Table of Content

15 December 2021 Volume 34 Issue 12

  

A Novel Sliding Mode Observer for Position Sensorless Control of Permanent Magnet Synchronous Motor

TONG Zhaojing,ZHENG Quan,HAN Yaofei,HE Guofeng,QIN Zini

Electronic Science and Technology. 2021, 34(12):  1-6.  doi:10.16180/j.cnki.issn1007-7820.2021.12.001

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In this study, a novel variable gain sliding mode observer is proposed to eliminate the chattering of traditional sliding mode observer and the phase delay of rotor position estimation. The designed sliding mode gain can vary with the back EMF value, and the back EMF observer, instead of the low-pass filter, is used to reduce the high frequency signal and chattering of the back EMF estimation value. The mathematical model of position sensorless control system is established, and the novel sliding mode observer is compared with the traditional sliding mode observer. The results show that the error of the new sliding film observer speed in steady state, acceleration and loading conditions is significantly reduced, which proves that the proposed new sliding mode observer has smaller chattering and higher accuracy.

Electromagnetic Structure Optimization of Three-Phase Hybrid Stepping Motor Based on Taguchi Method

FANG Lingli,XIAO Longfei,BI Chao

Electronic Science and Technology. 2021, 34(12):  7-12.  doi:10.16180/j.cnki.issn1007-7820.2021.12.002

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The tooth layer area of a hybrid stepping motor is the key to its torque generation. To achieve specific performance, the appropriate tooth layer geometry needs to be selected during motor design. In this study, the shape parameters of the trapezoidal tooth are optimized, and the electromagnetic structure of the motor is optimized to achieve a higher torque output. Specifically, an existing three-phase hybrid stepping motor in the market is used as a prototype. Based on its structural parameters, an efficient three-dimensional finite element model is established to generate optimized samples. Then, combined with the Taguchi optimization algorithm, the stator and rotor tooth shape parameters are used as optimization design variables to obtain the best trapezoidal tooth structure through numerical methods to maximize the motor output torque. After optimization, the torque peak of the torque-maximizing prototype is increased by nearly 25%, which proves the effectiveness of the optimization scheme.

Design and Simulation of High Power Factor PMSM without Electrolytic Capacitor

JIANG Kaiwen,JIN Hai,XU Shen

Electronic Science and Technology. 2021, 34(12):  13-18.  doi:10.16180/j.cnki.issn1007-7820.2021.12.003

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Permanent magnet synchronous motors are widely used because of low cost and long life. Low power factor and high current distortion rate are the major problems in the application of permanent magnet synchronous motors without electrolytic capacitor. In view of the problems above, this study puts forward a strategy of quadrature axis phase compensation and direct axis field weakening control based on the analysis of the power factor of the permanent magnet synchronous motors and the vector control algorithm, and establishes a simulation model in MATLAB/Simulink environment for verification. The simulation results show that under the condition of bus voltage fluctuations, the motor quadrature axis and direct axis current can track the fluctuating given current smoothly. Compared with the traditional vector control, the proposed control strategy eliminates the phase difference between the current and the voltage, and the power factor reaches 93.51%. The simulation results reveal that the phase compensation combined with the field weakening control strategy can effectively improve the power factor and reduce the electromagnetic pollution of the motor system to the power grid.

Comparative Analysis of Performance of Electric Excitation Concentrated Winding Motor

PENG Xinglai,LI Zheng

Electronic Science and Technology. 2021, 34(12):  19-24.  doi:10.16180/j.cnki.issn1007-7820.2021.12.004

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To reduce the torque ripple, this study investigates the effect of different slot/pole ratios and air gaps on the torque ripple of the motor. Under the same size, same speed, and the same excitation, the performance analysis of the electric excitation concentrated winding motor with 12/10, 18/10, 30/10 is carried out. First, the finite element analysis of the motors with different slot/pole ratios is performed and the influence of the uniformity of the air gap of the motors on the cogging torque is comparatively analyzed. Through comparison, it is found that an appropriate slot/pole ratio can reduce cogging torque and the harmonic content of back EMF, and from the perspective of cogging torque, fractional slot motors are lower than integer slot motors. The use of non-uniform air gap can reduce the content of air gap magnetic density harmonics, therefore further reducing cogging torque and torque ripple. After finite element calculation, the inductance of these three kinds of motors and their influence on torque are analyzed. Finally, it is found that the non-uniform air gap has a more obvious effect on reducing the torque ripple of the motor. The torque ripple percentage of the 12/10 motor has been reduced from 10.6% to 4.2%.

A Fault Detection Method for Mechanical System Based on Wideband Spectrum Processing

BAI Xingyu,HUA Shenghui,JIANG Yu,ZHANG Min

Electronic Science and Technology. 2021, 34(12):  25-29.  doi:10.16180/j.cnki.issn1007-7820.2021.12.005

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A fault detection method based on wideband spectrum processing is proposed to monitor the operation state of mechanical systems in this study. This method is based on interference noise suppression and voiceprint comparison technology. According to the characteristics of the interference noise source, the method of autocorrelation denoising and wavelet denoising is combined to effectively suppress the interference noise. On this basis, the method uses the Euclidean distance classifier to classify the collected voiceprint signals, so as to better detect, extract and identify the faulty voiceprint signals of the mechanical system, and realize the monitoring and fault detection of the operating state of the mechanical system. The proposed fault detection method is verified by numerical simulation, and the simulation results show that the fault detection method has good detection performance.

