Performance Analysis of Nested-loop Secondary Linear Doubly-fed Machine Considering End Effects

Nested-loop secondary linear doubly-fed machine(NLS-LDFM) is a novel linear machine evolved from rotary brushless doubly-fed induction machine, which has a good application prospect in linear metro. In order to analyze the performance of NLS-LDFM, the mechanism and action rules of end effects are investigated in this paper. Firstly, the mechanism of static and dynamic end effects is analyzed in aspect of direct coupling, winding asymmetry and transient secondary current. Furthermore, based on the winding theory for short primary linear machines, the machine parameters are established qualitatively considering pulsating magnetic field of NLS-LDFM. Finally, the NLS-LDFM performance analysis is supplemented by the finite element algorithm(FEA) simulation and experiments under different operating conditions.

Polar motion prediction using the combination of SSA and ARMA

High-precision polar motion(PM) prediction is of important significance in astronomy, geodesy, aviation,hydrographic mapping, interstellar navigation, and so on. SSA can effectively extract the trend and period terms of PM,in the process of achieving high-precision medium-and long-term polar motion prediction,it is necessary to solve the end effect problem and overfitting problem of SSA forecasting method;therefore, ARMA was applied to decreasethe end effect, and a suitable combination of reconstructed components was determined to avoid the high variance reaction of SSA overfitting. Based on the decomposition and reconstruction of the PM by SSA, the reconstructed components are determined to participate in the SSA iterative fitting model according to the variance contribution rate. The combination of the reconstructed components representing the polar motion period term and the trend term is determined according to the correlation analysis of the selected reconstructed components. After the above work, the principal component prediction sequence is obtained by fitting the period term and the trend term to convergence, respectively, and then, the SSA end effect is modified, and the residual term is predicted based on ARMA. The test results show that he prediction accuracy of SSA + ARMA at the front of the X and Y directions are improved by 96.90% and 97.53% compared with those of SSA, respectively,and the forecast accuracy of 365 days are improved by 37.93% and 19.53% in the X and Y directions compared with those of Bulletin A, respectively.

Hilbert-Huang transform with adaptive waveform matching extension and its application in power quality disturbance detection for microgrid

With the significant improvement of microgrid technology, microgrid has gained large-scale application.However, the existence of intermittent distributed generations, nonlinear loads and various electrical and electronic devices causes power quality problem in microgrid, especially in islanding mode. An accurate and fast disturbance detection method which is the premise of power quality control is necessary. Aiming at the end effect and the mode mixing of original Hilbert-Huang transform(HHT), an improved HHT with adaptive waveform matching extension is proposed in this paper. The innovative waveform matching extension method considers not only the depth of waveform, but also the rise time and fall time. Both simulations and field experiments have verified the correctness and validity of the improved HHT for power quality disturbance detection in microgrid.

A Proposal of a Spline Filter that Achieves Both Robustness and Lower Compatibility

A spline filter is used to extract roughness profiles for the measurement of surface texture and is useful when sufficient sections before and after a primary profile could not be secured.This is because a spline filter could prevent the end effect,which is the fluctuation of output data that occurs around both ends of the primary profile and depends on the width of the weighting function of a filter.The spline filter is based on the calculation of an inverse matrix instead of the convolution of the weighting function of the filters.When input data include outliers,the output of a spline filter greatly fluctuates.To solve this problem,we propose a robust spline filter.However,output of the robust spline is not in agreement with that of the spline filter when input data do not include outliers.Thus,the robust spline filter has no lower compatibility.In this paper,the"Fast M-estimation Spline Filter(FMSF)"is proposed.FMSF uses the fast M-estimation method.FMGF performs robust to input data including outliers,and gives the same output as that of general spline filters to input data without outliers.The value estimated by this method is in agreement with that estimated by the least squares method if no outlier exists.In order to apply the fast M-estimation method to the spline filter,convolution of weight function is used instead of the inverse matrix.And the input geometry with shear and point-symmetric extensions is pre-processed to prevent the end effect.