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Wen-Ping Cao
School of Engineering & Automation, Anhui University, Hefei, P. R. China

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Article
Published: 16 June 2021 in International Journal of Control, Automation and Systems
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Electric vehicles are a key technology to decarbonize the transport sector where interior permanent magnet synchronous motors (IPMSMs) are the best performer at the heart of the electrical drive system. In order to optimize their operational efficiency, the model-based method associated with parameter identification is widely adopted. However, efficiency optimization and parameter identification in the existing methods are implemented independently by different strategies in a sequential execution manner, which does not produce an optimized systemlevel solution. In this paper, the two methods are combined to deal with a constrained optimization problem in an IPMSM drive. Firstly, the problem is converted into a variational problem based on the variational principle and projection dynamic theory. Then, a unified projection dynamic equation (UPDE) is used to estimate the parameters and determine the solution of optimal current (OC) of the IPMSM. Further, a recursive neural network (RNN) corresponding to the UPDE is developed to implement the developed fast efficiency optimization of the IPMSM drive. The results of simulation experiments show the proposed method is effective to identify motor parameters and determine the OC of the drive system rapidly and accurately. Thus, it can rapidly realize efficiency optimization of an IPMSM drive-system. Because the designed RNN can be easily implemented in the hardware, such as a field-programmable gate array (FPGA) or dedicated neural network chip, the method can achieve instantaneous efficiency optimization of the IPMSM drive system and therefore improve the widespread application of IPMSMs in EVs.

ACS Style

Qin-Mu Wu; Yu Zhan; Mei Zhang; Xiang-Ping Chen; Wen-Ping Cao. Efficiency Optimization Control of an IPMSM Drive System for Electric Vehicles (EVs). International Journal of Control, Automation and Systems 2021, 19, 2716 -2733.

AMA Style

Qin-Mu Wu, Yu Zhan, Mei Zhang, Xiang-Ping Chen, Wen-Ping Cao. Efficiency Optimization Control of an IPMSM Drive System for Electric Vehicles (EVs). International Journal of Control, Automation and Systems. 2021; 19 (8):2716-2733.

Chicago/Turabian Style

Qin-Mu Wu; Yu Zhan; Mei Zhang; Xiang-Ping Chen; Wen-Ping Cao. 2021. "Efficiency Optimization Control of an IPMSM Drive System for Electric Vehicles (EVs)." International Journal of Control, Automation and Systems 19, no. 8: 2716-2733.

Journal article
Published: 08 June 2021 in IEEE Transactions on Vehicular Technology
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An active balancing method based on two flyback converters is proposed for series-connected battery pack. Balanced energy can be transferred between the whole battery and any single cell. The proposed topology reduces the number of energy storage components, the volume and the cost of the balancing system. And it has the characteristics of fast balancing speed and high balancing efficiency. Based on the topology, a dual-objective hybrid control strategy is proposed, which can reduce the highest voltage and boost the lowest voltage in the charging or discharging process of the cells simultaneously, so as to improve the balancing speed. Simulation and experimental results show that the proposed method has a good balancing effect and can significantly improve the consistency of series battery pack. This work is potentially significant in terms of improved reliability of battery packs and savings of costs and lives in safety-critical applications.

ACS Style

Xiangwei Guo; Jiahao Geng; Zhen Liu; Xiaozhuo Xu; Wenping Cao. A Flyback Converter-Based Hybrid Balancing Method for Series-Connected Battery Pack in Electric Vehicles. IEEE Transactions on Vehicular Technology 2021, 70, 6626 -6635.

AMA Style

Xiangwei Guo, Jiahao Geng, Zhen Liu, Xiaozhuo Xu, Wenping Cao. A Flyback Converter-Based Hybrid Balancing Method for Series-Connected Battery Pack in Electric Vehicles. IEEE Transactions on Vehicular Technology. 2021; 70 (7):6626-6635.