Calculation of Motor Temperature Field Based on Fluent

FANG Xin,WU Yaohui,WU Haozhen

Electronic Science and Technology. 2021, 34(12):  30-35.  doi:10.16180/j.cnki.issn1007-7820.2021.12.006

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The calculation of motor temperature field is an essential part of motor design. The temperature rise of motor is related to the safety and stability of the whole system and related equipment. According to the parameters of the motor, the electromagnetic model of the motor is established to calculate the loss, and the loss is loaded into the two-dimensional model in the form of heat source density to calculate the motor temperature field. In order to improve the accuracy of temperature rise calculation, the equivalent thermal conductivity method is used to process the heat exchange in the air gap of the motor according to the structure characteristics of the moto. The accuracy of the equivalent thermal conductivity method is verified by comparing the simulation results with the experimental data. Fluent software is adopted to solve the two-dimensional temperature field model in a unidirectional coupling way. Finally, the temperature field data obtained by the experiment is compared with the simulation calculation results to verify the accuracy of the two-dimensional model to calculate the steady-state temperature field of the experimental motor.

Estimation Model of Wind Power Reserve Capacity Based on PSO-BP Neural Network

ZHU Chengming,WEI Yunbing,JIANG Chengcheng,ZHU Jian'an

Electronic Science and Technology. 2021, 34(12):  36-41.  doi:10.16180/j.cnki.issn1007-7820.2021.12.007

Abstract ( 238 )   HTML ( 5 )   PDF (807KB) ( 37 )  

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In view of the effect of the volatility and randomness of wind power on the dispatching of the power grid, a neural network-based wind power reserve capacity estimation model is proposed. In this model, the weights of each link layer in BP neural network are optimized by particle swarm optimization algorithm to improve the prediction of wind power value in the future. The Pearson correlation coefficient is used to extract the influence factors which are positively correlated with the prediction error, and then the multiple linear regression method is used to associate the extracted influencing factors to calculate the reserve capacity of wind power. The simulation results show that 80% of the prediction error is within the estimation range of the model, further verifying the effectiveness of the model.

Load Forecasting Method Based on Ensemble Empirical Mode Decomposition and ARIMA-GRNN

WANG Hongliang,CHEN Xinyuan,ZHAO Yumeng

Electronic Science and Technology. 2021, 34(12):  42-48.  doi:10.16180/j.cnki.issn1007-7820.2021.12.008

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In view of the problem that traditional load forecasting methods hardly take into account the inherent linear and nonlinear characteristics of power load, this study proposes a hybrid load forecasting model based on EEMD and ARIMA-GRNN. In this method, EEMD method is used to decompose the load data into IMF components and residual terms without modal aliasing. ARIMA model algorithm is used to make linear prediction for each IMF component to obtain the time series prediction component, and subtract it from the original data to obtain the non-linear component. The non-linear component is predicted by the GRNN neural network algorithm to obtain the predicted value of the non-linear component. The obtained linear and the nonlinear predicted components are added to obtain the final predicted value. The simulation results show that the hybrid forecasting model based on EEMD and ARIMA-GRNN proposed in the present study is superior to the load forecasting method using a single algorithm in terms of forecasting accuracy and performance.

Medium and Long-Term Load Forecasting Based on Optimized Grey Fourier Residual Correction

ZHU Jian'an,WEI Yunbing,ZHU Pengjie,JIANG Chengcheng,ZHU Chengming

Electronic Science and Technology. 2021, 34(12):  49-55.  doi:10.16180/j.cnki.issn1007-7820.2021.12.009

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In view of the problem of low accuracy of mid- and long-term load forecasting in power systems, a gray Fourier residual correction modified particle swarm optimization model for mid- and long-term power load forecasting is proposed. The model uses a three-point smoothing method to preprocess the original load data to weaken the influence of outliers. The gray model of improved particle swarm optimization is adopted to predict the original load, which solves the problems of insufficient forecast data and low forecast accuracy. Fourier transform corrects the prediction error and greatly improves the prediction accuracy. The results of the annual load forecasting in a certain area of Zhejiang from 2013 to 2018 show that the average load forecasting accuracy has increased by 1.94%, which indicates that the model has high accuracy and feasibility in medium and long-term load forecasting.

