Forgetting Factor ◽  
Bacterial Foraging Optimization Algorithm

In this paper, a hybrid configuration algorithm called stochastic gradient method with variable forgetting factor (SGVFF) is proposed to better estimate unknown parameters in a power system such as amplitude and phase of harmonics using variable forgetting factor following the bacterial foraging optimization algorithm (BFO). It must be mentioned that harmonic estimation is a nonlinear problem and using linear optimization algorithms for solving this problem reduces the convergence speed. Thus, BFO algorithm is used for initial estimation. In this paper, first, using little information and by applying BFO algorithm in an off-line procedure initial value for SGVFF algorithm is achieved and then SGVFF algorithm is gained in an on-line procedure. In the hybrid algorithm applied in this paper, amplitudes and phases are estimated simultaneously. Simulation results indicate that the proposed method has faster convergence speed, better performance and higher accuracy in a noisy system in comparison with recursive least squares variable forgetting factors algorithm (RLSVFF). This proves the superiority of the proposed method.KEYWORDS:  Power system harmonic; BFO algorithm; SGVFF method; RLSVFF method

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