Chicago/Turabian Style

Xiangwei Guo; Jiahao Geng; Zhen Liu; Xiaozhuo Xu; Wenping Cao. 2021. "A Flyback Converter-Based Hybrid Balancing Method for Series-Connected Battery Pack in Electric Vehicles." IEEE Transactions on Vehicular Technology 70, no. 7: 6626-6635.

Journal article
Published: 12 May 2021 in IEEE Transactions on Energy Conversion
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Angle of rotation is a key parameter in motor fault diagnosis under varying speed conditions, and is usually measured by an optical encoder. However, the use of encoders is intrusive and in many scenarios its signal is difficult to access due to technical or commercial reasons. In this study, a novel rotation angle measurement method based on stray flux analysis is proposed and applied to bearing fault diagnosis of brushless direct-current (BLDC) motors. The measurement accuracy of the proposed method is comparable to that from an encoder. The developed method is flexible, noninvasive, and nondestructive. It is easy to implement and eliminates the need for long cables and access of the motor control system. The proposed method can be extended to the diagnosis of motor electrical and drive faults. If implemented with an Internet of Things (IoT) or a hand-held device, it can further improve the reliability of sensorless motor drive systems in industrial automation so as to meet Industry 4.0 requirements.

ACS Style

Xiaoxian Wang; Siliang Lu; Wenping Cao; Min Xia; Kang Chen; Jianming Ding; Shiwu Zhang. Stray Flux-Based Rotation Angle Measurement for Bearing Fault Diagnosis in Variable-Speed BLDC Motors. IEEE Transactions on Energy Conversion 2021, PP, 1 -1.

AMA Style

Xiaoxian Wang, Siliang Lu, Wenping Cao, Min Xia, Kang Chen, Jianming Ding, Shiwu Zhang. Stray Flux-Based Rotation Angle Measurement for Bearing Fault Diagnosis in Variable-Speed BLDC Motors. IEEE Transactions on Energy Conversion. 2021; PP (99):1-1.

Chicago/Turabian Style

Xiaoxian Wang; Siliang Lu; Wenping Cao; Min Xia; Kang Chen; Jianming Ding; Shiwu Zhang. 2021. "Stray Flux-Based Rotation Angle Measurement for Bearing Fault Diagnosis in Variable-Speed BLDC Motors." IEEE Transactions on Energy Conversion PP, no. 99: 1-1.

Journal article
Published: 16 April 2021 in IEEE Access
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This paper presents a comprehensive study on the fractional slot concentrated winding (FSCW) interior permanent magnet synchronous motors (IPMSMs) with different pole slot combination for the application of electric vehicle (EV) and hybrid electric vehicle (HEV). Three motors with the same dimension constraint and rated parameters are designed and optimized. With the optimized motors, winding factors and magnetomotive force (MMF), inductances, torque capacity, constant power speed range (CPSR), the losses, the efficiency, demagnetization capability and vibration are investigated and compared. The comparison results show that 12/8 motor and 12/10 motor have their respective unique advantages while 12/14 motor is not as good as the others.

ACS Style

Chenxi Zhou; Xiaoyan Huang; Zhaokai Li; Wenping Cao. Design Consideration of Fractional Slot Concentrated Winding Interior Permanent Magnet Synchronous Motor for EV and HEV Applications. IEEE Access 2021, 9, 64116 -64126.

AMA Style

Chenxi Zhou, Xiaoyan Huang, Zhaokai Li, Wenping Cao. Design Consideration of Fractional Slot Concentrated Winding Interior Permanent Magnet Synchronous Motor for EV and HEV Applications. IEEE Access. 2021; 9 ():64116-64126.

Chicago/Turabian Style

Chenxi Zhou; Xiaoyan Huang; Zhaokai Li; Wenping Cao. 2021. "Design Consideration of Fractional Slot Concentrated Winding Interior Permanent Magnet Synchronous Motor for EV and HEV Applications." IEEE Access 9, no. : 64116-64126.

