Self-Reconfiguration Property of a Mixed Signal Controller for Improving Power Quality Compensation During Light Loading

Wong, Man-Chung; Yang, Yanzheng; Lam, Chi-Seng; Choi, Wai-Hei; Dai, Ningyi; Wu, Yajie; Wong, Chi-Yin; Sin, Sai-Weng; Chio, U-Fat; Seng-Pan, U; Martins, Rui P.

doi:10.1109/tpel.2014.2376983

Cited by 11 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to verify the influence of online inductance identification compensation algorithm on deadbeat control strategy based on frequency multiplication sampling, the Simulink simulation model of online inductance compensation algorithm is established. e influence of inductance error on the current harmonic distortion ATHD [37] in the deadbeat control algorithm is studied because the grid-connected LC filter is also the influencing factor of the grid current ATHD. erefore, the parameters of the LC filter remain unchanged in the simulation process, by modifying the initial value of the inductance of the deadbeat control model.…”

Section: Simulation and Analysis Of Online Inductance Identification mentioning

confidence: 99%

Research on Improved Deadbeat Control Strategy Based on Interpolation Prediction and Online Inductance Identification

Xie

Xue

et al. 2020

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

Aiming at the problem of control delay and inductance deviation, which exist in the traditional deadbeat control of the full-bridge circuit, an improved deadbeat control strategy was proposed. An improved Newton interpolation prediction algorithm was proposed to compensate the delay problem of deadbeat control, and an on-line inductance identification algorithm based on double frequency sampling was proposed to correct the inductance deviation. A mathematical model of deadbeat control for full-bridge inverter was established; besides, the performance of different interpolation prediction algorithms was analyzed. An online inductor identification model is established, on the basis of which the online inductance identification compensation formula is derived. It is indicated that an output constant current of 10 A is available with the deadbeat control relative error of only 0.2%, the grid-connected power factor up to 0.999, and the output current’s total harmonic distortion of only 2.37%. The prototype experiment shows that the output current’s total harmonic distortion is as low as 2.403% and the power factor is as high as 0.998. The results show that the improved deadbeat control strategy can effectively improve the control accuracy and the quality of grid-connected power.

show abstract

Section: Simulation and Analysis Of Online Inductance Identification mentioning

confidence: 99%

Research on Improved Deadbeat Control Strategy Based on Interpolation Prediction and Online Inductance Identification

Xie

Xue

et al. 2020

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

show abstract

“…The FPGA reconfiguration capability and its parallel processing power are "hot topics", recognised in many papers focused in industrial applications: hardware implemented polar decoders [1], FPGA embedded controller of an n-Level DC-DC-AC inverter [2] or hardware implementation of predictive control algorithms for power converters [3] With the newly emerged development environments for All Programmable Systems-on-Chip (SoCs) and multiprocessor Systems-on-Chip (MPSoCs) complex algorithms are now implemented in FPGA embedded processors [4]: FPGA/DSPbased digital controller with self-reconfiguration property for power quality compensation [5], FPGA embedded multiprocessor PLC that provides high execution speed, multiprocessing programming [6]. Despite ANNs being implemented in hardware for more than 25 years [7], it remains in the centre of attention for many researchers and a variety of methods to develop hardware implemented ANNs have been reported in the literature in the past decade [8,9].…”

Section: Introductionmentioning

confidence: 99%

An End-User Platform for FPGA-Based Design and Rapid Prototyping of Feedforward Artificial Neural Networks With On-Chip Backpropagation Learning

Tisan

Chin

2016

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

The hardware implementation of an Artificial Neural Network (ANN) using field-programmable gate arrays (FPGA) is a research field that has attracted much interest and attention. With the developments made, the programmer is now forced to face various challenges, such as the need to master various complex hardware-software development platforms, hardware description languages and advanced ANN knowledge. Moreover, such an implementation is very time consuming. To address these challenges, the paper presents a novel neural design methodology using a holistic modelling approach. Based on the end user programming concept, the presented solution empowers end users by means of abstracting the low-level hardware functionalities, streamlining the FPGA design process and supporting rapid ANN prototyping. A case study of an ANN as a pattern recognition module of an artificial olfaction system trained to identify four coffee brands is presented. The recognition rate versus training data features and data representation was analysed extensively.

show abstract

Adaptive DC-Link Voltage Control of Thyristor Controlled LC-Coupling Hybrid Active Power Filter (TCLC-HAPF)

2018

View full text Add to dashboard Cite

Self-Reconfiguration Property of a Mixed Signal Controller for Improving Power Quality Compensation During Light Loading

Cited by 11 publications

References 24 publications

Research on Improved Deadbeat Control Strategy Based on Interpolation Prediction and Online Inductance Identification

Research on Improved Deadbeat Control Strategy Based on Interpolation Prediction and Online Inductance Identification

An End-User Platform for FPGA-Based Design and Rapid Prototyping of Feedforward Artificial Neural Networks With On-Chip Backpropagation Learning

Adaptive DC-Link Voltage Control of Thyristor Controlled LC-Coupling Hybrid Active Power Filter (TCLC-HAPF)

Contact Info

Product

Resources

About