Yang Zhao

Model-based sensorless control of permanent magnet synchronous motor (PMSM) is promising for high-speed operation to estimate motor state, which is the speed and the position of the rotor, via electric signals of the stator, beside the inevitable fact that estimation accuracy is degraded by electromagnet interference (EMI) from switching devices of the converter. In this paper, the simulation system based on Luenberger observer and phase-locked loop (PLL) has been established, analyzing impacts of EMI on motor state estimations theoretically, exploring influences of EMI with different cutoff frequency, rated speeds, frequencies and amplitudes. The results show that Luenberger observer and PLL have strong immunity, which enable PMSM can still operate stably even under certain degrees of interference. EMI produces sideband harmonics that enlarge pulsation errors of speed and position estimations. Additionally, estimation errors are positively correlated with cutoff frequency of low-pass filter and the amplitude of EMI, and negatively correlated with rated speed of the motor and the frequency of EMI. When the frequency is too high, its effects on motor state estimations are negligible. This work contributes to the comprehensive understanding of how EMI affects motor state estimations, which further enhances practical application of sensorless PMSM.

Based on the research of genetic algorithm (GA) to optimize the BP neural network algorithm, this paper proposes a method for predicting twisted wire crosstalk based on the algorithm. Firstly, the equivalent circuit model of a multi-conductor transmission line is established, combined with the method of similarity transformation, the second-order differential transmission line equations are decoupled into n groups of independent two-conductor transmission line equations, and the crosstalk is finally solved. Then the mathematical model of the twisted wire is established and its structural characteristics are analyzed, and the GA-BP neural network algorithm is used to realize the mapping of the electromagnetic parameter matrix of the twisted wire and the position of the twisted wire. Finally, the mapping relationship is substituted into the transmission line equation, and the near-end crosstalk (NEXT) and the far-end crosstalk (FEXT) of an example three-core twisted wire are predicted based on the idea of cascade combined. By comparing with the transmission line matrix method (TLM), it can be seen that the method proposed in this paper is in good agreement with the crosstalk results obtained by the electromagnetic field numerical method, which verifies the effectiveness of the algorithm proposed in this paper.

In this article, an efficient domain decomposition method finite element method (DDM-FEM) algorithm is presented for the lossy twisted pair cable. In harsh environment and anti-interface ability, cables need high toughness, noise immunity, and extraordinary strength. We, in this paper, simulate a physical model of twisted pair cable and apply a hybrid solver of DDM-FEM to analyze these problems by compression and approximation of matrix-vector product. The DDM-FEM solver along with matrix compression is used to compute the RLCG, propagation constant in the twisted pair cable, and to reduce the computational time and memory size. Therefore, in the proposed algorithm, the complexities of the system become linear. The study compares the calculated results with the existing standard to verify the effectiveness of the proposed algorithm.

Based on the theory of multi-conductor transmission lines (MTL), this paper proposes a new method for predicting and suppressing crosstalk of twisted-wire pair (TWP). The per unit length (p.u.l) RLCG parameters change caused by the inconsistent cross-sectional shape of TWP, changes in parameters make it difficult to solve the telegraph equation. In this paper, the method of transmission lines cascade is used. TWP is divided into several segments, and p.u.l parameters of each segment are predicted. Compared with before method, we propose a higher precision algorithm—beetle swarm optimization (BSO) to optimize the weights of back-propagation (BP) neural network, which predict p.u.l parameters at each segment. On this basis, it is divided into two steps: 1) Use MTL frequency domain method combined with lines’ terminal conditions to solve crosstalk and compare with CST simulation results; 2) Use the singular value decomposition (SVD) method to add matrix modules at both ends of lines for suppressing crosstalk. The results show that proposed method in this paper is consistent with the simulation, and the accuracy is higher than before

Power electronic devices and electrical equipment produce a large quantity of radiated electromagnetic interference (EMI) in the working process. Electromagnetic field analysis method of the near-field and far-field of the antenna can be employed to separate the radiated interference noises. However, the method has low separation accuracy and requires a lot of time. A novel method based on joint approximate diagonalization algorithm (JADA) to separate the radiated EMI noise is proposed in this paper. Compared with the traditional independent component algorithm method, the accuracy is improved by 14% and the efficiency is increased by 30%. The common-differential mode mixed noise source is taken as the experimental object to demonstrate the