Journal article
Published: 28 December 2020 in Inventions
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Wind energy conversion systems have become a key technology to harvest wind energy worldwide. In permanent magnet synchronous generator-based wind turbine systems, the rotor position is needed for variable speed control and it uses an encoder or a speed sensor. However, these sensors lead to some obstacles, such as additional weight and cost, increased noise, complexity and reliability issues. For these reasons, the development of new sensorless control methods has become critically important for wind turbine generators. This paper aims to develop a new sensorless and adaptive control method for a surface-mounted permanent magnet synchronous generator. The proposed method includes a new model reference adaptive system, which is used to estimate the rotor position and speed as an observer. Adaptive control is implemented in the pulse-width modulated current source converter. In the conventional model reference adaptive system, the proportional-integral controller is used in the adaptation mechanism. Moreover, the proportional-integral controller is generally tuned by the trial and error method, which is tedious and inaccurate. In contrast, the proposed method is based on model predictive control which eliminates the use of speed and position sensors and also improves the performance of model reference adaptive control systems. In this paper, the proposed predictive controller is modelled in MATLAB/SIMULINK and validated experimentally on a 6-kW wind turbine generator. Test results prove the effectiveness of the control strategy in terms of energy efficiency and dynamical adaptation to the wind turbine operational conditions. The experimental results also show that the control method has good dynamic response to parameter variations and external disturbances. Therefore, the developed technique will help increase the uptake of permanent magnet synchronous generators and model predictive control methods in the wind power industry.

ACS Style

Wenping Cao; Ning Xing; Yan Wen; Xiangping Chen; Dong Wang. New Adaptive Control Strategy for a Wind Turbine Permanent Magnet Synchronous Generator (PMSG). Inventions 2020, 6, 3 .

AMA Style

Wenping Cao, Ning Xing, Yan Wen, Xiangping Chen, Dong Wang. New Adaptive Control Strategy for a Wind Turbine Permanent Magnet Synchronous Generator (PMSG). Inventions. 2020; 6 (1):3.

Chicago/Turabian Style

Wenping Cao; Ning Xing; Yan Wen; Xiangping Chen; Dong Wang. 2020. "New Adaptive Control Strategy for a Wind Turbine Permanent Magnet Synchronous Generator (PMSG)." Inventions 6, no. 1: 3.

Journal article
Published: 25 August 2020 in IEEE Transactions on Industrial Electronics
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Permanent magnet spherical motors (PMSMs) operate on the principle of the DC excitation of stator coils and three freedom of motion in the rotor. Each coil generates the torque in a specific direction, collectively they move the rotor to a direction of motion. Modeling and analysis of the output torque are of critical importance for in precise position control applications. The control of these motors requires precise output torques by all coils at a specific rotor position. It is difficult to achieve in the three-dimension space. This paper is the first to apply the Gaussian process to establish the relationship of the rotor position and the output torque for PMSMs. Traditional methods are difficult to resolve such a complex 3D problem with a reasonable computational accuracy and time. This paper utilizes a data-driven method using only input and output data validated by experiments. The multi-task Gaussian process (MTGP) is developed to calculate the total torque produced by multiple coils at the full operational range. The training data and test data are obtained by the finite element method. The effectiveness of the proposed method is validated and compared with existing data-driven approaches. The results exhibit superior performance of accuracy.

ACS Style

Yan Wen; Guoli Li; Qunjing Wang; Xiwen Guo; Wenping Cao. Modeling and Analysis of Permanent Magnet Spherical Motors by a Multitask Gaussian Process Method and Finite Element Method for Output Torque. IEEE Transactions on Industrial Electronics 2020, 68, 8540 -8549.

AMA Style

Yan Wen, Guoli Li, Qunjing Wang, Xiwen Guo, Wenping Cao. Modeling and Analysis of Permanent Magnet Spherical Motors by a Multitask Gaussian Process Method and Finite Element Method for Output Torque. IEEE Transactions on Industrial Electronics. 2020; 68 (9):8540-8549.

