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Press-pack insulated gate bipolar transistors (PP-IGBTs) are commonly connected in series and stacked together with heatsinks using an exterior clamping fixture in order to achieve high-voltage dc-link levels. A suitable contact area between the clamping fixture and the device is essential to ensuring optimal PP-IGBT thermomechanical performance, especially for the first and last devices in a stack. In this study, the effects of the clamping area on collector deformation, temperature, and stress distributions are investigated by means of the finite-element method (FEM). Moreover, this article analyzes the influence of heatsink thickness to maximize the stress evenness of the terminal PP-IGBT and reduce the overall length of the stack system. The results indicate that the collector lid is prone to warpage due to thermal expansion, which results in a decrease in the effective contact area between component layers. As the contact resistance increases, the chips accumulate considerable heat. Increasing the clamping area at this point can adequately compensate for the warp deformation and can also improve the stress uniformity of the chips. Finally, an experiment making use of stress-sensitive film has been carried out to verify the developed FEM models.
Siyang Dai; Zhiqiang Wang; Haimeng Wu; Xueguan Song; Guofeng Li; Volker Pickert. Thermal and Mechanical Analyses of Clamping Area on the Performance of Press-Pack IGBT in Series-Connection Stack Application. IEEE Transactions on Components, Packaging and Manufacturing Technology 2021, 11, 200 -211.
AMA StyleSiyang Dai, Zhiqiang Wang, Haimeng Wu, Xueguan Song, Guofeng Li, Volker Pickert. Thermal and Mechanical Analyses of Clamping Area on the Performance of Press-Pack IGBT in Series-Connection Stack Application. IEEE Transactions on Components, Packaging and Manufacturing Technology. 2021; 11 (2):200-211.
Chicago/Turabian StyleSiyang Dai; Zhiqiang Wang; Haimeng Wu; Xueguan Song; Guofeng Li; Volker Pickert. 2021. "Thermal and Mechanical Analyses of Clamping Area on the Performance of Press-Pack IGBT in Series-Connection Stack Application." IEEE Transactions on Components, Packaging and Manufacturing Technology 11, no. 2: 200-211.
This paper presents a hybrid approach combining phase space reconstruction (PSR) with a convolutional neural network (CNN) for power quality disturbance (PQD) classification. Firstly, a PSR technique is developed to transform a 1D voltage disturbance signal into a 2D image file. Then, a CNN model is developed for the image classification. The feature maps are extracted automatically from the image file and different patterns are derived from variables in CNN. A set of synthetic signals, as well as operational measurements, are used to validate the proposed method. Moreover, the test results are also compared with existing methods, including empirical mode decomposition (EMD) with balanced neural tree (BNT), S-transform (ST) with neural network (NN) and decision tree (DT), hybrid ST with DT, adaptive linear neuron (ADALINE) with feedforward neural network (FFNN), and variational mode decomposition (VMD) with deep stochastic configuration network (DSCN). Based on deep learning algorithms, the proposed method is capable of providing more accurate results without any human intervention for PQDs. It also enables the planning of PQ remedy actions.
Kewei Cai; Taoping Hu; Wenping Cao; Guofeng Li. Classifying Power Quality Disturbances Based on Phase Space Reconstruction and a Convolutional Neural Network. Applied Sciences 2019, 9, 3681 .
AMA StyleKewei Cai, Taoping Hu, Wenping Cao, Guofeng Li. Classifying Power Quality Disturbances Based on Phase Space Reconstruction and a Convolutional Neural Network. Applied Sciences. 2019; 9 (18):3681.
Chicago/Turabian StyleKewei Cai; Taoping Hu; Wenping Cao; Guofeng Li. 2019. "Classifying Power Quality Disturbances Based on Phase Space Reconstruction and a Convolutional Neural Network." Applied Sciences 9, no. 18: 3681.
This paper proposes a hybrid approach combining Wigner-Ville distribution (WVD) with convolutional neural network (CNN) for power quality disturbance (PQD) classification. Firstly, a WVD technique is developed to transfer a 1D voltage disturbance signal into a 2D image file, followed by a CNN model developed for the image classification. Then, the feature maps are extracted automatically from the image file and different patterns are extracted from variables on CNN. A set of synthetic signals, as well as real-world measurement data, are used to test the proposed method. The high classification accuracy of test results is achieved to confirm the effectiveness of the proposed method. Furthermore, the model is simplified and optimized by visualizing the output of convolutional layers. On this basis, one visualizing technique called the class activation map (CAM) is used to identify the location and shape of “hotspots (PQDs)”. The effect of incorrect classification of the model is analyzed with the CAM. Therefore, the proposed method is proved to have the capability of providing necessary and accurate information for PQDs, which will then be used to determine the subsequent PQ remedy actions accordingly.
Kewei Cai; Wenping Cao; Lassi Aarniovuori; Hongshuai Pang; Yuanshan Lin; Guofeng Li. Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks. IEEE Access 2019, 7, 119099 -119109.