Face Recognition System Based on Improved PCA+SVM

PENG Rongjie,PENG Yaxiong,LU Anjiang

Electronic Science and Technology. 2021, 34(12):  56-61.  doi:10.16180/j.cnki.issn1007-7820.2021.12.010

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The PCA algorithm has low recognition rate for digital image processing, and cannot deal with the non-linear features of the face. In view of this problem, in the basis of original PCA algorithm, a face recognition research method based on KPCA combined with SVM is proposed in this study. By using the internal non-linear kernel function of the KPCA algorithm after the PCA is improved by the kernel, the facial contours of the face are extracted, the non-linear feature data is processed and the data dimension is reduced, which can better reduce the space required for feature data storage and improve the computing power. Then, combined with the SVM classifier for classification and recognition, the system recognition rate is improved. Experiments show that the recognition rate of the proposed algorithm in the ORL face database is 95.16%, and the recognition rate in the Yale face database is 95.10%. The system established on MATLAB can correctly recognize human faces, which proves the feasibility of the system proposed in the study, and has certain reference value for actual research.

A Semi-Dense 3D Reconstruction ORB-SLAM Algorithm with Improved ORB Feature Matching

CHEN Wenyou,ZHANG Wei,SHI Xiaofan,SONG Fang

Electronic Science and Technology. 2021, 34(12):  62-67.  doi:10.16180/j.cnki.issn1007-7820.2021.12.011

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The goal of visual SLAM technology is to build a more complete map and estimate a more accurate camera pose. To construct a more detailed and complete 3D map, a semi-dense 3D reconstruction ORB-SLAM algorithm with improved ORB feature matching is proposed to realize the construction of a sparse 3D point cloud map of the environment. On the basis of the ORB-SLAM algorithm, a semi-dense mapping thread is added to establish a semi-dense 3D point cloud map. Then, the scale invariance of SURF feature matching algorithm is used to improve ORB feature matching. Simulation experiments in the TUM RGBD datasets reveal that the 3D map established by the improved ORB-SLAM algorithm can more intuitively exhibit the contours of objects in the environment when compared with the ORB-SLAM algorithm, and the feature matching accuracy is increased. Finally, the consistency of the algorithm is verified by experiments in two datasets.

Semantic Segmentation of Cervical Cell Image Based on Weak Supervision

ZHANG Can,CHEN Wei,YIN Zhong

Electronic Science and Technology. 2021, 34(12):  68-74.  doi:10.16180/j.cnki.issn1007-7820.2021.12.012

Abstract ( 336 )   HTML ( 10 )   PDF (1187KB) ( 57 )  

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Neural network algorithm based on weakly supervised label is a hot research topic in medical field. In view of the lack of labeling data and the inaccurate segmentation of cytoplasm and nucleus, this study proposes a semantic segmentation algorithm based on weakly supervised cervical cell images. First, the algorithm uses unsupervised K-means as the labeling function to generate cell image segmentation labels. Then, training is conducted through an improved Encoder-Decoder network. Subsequently, CRF is introduced as the last layer of the network to integrate the global information of the image and optimize the segmentation results. The label and prediction images are optimized and trained in three times to achieve pixel level classification of cell images. Finally, the algorithm is verified using the cervical cell image dataset. The experimental results show that the algorithm has high generalization ability, and the accuracy rate is up to 96.7%.

Security State Estimation and Detection for Biasing Attack

SUN Yangyan,ZHOU Xiuying,REN Zhu

Electronic Science and Technology. 2021, 34(12):  75-80.  doi:10.16180/j.cnki.issn1007-7820.2021.12.013

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Cyber-physical system is an intelligent system integrating computing, communication and control, which can realize the deep cooperation between network and physics. Biasing attack infuses constant false bias data by attacking operational data in information physical system, which leads to system state estimation error and affects the normal operation of the system. To solve this problem, the study adopts the attack detection model combining Kalman filter state estimator and t detector based on the least trace principle to detect the biasing attack of the measurement residual error of the system based on the optimal state estimation in the same attack scene. The detection of false biasing data is based on the measurement of the observed value of the residual, and the system is attacked according to the bias of the target observation function. Furthermore, the present study proposes the attack detection threshold of the target observation function and the t-test scheme based on hypothesis testing. MATLAB simulation shows that the scheme can detect the occurrence of biasing attack in a short time, and the detection rate is increased by more than 2% compared with traditional detection methods, and has stronger robustness.

Multi-Sensor Data Fusion in Measurement of Flow Field of Air Velocity in Three-Dimensional Large Space

XIAO Xinzhao,LIU Jianxu,WU Guojing,FU Dongxiang

Electronic Science and Technology. 2021, 34(12):  81-86.  doi:10.16180/j.cnki.issn1007-7820.2021.12.014

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Flow field in the enclosed three-dimensional space is very important for the design of the ventilation system. In view of the data processing of the air velocity sensor array composed of multiple sensors, a multi-sensor data fusion algorithm based on the correlation function-Kalman filter algorithm is proposed in this study. Invalid data acquired by flow sensors is excluded by correlation judgement in measuring. Then, the sensor calibration output data and variance are used as the initial estimated value and variance estimation of Kalman filter to perform the multi-sensor data fusion. Compared with the measurement of common sensor calibration, the measurement error of air velocity obtained by this method is smaller. The experimental results show that the method can effectively improve the measurement accuracy, and the experimental results of the three-dimensional flow field measurement based on the data processing method are accurate and reliable.