Chicago/Turabian Style

Yan Wen; Guoli Li; Qunjing Wang; Xiwen Guo; Wenping Cao. 2020. "Modeling and Analysis of Permanent Magnet Spherical Motors by a Multitask Gaussian Process Method and Finite Element Method for Output Torque." IEEE Transactions on Industrial Electronics 68, no. 9: 8540-8549.

Journal article
Published: 25 August 2020 in IEEE Transactions on Vehicular Technology
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A series of integrated equalizers based on joint buck-boost (BB) and switched-capacitor (SC) converters are proposed for balancing the voltages of series-connected battery packs. All these equalizers realize the any-cells-to-any-cells (AC2AC) equalization mode without increasing any MOSFETs and drivers. Corresponding operational principles are analyzed and the expressions of balancing currents are verified by experimental waveforms. According to the comparative balancing experiments for four and six series-connected Li-ion cells, one proposed CBB-PCSC equalizer, which achieves the dual AC2AC balancing modes through the integration of both coupled buck-boost (CBB) and parallel-connected switched-capacitor (PCSC) converters, leads to the highest balancing speed and efficiency. Moreover, compared with several conventional equalizers, this CBB-PCSC topology also has the compact size and low cost, making it become a well-performing integrated topology for automotive battery voltages equalization.

ACS Style

Kailong Liu; Zhile Yang; Xiaopeng Tang; Wenping Cao. Automotive Battery Equalizers Based on Joint Switched-Capacitor and Buck-Boost Converters. IEEE Transactions on Vehicular Technology 2020, 69, 12716 -12724.

AMA Style

Kailong Liu, Zhile Yang, Xiaopeng Tang, Wenping Cao. Automotive Battery Equalizers Based on Joint Switched-Capacitor and Buck-Boost Converters. IEEE Transactions on Vehicular Technology. 2020; 69 (11):12716-12724.

Chicago/Turabian Style

Kailong Liu; Zhile Yang; Xiaopeng Tang; Wenping Cao. 2020. "Automotive Battery Equalizers Based on Joint Switched-Capacitor and Buck-Boost Converters." IEEE Transactions on Vehicular Technology 69, no. 11: 12716-12724.

Journal article
Published: 14 May 2020 in IEEE Access
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Integrating renewable energy into power grids is seen in increase in recent years since these energy sources are sustainable and clean. However, the integration brings about considerable technical challenges associated with fluctuations and uncertainties of the energy availability whilst maintaining the stability of smart grids. The prediction of renewable energy generation is key to achieve optimal power dispatch in renewable-intensive smart grids. However, uncertain interruption and prediction errors will make an optimal decision more challenging. Model predictive control (MPC) is an effective way to overcome the discrepancies between the prediction and the real-world system through a closed-loop correction over iteration process. This study develops an improved MPC scheme used with a hybrid energy storage system for optimal power dispatch in a smart grid. This hybrid renewable energy system consists of a wind farm, a hydrogen/oxygen storage system and several fuel cells (FCs). In this study, particle swarm optimization (PSO) with a back propagation (BP) artificial neural network is developed to predict the wind energy availability by using measured data. Then, a genetic algorithm (GA) is combined with a state space model (SSM) to achieve the MPC control. A dataset of 24-hour ahead predictive generation is calibrated from the measured data and is defined for optimal power flow between the grid, the wind farm and the storage subsystem so as to balance the supply and load. The optimization target is to achieve a minimal energy exchange between the power grid and the hybrid renewable energy storage system. Based on actual measured data, the test results have shown that the proposed methodology can maximize the local usage of wind power whilst minimizing the power exchange with the grid. An optimal power dispatch strategy is proved to be effective to meet the demand and efficiency with dynamic control of the FCs. The usage of the intermittent wind power is increased from 45% to 90% in the four test studies. Therefore, this work can minimize the impact of fluctuating renewable energy on the power grid and enhance uptakes of FC-based energy systems. This is particularly economic and relevant to the remote and under-developed regions where their power networks are weak and vulnerable.