This paper proposes a novel crosstalk prediction method between the triple-twisted strand (uniform and non-uniform) and the signal wire, that is, using back-propagation neural network optimized by the beetle antennae search algorithm based on chaotic disturbance mechanism (CDBAS-BPNN) to extract the per unit length (p.u.l) parameter matrix, and combined with the chain parameter method to obtain crosstalk. Firstly, the geometric model and cross-sectional model between the uniform triple-twisted strand and the signal wire are established, and the corresponding model between the non-uniform triple-twisted strand and the signal wire is obtained by the Monte Carlo (MC) method. Then, the beetle antennae search algorithm based on chaotic disturbance mechanism (CDBAS) and backpropagation neural network (BPNN) are combined to construct a new extraction network of the p.u.l parameter matrix, and the chain parameter method is combined to predict crosstalk. Finally, in the verification and analysis part of the numerical experiments, comparing the crosstalk results of CDBAS-BPNN, BAS-BPNN and Transmission Line Matrix (TLM) algorithms, it is verified that the proposed method has better accuracy for the prediction of the model.

In this paper, a twisted-wire pairs (TWP) with random non-uniform twisting is established. It is divided into a complete pitch segment and a noncomplete pitch segment by the ratio between the pitch and the length. The randomness of the actual TWP cable is accurately simulated by the following methods: 1) random combination of complete pitch segments; 2) random combination of non-complete pitch segments; 3) random combination between 1) and 2). Based on the TWP model, an equivalent multi-conductor transmission lines (MTLs) model can be obtained. The neural network algorithm is introduced to describe the complex relationship between the arbitrary position of the TWP and the per-unit-length (p.u.l) parameter matrix. In addition, the crosstalk and the common-mode (CM) and differential-mode (DM) noise under field-to-wire coupling are predicted. The numerical results show that crosstalk and CM/DM noise in TWP cable are susceptible to the twisted pitch at high frequencies. Compared with full-wave simulation, the accuracy of the proposed method is proved.

This letter presents an algorithm for canceling crosstalk in multiconductor transmission lines (MTL). At present, to reduce the crosstalk, multiconductor systems usually use differential mode transport, in which the number of transmitted signals is half the number of conductors. A method is proposed to cancel crosstalk in MTL, through the singular value decomposition (SVD) method in matrix theory, in which the transmission line matrix is converted into an identity matrix. That is, the off-diagonal elements in the transmission matrix are zero, which corresponds to crosstalk cancellation. In this letter, this method is applied to the previous literature models to verify its effectiveness firstly. Then, the method is applied to a triple-twisted strand. In the strand, different rotation degrees correspond to different per unit length (p.u.l) parameter matrix. A set of parameters are extracted, the rest of are predicted by the beetle antennae search-back propagation neural network (BAS-BPNN) proposed in a previous work. The simulation results show that the near and far end crosstalk can be canceled effectively.

Aiming at the radiated electromagnetic interference (EMI) noise of electronic equipment, a novel method of radiated EMI noise analysis based on non-linear principal component analysis (NLPCA) algorithm is proposed in this paper. In order to obtain multiple independent common-mode / differential-mode radiated sources, and to find the sources that cause the radiated noises that exceed the limit of standard, NLPCA algorithm is used to process the near-field radiated signals superimposed by multiple radiated sources. The simulation results show that NLPCA can successfully screen out the radiated EMI noises which exceed the limit of standard. Moreover, the experiments are carried out with three models: double-common-mode hybrid sources, double-differential-mode hybrid sources and common-differential-mode hybrid sources. Compared with the traditional independent component algorithm (ICA), the method proposed in this paper can separate the radiated EMI noise sources more accurately and quickly. It can be concluded that the accuracy of NLPCA algorithm is 10% higher than ICA algorithm. This work will contribute to trace the radiated EMI noise sources, and to provide the theoretical basis for the future suppression.