FPGA HiL Simulation of Back-to-Back Converter PMSG Wind Turbine Systems

Zhenbin Zhang,Fengxiang Wang,Metehan Acikgoz,Xinbo Cai,Ralph Kennel 전력전자학회 2015 ICPE(ISPE)논문집 Vol.2015 No.6

This work presents a back-to-back power converter Permanent-magnet Synchronous Generator (PMSG) wind turbine real-time simulation system based on FPGA Hardware-in-the-Loop (HiL) concept. The proposed system can be used for Electric Control Units (ECUs) evaluation in a real-time manner. The refreshing rate effect of a HiL emulator is discussed and analyzed with real-time simulation proof. Within this work, perunit system model of a 2MW Back-to-Back converter direct-drive PMSG wind turbine system is developed both in continuous and discrete formats. The effectiveness of the discrete per-unit system model is firstly verified through off-line simulation in Matlab/Simulink environment. Then by using a so-called Single-Cycle-Timed- Loop (SCTL) technique, the per-unit discrete system model is implemented on an FPGA based platform as the emulator, achieving a very high refreshing-rate (up to 250 kHz, i.e., 4μs). The emulator refreshing rate effect is evaluated in a real-time operating manner by implementing an ECU (using FOC/VOC control scheme) in a second FPGA chassis. At the end of this paper the results are analyzed and theoretically interpreted.

Saliency Detection based on Global Color Distribution and Active Contour Analysis

( Zhengping Hu ),( Zhenbin Zhang ),( Zhe Sun ),( Shuhuan Zhao ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.12

In computer vision, salient object is important to extract the useful information of foreground. With active contour analysis acting as the core in this paper, we propose a bottom-up saliency detection algorithm combining with the Bayesian model and the global color distribution. Under the supports of active contour model, a more accurate foreground can be obtained as a foundation for the Bayesian model and the global color distribution. Furthermore, we establish a contour-based selection mechanism to optimize the global-color distribution, which is an effective revising approach for the Bayesian model as well. To obtain an excellent object contour, we firstly intensify the object region in the source gray-scale image by a seed-based method. The final saliency map can be detected after weighting the color distribution to the Bayesian saliency map, after both of the two components are available. The contribution of this paper is that, comparing the Harris-based convex hull algorithm, the active contour can extract a more accurate and non-convex foreground. Moreover, the global color distribution can solve the saliency-scattered drawback of Bayesian model, by the mutual complementation. According to the detected results, the final saliency maps generated with considering the global color distribution and active contour are much-improved.

Current Balancing Control of High Power Parallel-Connected AFE with Small Current Ripples

Xinbo Cai,Zhenbin Zhang,Liang Cai,Ralph Kennel 전력전자학회 2015 ICPE(ISPE)논문집 Vol.2015 No.6

Parallel connected Active Front End (AFE) voltage converters are widely employed in high-power grid interfaces for renewable energy applications. With parallel connected two level power converters, the power rating of the system can be easily increased with higher stability and less control efforts than multilevel power converter based solutions. However, due to unmatched modules, unbalanced currents inevitably happen to different modules of the same phase, which seriously impacts the efficiency and performances of the converter. This paper presents an improved current balancing control methods for a high power (600 kVA) parallel connected AFE for wind energy applications. The proposed current balancing method is integrated within a well-known Voltage Oriented Control Scheme with few implementation efforts. The simulation results show that with the proposed strategy the current ripples of each parallelconnected converter leg decrease evidently and the THDs are therefore decreased. Moreover, the effectiveness of the proposed method is also confirmed by experimental results with a grid-code compatible testing set-up.

Saliency Detection based on Global Color Distribution and Active Contour Analysis

Hu, Zhengping,Zhang, Zhenbin,Sun, Zhe,Zhao, Shuhuan Korean Society for Internet Information 2016 KSII Transactions on Internet and Information Syst Vol.10 No.12

In computer vision, salient object is important to extract the useful information of foreground. With active contour analysis acting as the core in this paper, we propose a bottom-up saliency detection algorithm combining with the Bayesian model and the global color distribution. Under the supports of active contour model, a more accurate foreground can be obtained as a foundation for the Bayesian model and the global color distribution. Furthermore, we establish a contour-based selection mechanism to optimize the global-color distribution, which is an effective revising approach for the Bayesian model as well. To obtain an excellent object contour, we firstly intensify the object region in the source gray-scale image by a seed-based method. The final saliency map can be detected after weighting the color distribution to the Bayesian saliency map, after both of the two components are available. The contribution of this paper is that, comparing the Harris-based convex hull algorithm, the active contour can extract a more accurate and non-convex foreground. Moreover, the global color distribution can solve the saliency-scattered drawback of Bayesian model, by the mutual complementation. According to the detected results, the final saliency maps generated with considering the global color distribution and active contour are much-improved.

Compressive Sensing Based Predictive Current Control of Electrical Drives

Ferdinand Grimm,Zhenbin Zhang 전력전자학회 2019 ICPE(ISPE)논문집 Vol.2019 No.5

We propose a novel control approach for two level inverter drives that is based on a reformulation of the model predictive current control framework. Using inverse modeling we define a sparse optimization problem to achieve the control goal. In order to reduce the computational effort the optimization problem is relaxed to an l1-minimization. A structured iterative hard thresholding algorithm is proposed to solve the `1-minimization. Furthermore, an outlook to multistep model predictive control is given. The results are then verified by means of simulations.

Model Predictive Control with Extrapolation Strategy for the Arm Current Commutation Control of Modular Multilevel Converter Operating in Quasi Two-Level Mode

Xiaonan Gao,Wei Tian,Zhenbin Zhang,Ralph Kennel 전력전자학회 2019 ICPE(ISPE)논문집 Vol.2019 No.5

Modular multilevel converters (MMCs) have become an attractive topology for medium-voltage motor drive applications due to their modular construction and voltage scalability. The main drawback of using MMC to drive motors is the large voltage variation of submodule (SM) capacitors when the motor is running at a low speed with a large load torque condition. Operating in the quasi two-level mode of MMCs is an effective solution for such a problem. The arm current commutation control is the most important part for the operation of MMCs in the quasi two-level mode. This paper proposes a new current commutation method. The general idea is to use the delay control method to realize the arm current commutation. Therefore, the key point is to decide when to delay the operation of a submodule at each step during the commutation process. In this paper, the model predictive control (MPC) method with extrapolation technique has been used to determine whether to delay the SM at the present moment. Therefore, the waveforms of the two arms in the same phase may not be completely complementary throughout the whole commutation period. Due to such non-complementary PWMs, the currents between two arms can be exchanged.

Predictive Control for Power Converters and Drives: Concept, Applications, and Trends

Ralph M. Kennel,Jose Rodriguez,Zhenbin Zhang 전력전자학회 2019 ICPE(ISPE)논문집 Vol.2019 No.5