ACS Style

Xiangping Chen; Wenping Cao; Qilong Zhang; Shubo Hu; Jing Zhang. Artificial Intelligence-Aided Model Predictive Control for a Grid-Tied Wind-Hydrogen-Fuel Cell System. IEEE Access 2020, 8, 92418 -92430.

AMA Style

Xiangping Chen, Wenping Cao, Qilong Zhang, Shubo Hu, Jing Zhang. Artificial Intelligence-Aided Model Predictive Control for a Grid-Tied Wind-Hydrogen-Fuel Cell System. IEEE Access. 2020; 8 (99):92418-92430.

Chicago/Turabian Style

Xiangping Chen; Wenping Cao; Qilong Zhang; Shubo Hu; Jing Zhang. 2020. "Artificial Intelligence-Aided Model Predictive Control for a Grid-Tied Wind-Hydrogen-Fuel Cell System." IEEE Access 8, no. 99: 92418-92430.

Journal article
Published: 11 April 2020 in Energies
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This paper proposes an innovative approach for controlling pollutant release in a long-distance tunnel via longitudinal ventilation. Enhanced by an active disturbance rejection control (ADRC) method, a ventilation controller is developed to regulate the forced air ventilation in a road tunnel. As a result, the pollutants (particulate matter and carbon monoxide) are reduced by actively regulating the air flow rate through the tunnel. The key contribution of this study lies in the development of an extended state observer that can track the system disturbance and provide the system with compensation via a nonlinear state feedback controller equipped by the ADRC. The proposed method enhances the disturbance attenuation capability in the ventilation system and keeps the pollutant concentration within the legitimate limit in the tunnel. In addition to providing a safe and clean environment for passengers, the improved tunnel ventilation can also achieve better energy saving as the air flow rate is optimized.

ACS Style

Liyun Si; Wenping Cao; Xiangping Chen. Active Disturbance Rejection Control of a Longitudinal Tunnel Ventilation System. Energies 2020, 13, 1871 .

AMA Style

Liyun Si, Wenping Cao, Xiangping Chen. Active Disturbance Rejection Control of a Longitudinal Tunnel Ventilation System. Energies. 2020; 13 (8):1871.

Chicago/Turabian Style

Liyun Si; Wenping Cao; Xiangping Chen. 2020. "Active Disturbance Rejection Control of a Longitudinal Tunnel Ventilation System." Energies 13, no. 8: 1871.

Journal article
Published: 28 March 2020 in Energies
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In a traditional lumped-parameter thermal network, no distinction is made between the heat and non-heat sources, resulting in both larger heat flux and temperature drop in the uniform heat source. In this paper, an improved lumped-parameter thermal network is proposed to deal with such problems. The innovative aspect of this proposed method is that it considers the influence of heat flux change in the heat source, and then gives a half-resistance theory for the heat source to achieve the temperature drop balance. In addition, the coupling relationship between the boundary temperature and loading position of the heat generator is also added in the lumped-parameter thermal network, so as to amend the loading position and nodes’ temperature through iterations. This approach breaks the limitation of the traditional lumped-parameter thermal network: that the heat generator can only be loaded at the midpoint, which is critical to determining the maximum temperature in asymmetric heat dissipation. By adjusting the location of heat generator and thermal resistances of each branch, the accuracy of temperature prediction is further improved. A simulation and an experiment on a U-core motor show that the improved lumped-parameter thermal network not only achieves higher accuracy than the traditional one, but also determines the loading position of the heat generator well.

ACS Style

Bin Li; Liang Yan; Wenping Cao; Christopher Gerada; Suokui Chang. An Improved LPTN Method for Determining the Maximum Winding Temperature of a U-Core Motor. Energies 2020, 13, 1566 .

AMA Style

Bin Li, Liang Yan, Wenping Cao, Christopher Gerada, Suokui Chang. An Improved LPTN Method for Determining the Maximum Winding Temperature of a U-Core Motor. Energies. 2020; 13 (7):1566.