AMA StyleKewei Cai, Wenping Cao, Lassi Aarniovuori, Hongshuai Pang, Yuanshan Lin, Guofeng Li. Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks. IEEE Access. 2019; 7 (99):119099-119109.
Chicago/Turabian StyleKewei Cai; Wenping Cao; Lassi Aarniovuori; Hongshuai Pang; Yuanshan Lin; Guofeng Li. 2019. "Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks." IEEE Access 7, no. 99: 119099-119109.
Separation of aluminum from fine granules of black dross, which is a waste by-product in secondary aluminum production, was investigated. The separation was performed by a multi-stage electrostatic separation method. There are three stages to complete the separation, including preliminary separation, pulse charging enhancement and secondary concentration. Chemical and mineralogical compositions of collection products were analyzed and determined by X-ray diffraction (XRD) and X-ray Fluorescence (XRF). After multistage electrostatic separation, the Al2O3 content of the collection products increases from 50.74% to 69.77%. The mineralogical phase analysis indicates that the final recovery of metallic aluminum phase increases from 8% to 37%, and the aluminum oxide phase increases from 20% to 26%. The research results show the multi-stage electrostatic separation method is effective for recovering of aluminum from fine granules of black dross, and upgrades the black dross to a recoverable material.
Yunxiao Cao; Zhiqiang Wang; Jinjun Wang; Guofeng Li. Multi-stage Electrostatic Separation for Recovering of Aluminum from Fine Granules of Black Dross. Journal of Wuhan University of Technology-Mater. Sci. Ed. 2019, 34, 925 -931.
AMA StyleYunxiao Cao, Zhiqiang Wang, Jinjun Wang, Guofeng Li. Multi-stage Electrostatic Separation for Recovering of Aluminum from Fine Granules of Black Dross. Journal of Wuhan University of Technology-Mater. Sci. Ed.. 2019; 34 (4):925-931.
Chicago/Turabian StyleYunxiao Cao; Zhiqiang Wang; Jinjun Wang; Guofeng Li. 2019. "Multi-stage Electrostatic Separation for Recovering of Aluminum from Fine Granules of Black Dross." Journal of Wuhan University of Technology-Mater. Sci. Ed. 34, no. 4: 925-931.
In the process of electric motor design, it is essential to predict and provide an accurate thermal and mechanical model. The aim of this research is to improve the thermal and mechanical performance—which is implemented into a 72/48 switched reluctance motor (SRM) with 75 kW—of a low-speed direct-drive mining system (pulverizer). Thermal analysis of the SRM requires a deep understanding of the coolant behavior and the thermal mechanism in the motor. Computational fluid dynamics (CFD) based finite element analysis (FEA) was carried out in order to precisely visualize and estimate fluid state and temperature distribution inside the motor. Several different coolant configurations were carried out, with the purpose of determining an appropriate one for uniform temperature distribution in the SRM. The natural frequencies are presented with the developed finite element mechanical, structural model. To adapt in the mining application, the cooling jacket configurations with 17 channels and the shaft with spoke was found to be optimal for the SRM, which may raise the natural frequency and reduce the weight and temperature of the motor. The simulations results showed a good agreement with experimental results regarding temperature distribution within the motor.
Esmail Elhomdy; Zheng Liu; Guofeng Li. Thermal and Mechanical Analysis of a 72/48 Switched Reluctance Motor for Low-Speed Direct-Drive Mining Applications. Applied Sciences 2019, 9, 2722 .
AMA StyleEsmail Elhomdy, Zheng Liu, Guofeng Li. Thermal and Mechanical Analysis of a 72/48 Switched Reluctance Motor for Low-Speed Direct-Drive Mining Applications. Applied Sciences. 2019; 9 (13):2722.
Chicago/Turabian StyleEsmail Elhomdy; Zheng Liu; Guofeng Li. 2019. "Thermal and Mechanical Analysis of a 72/48 Switched Reluctance Motor for Low-Speed Direct-Drive Mining Applications." Applied Sciences 9, no. 13: 2722.
This paper proposes a novel, two-stage and hybrid approach based on variational mode decomposition (VMD) and the deep stochastic configuration network (DSCN) for power quality (PQ) disturbances detection and classification in power systems. Firstly, a VMD technique is applied to discriminate between stationary and non-stationary PQ events. Secondly, the key parameters of VMD are determined as per different types of disturbance. Three statistical features (mean, variance, and kurtosis) are extracted from the instantaneous amplitude (IA) of the decomposed modes. The DSCN model is then developed to classify PQ disturbances based on these features. The proposed approach is validated by analytical results and actual measurements. Moreover, it is also compared with existing methods including wavelet network, fuzzy and S-transform (ST), adaptive linear neuron (ADALINE) and feedforward neural network (FFNN). Test results have proved that the proposed method is capable of providing necessary and accurate information for PQ disturbances in order to plan PQ remedy actions accordingly.