Chicago/Turabian Style

Bin Li; Liang Yan; Wenping Cao; Christopher Gerada; Suokui Chang. 2020. "An Improved LPTN Method for Determining the Maximum Winding Temperature of a U-Core Motor." Energies 13, no. 7: 1566.

Journal article
Published: 30 October 2019 in IEEE Transactions on Industrial Electronics
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ACS Style

Lassi Aarniovuori; Hannu Karkkainen; Alecksey Anuchin; Juha J. Pyrhonen; Pia Lindh; Wenping Cao. Voltage-Source Converter Energy Efficiency Classification in Accordance With IEC 61800-9-2. IEEE Transactions on Industrial Electronics 2019, 67, 8242 -8251.

AMA Style

Lassi Aarniovuori, Hannu Karkkainen, Alecksey Anuchin, Juha J. Pyrhonen, Pia Lindh, Wenping Cao. Voltage-Source Converter Energy Efficiency Classification in Accordance With IEC 61800-9-2. IEEE Transactions on Industrial Electronics. 2019; 67 (10):8242-8251.

Chicago/Turabian Style

Lassi Aarniovuori; Hannu Karkkainen; Alecksey Anuchin; Juha J. Pyrhonen; Pia Lindh; Wenping Cao. 2019. "Voltage-Source Converter Energy Efficiency Classification in Accordance With IEC 61800-9-2." IEEE Transactions on Industrial Electronics 67, no. 10: 8242-8251.

Journal article
Published: 05 September 2019 in Applied Sciences
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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.

ACS Style

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 Style

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 (18):3681.

Chicago/Turabian Style

Kewei 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.

Journal article
Published: 30 August 2019 in Applied Sciences
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Utilization of renewable energy (e.g., wind, solar, bio-energy) is high on international and governmental agendas. In order to address energy poverty and increase energy efficiency for rural villages, a hybrid distribution generation (DG) system including wind, hydrogen and fuel cells is proposed to supplement to the main grid. Wind energy is first converted into electrical energy while part of the generated electricity is used for water electrolysis to generate hydrogen for energy storage. Hydrogen is used by fuel cells to convert back to electricity when electrical energy demand peaks. An analytical model has been developed to coordinate the operation of the system involving energy conversion between mechanical, electrical and chemical forms. The proposed system is primarily designed to meet the electrical demand of a rural village in the UK where the energy storage system can balance out the discrepancy between intermittent renewable energy supplies and fluctuating energy demands so as to improve the system efficiency. Genetic Algorithm (GA) is used as an optimization strategy to determine the operational scheme for the multi-vector energy system. In the work, four case studies are carried out based on real-world measurement data. The novelty of this study lies in the GA-based optimization and operational methods for maximized wind energy utilization. This provides an alternative to battery energy storage and can be widely applied to wind-rich rural areas.

ACS Style

Xiangping Chen; Wenping Cao; Lei Xing. GA Optimization Method for a Multi-Vector Energy System Incorporating Wind, Hydrogen, and Fuel Cells for Rural Village Applications. Applied Sciences 2019, 9, 3554 .

AMA Style

Xiangping Chen, Wenping Cao, Lei Xing. GA Optimization Method for a Multi-Vector Energy System Incorporating Wind, Hydrogen, and Fuel Cells for Rural Village Applications. Applied Sciences. 2019; 9 (17):3554.

Chicago/Turabian Style

Xiangping Chen; Wenping Cao; Lei Xing. 2019. "GA Optimization Method for a Multi-Vector Energy System Incorporating Wind, Hydrogen, and Fuel Cells for Rural Village Applications." Applied Sciences 9, no. 17: 3554.

Journal article
Published: 23 August 2019 in IEEE Access
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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.

ACS Style

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 Style

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 (99):119099-119109.

Chicago/Turabian Style

Kewei 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.

Journal article
Published: 09 May 2019 in Energies
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The efficiency of a photovoltaic (PV) system strongly depends on the transformation process from solar energy to electricity, where maximum power point tracking (MPPT) is widely regarded as a promising technology to harvest solar energy in the first step. Furthermore, inverters are an essential part of solar power generation systems. Their performance dictates the power yield, system costs and reliable operation. This paper proposes a novel control technology combining discontinuous pulse width modulation (DPWM) and overmodulation technology to better utilize direct current (DC) electrical power and to reduce the switching losses in the electronic power devices in conversion. In order to optimize the performance of the PV inverter, the overmodulation region is refined from conventional two-level space vector pulse width modulation (SVPWM) control technology. Then, the turn-on and turn-off times of the switching devices in different modulation areas are deduced analytically. A new DPWM algorithm is proposed to achieve the full region control. An experimental platform based on a digital signal processing (DSP) controller is developed for validation purposes, after maximum power is achieved via a DC/DC converter under MPPT operation. Experimental results on a PV system show that the DPWM control algorithm lowers the harmonic distortion of the output voltage and current, as well as the switching losses. Moreover, better utilization of the DC-link voltage also improves the PV inverter performance. The developed algorithm may also be applied to other applications utilizing grid-tie power inverters.

ACS Style

Lan Li; Hao Wang; Xiangping Chen; Abid Ali Shah Bukhari; Wenping Cao; Lun Chai; Bing Li. High Efficiency Solar Power Generation with Improved Discontinuous Pulse Width Modulation (DPWM) Overmodulation Algorithms. Energies 2019, 12, 1765 .

AMA Style

Lan Li, Hao Wang, Xiangping Chen, Abid Ali Shah Bukhari, Wenping Cao, Lun Chai, Bing Li. High Efficiency Solar Power Generation with Improved Discontinuous Pulse Width Modulation (DPWM) Overmodulation Algorithms. Energies. 2019; 12 (9):1765.

Chicago/Turabian Style

Lan Li; Hao Wang; Xiangping Chen; Abid Ali Shah Bukhari; Wenping Cao; Lun Chai; Bing Li. 2019. "High Efficiency Solar Power Generation with Improved Discontinuous Pulse Width Modulation (DPWM) Overmodulation Algorithms." Energies 12, no. 9: 1765.

Journal article
Published: 15 February 2019 in Energies
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This paper presents an accelerated particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm to track global maximum power point (MPP) of photovoltaic (PV) generation under partial shading conditions. Conventional PSO-based MPPT algorithms have common weaknesses of a long convergence time to reach the global MPP and oscillations during the searching. The proposed algorithm includes a standard PSO and a perturb-and-observe algorithm as the accelerator. It has been experimentally tested and compared with conventional MPPT algorithms. Experimental results show that the proposed MPPT method is effective in terms of high reliability, fast dynamic response, and high accuracy in tracking the global MPP.

ACS Style

Muhannad Alshareef; Zhengyu Lin; Mingyao Ma; Wenping Cao. Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions. Energies 2019, 12, 623 .

AMA Style

Muhannad Alshareef, Zhengyu Lin, Mingyao Ma, Wenping Cao. Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions. Energies. 2019; 12 (4):623.

Chicago/Turabian Style

Muhannad Alshareef; Zhengyu Lin; Mingyao Ma; Wenping Cao. 2019. "Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions." Energies 12, no. 4: 623.

Journal article
Published: 19 December 2018 in IEEE Transactions on Vehicular Technology
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ACS Style

Guanhao Du; Wenping Cao; Shubo Hu; Zhengyu Lin; Tiejiang Yuan. Design and Assessment of an Electric Vehicle Powertrain Model Based on Real-World Driving and Charging Cycles. IEEE Transactions on Vehicular Technology 2018, 68, 1178 -1187.

AMA Style

Guanhao Du, Wenping Cao, Shubo Hu, Zhengyu Lin, Tiejiang Yuan. Design and Assessment of an Electric Vehicle Powertrain Model Based on Real-World Driving and Charging Cycles. IEEE Transactions on Vehicular Technology. 2018; 68 (2):1178-1187.