Kewei Cai; Belema Prince Alalibo; Wenping Cao; Zheng Liu; Zhiqiang Wang; Guofeng Li. Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network. Energies 2018, 11, 3040 .
AMA StyleKewei Cai, Belema Prince Alalibo, Wenping Cao, Zheng Liu, Zhiqiang Wang, Guofeng Li. Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network. Energies. 2018; 11 (11):3040.
Chicago/Turabian StyleKewei Cai; Belema Prince Alalibo; Wenping Cao; Zheng Liu; Zhiqiang Wang; Guofeng Li. 2018. "Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network." Energies 11, no. 11: 3040.
This paper introduces a new rotor design for the easy insertion and removal of rotor windings. The shape of the rotor is optimized based on a surrogate method in order to achieve low power loss under the maximum power output. The synchronous machine with the new rotor is evaluated in 2-D finite element software and validated by experiments. This rotor shows great potential for reducing the maintenance and repair costs of synchronous machines, making it particularly suited for low-cost mass production markets including gen-sets, steam turbines, wind power generators, and hybrid electric vehicles.
Tiejiang Yuan; Nan Yang; Wei Zhang; Wenping Cao; Ning Xing; Zheng Tan; Guofeng Li. Improved Synchronous Machine Rotor Design for the Easy Assembly of Excitation Coils Based on Surrogate Optimization. Energies 2018, 11, 1311 .
AMA StyleTiejiang Yuan, Nan Yang, Wei Zhang, Wenping Cao, Ning Xing, Zheng Tan, Guofeng Li. Improved Synchronous Machine Rotor Design for the Easy Assembly of Excitation Coils Based on Surrogate Optimization. Energies. 2018; 11 (5):1311.
Chicago/Turabian StyleTiejiang Yuan; Nan Yang; Wei Zhang; Wenping Cao; Ning Xing; Zheng Tan; Guofeng Li. 2018. "Improved Synchronous Machine Rotor Design for the Easy Assembly of Excitation Coils Based on Surrogate Optimization." Energies 11, no. 5: 1311.
This paper introduces a new rotor design for the easy insertion and removal of the rotor windings. The shape of the rotor is optimized based on surrogate method in order to achieve the lowest power loss under the maximum power output. The performance of the new rotor is examined in 2-D finite element software and validated by experiments. This rotor shows good potentials for reducing the maintenance and repair costs of synchronous machines, making it suitable for manufacturers within the mass production markets such as gen-sets, steam turbines, wind power generators and hybrid electric vehicles.
Tiejiang Yuan; Nan Yang; Wenping Cao; Zheng Tan; Guofeng Li; Xueguan Song. New Synchronous Machine Rotor Design for Easy Insertion of Excitation Coils Based on Surrogate optimization. 2018, 1 .
AMA StyleTiejiang Yuan, Nan Yang, Wenping Cao, Zheng Tan, Guofeng Li, Xueguan Song. New Synchronous Machine Rotor Design for Easy Insertion of Excitation Coils Based on Surrogate optimization. . 2018; ():1.
Chicago/Turabian StyleTiejiang Yuan; Nan Yang; Wenping Cao; Zheng Tan; Guofeng Li; Xueguan Song. 2018. "New Synchronous Machine Rotor Design for Easy Insertion of Excitation Coils Based on Surrogate optimization." , no. : 1.
Typically, a geared drive system is used to connect an induction motor of 1500 rpm with a Raymond Pulverizer of 105 rpm in mining applications. This system suffers from low efficiency and a heavy motor drive. This paper proposes a novel design of a 75 kW, 72/48 switched reluctance motor (SRM) for a low-speed direct-drive as for mining applications. The paper is focused on the design and comparative evaluation of the proposed machine in order to replace a geared drive system whilst providing a high torque low-speed and direct-drive solution. The machine performance is studied and the switching angle configuration of the machine is also optimised. The efficiency of the whole drive system is found to be as high as 90.19%, whereas the geared induction motor drive provides only an efficiency of 59.32% under similar operating conditions. An SRM prototype was built and experimentally tested. Simulation and experimental results show that the drive system has better performance to substitute the induction motor option in mining applications.
Esmail Elhomdy; Guofeng Li; Jiang Liu; Syed Abid Bukhari; Wen-Ping Cao. Design and Experimental Verification of a 72/48 Switched Reluctance Motor for Low-Speed Direct-Drive Mining Applications. Energies 2018, 11, 192 .
AMA StyleEsmail Elhomdy, Guofeng Li, Jiang Liu, Syed Abid Bukhari, Wen-Ping Cao. Design and Experimental Verification of a 72/48 Switched Reluctance Motor for Low-Speed Direct-Drive Mining Applications. Energies. 2018; 11 (1):192.
Chicago/Turabian StyleEsmail Elhomdy; Guofeng Li; Jiang Liu; Syed Abid Bukhari; Wen-Ping Cao. 2018. "Design and Experimental Verification of a 72/48 Switched Reluctance Motor for Low-Speed Direct-Drive Mining Applications." Energies 11, no. 1: 192.