Chicago/Turabian Style

Guanhao Du; Wenping Cao; Shubo Hu; Zhengyu Lin; Tiejiang Yuan. 2018. "Design and Assessment of an Electric Vehicle Powertrain Model Based on Real-World Driving and Charging Cycles." IEEE Transactions on Vehicular Technology 68, no. 2: 1178-1187.

Journal article
Published: 05 November 2018 in Energies
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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.

ACS Style

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 Style

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 (11):3040.

Chicago/Turabian Style

Kewei 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.

Journal article
Published: 22 October 2018 in IEEE Transactions on Sustainable Energy
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This paper presents the conceptual analysis and comparative simulation and experimental evaluation of a novel power error comparison direct power control (PEC-DPC) strategy of the open-winding brushless doubly-fed reluctance generator (OW-BDFRG) for wind energy conversion systems (WECSs). As one of the promising candidates for limited speed range application of pump-alike and wind turbine with partially-rated converter. The emerging OW-BDFRG employed for the proposed PEC-DPC is fed via dual low-cost two-level converters, while the DPC concept is derived from the fundamental dynamic analyses between the calculated and controllable electrical power and flux of the BDFRG with two stators measurable voltage and current. Compared to the traditional two-level and three-level converter systems, the OW-BDFRG requires lower rated capacity of power devices and switching frequency converter, though have more flexible switching mode, higher reliability, redundancy and fault tolerance capability. The performance correctness and effectiveness of the proposed DPC strategy with the selected and optimised switching vector scheme are evaluated and confirmed through computer simulation studies and experimental measurements on a 25 kW generator test rig.

ACS Style

Liancheng Zhu; Fengge Zhang; Shi Jin; Sul Ademi; Xiaoying Su; Wenping Cao. Optimized Power Error Comparison Strategy for Direct Power Control of the Open-Winding Brushless Doubly Fed Wind Power Generator. IEEE Transactions on Sustainable Energy 2018, 10, 2005 -2014.

AMA Style

Liancheng Zhu, Fengge Zhang, Shi Jin, Sul Ademi, Xiaoying Su, Wenping Cao. Optimized Power Error Comparison Strategy for Direct Power Control of the Open-Winding Brushless Doubly Fed Wind Power Generator. IEEE Transactions on Sustainable Energy. 2018; 10 (4):2005-2014.

Chicago/Turabian Style

Liancheng Zhu; Fengge Zhang; Shi Jin; Sul Ademi; Xiaoying Su; Wenping Cao. 2018. "Optimized Power Error Comparison Strategy for Direct Power Control of the Open-Winding Brushless Doubly Fed Wind Power Generator." IEEE Transactions on Sustainable Energy 10, no. 4: 2005-2014.

Journal article
Published: 01 July 2018 in Journal of Renewable and Sustainable Energy
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ACS Style

Tiejiang Yuan; Xiaoshun Dong; Xiangping Chen; Wenping Cao; Juan Hu; Chuang Liu. Energetic macroscopic representation control method for a hybrid-source energy system including wind, hydrogen, and fuel cell. Journal of Renewable and Sustainable Energy 2018, 10, 043308 .

AMA Style

Tiejiang Yuan, Xiaoshun Dong, Xiangping Chen, Wenping Cao, Juan Hu, Chuang Liu. Energetic macroscopic representation control method for a hybrid-source energy system including wind, hydrogen, and fuel cell. Journal of Renewable and Sustainable Energy. 2018; 10 (4):043308.

Chicago/Turabian Style

Tiejiang Yuan; Xiaoshun Dong; Xiangping Chen; Wenping Cao; Juan Hu; Chuang Liu. 2018. "Energetic macroscopic representation control method for a hybrid-source energy system including wind, hydrogen, and fuel cell." Journal of Renewable and Sustainable Energy 10, no. 4: 043308.