scholarly journals Investigation on GaN HEMTs Based Three-Phase STATCOM with Hybrid Control Scheme

Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 464
Author(s):  
Chao-Tsung Ma ◽  
Zhen-Huang Gu
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Distribution Systems ◽  
Reactive Power ◽  
Hybrid Control ◽  
Three Phase ◽  
Control Scheme ◽  
Gan Hemts ◽  
Hybrid Control Scheme ◽  
Switching Devices

The modern trend of decarbonization has encouraged intensive research on renewable energy (RE)-based distributed power generation (DG) and smart grid, where advanced electronic power interfaces are necessary for connecting the generator with power grids and various electrical systems. On the other hand, modern technologies such as Industry 4.0 and electrical vehicles (EV) have higher requirements for power converters than that of conventional applications. Consequently, the enhancement of power interfaces will play an important role in the future power generation and distribution systems as well as various industrial applications. It has been discovered that wide-bandgap (WBG) switching devices such as gallium nitride (GaN) high electron mobility transistors (HEMTs) and silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs) offer considerable potential for outperforming conventional silicon (Si) switching devices in terms of breakdown voltage, high temperature capability, switching speed, and conduction losses. This paper investigates the performance of a 2kVA three-phase static synchronous compensator (STATCOM) based on a GaN HEMTs-based voltage-source inverter (VSI) and a neural network-based hybrid control scheme. The proportional-integral (PI) controllers along with a radial basis function neural network (RBFNN) controller for fast reactive power control are designed in synchronous reference frame (SRF). Both simulation and hardware implementation are conducted. Results confirm that the proposed RBFNN assisted hybrid control scheme yields excellent dynamic performance in terms of various reactive power tracking control of the GaN HEMTs-based three-phase STATCOM system.

scholarly journals Hybrid Control Scheme for Three-Phase Multilevel Unidirectional Rectifier Under Unbalanced Input Voltages

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 29989-30001 ◽  
Author(s):  
Hong Cheng ◽  
Jiayi Kong ◽  
Penghui Wang ◽  
Cong Wang
Keyword(s):  
Hybrid Control ◽  
Three Phase ◽  
Control Scheme ◽  
Hybrid Control Scheme

P-CMAC-Based Control Method and its Application in Filament Tension Control

2010 ◽  
Vol 33 ◽  
pp. 101-104
Author(s):  
D.X. Chen ◽  
G. Wang
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Inference ◽  
Control Method ◽  
Hybrid Control ◽  
Tracking Error ◽  
Tension Control ◽  
Approximation Accuracy ◽  
Control Scheme ◽  
Hybrid Control Scheme

Cerebella Model Articulation Controller (CMAC) is considered as local association and generalization neural network. Parametric CMAC (P-CMAC) is a modification to the original CMAC. The introduction of continuous activation function applied in the input space can overcome the binary behavior of the original CMAC. Takagi-Sugeno(TS) type fuzzy inference is embedded in the internal mapping to improve the approximation accuracy. Hybrid control scheme, which combines P-CMAC neural network and traditional PID controller, is proposed in the paper. The output of P-CMAC network dominates the overall control signal applied to the plant, while the traditional PID controller serves as compensator for reducing tracking error. The application of hybrid control scheme to filament tension control is illustrated. The experimental results have shown the effectiveness and accuracy improvement of the control scheme.

A Hybrid Control Scheme for Three-Phase Vienna Rectifiers

2018 ◽  
Vol 33 (1) ◽  
pp. 629-640 ◽  
Author(s):  
Xing Li ◽  
Yao Sun ◽  
Hui Wang ◽  
Mei Su ◽  
Shoudao Huang
Keyword(s):  
Hybrid Control ◽  
Three Phase ◽  
Control Scheme ◽  
Hybrid Control Scheme

scholarly journals Reactive Power Control of Single-Stage Three-Phase Photovoltaic System during Grid Faults Using Recurrent Fuzzy Cerebellar Model Articulation Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Faa-Jeng Lin ◽  
Kuang-Chin Lu ◽  
Hsuan-Yu Lee
Keyword(s):  
Neural Network ◽  
Power Control ◽  
Reactive Power ◽  
Single Stage ◽  
Pv System ◽  
Reactive Power Control ◽  
Active And Reactive Power ◽  
Three Phase ◽  
Control Scheme ◽  
Reactive Current

This study presents a new active and reactive power control scheme for a single-stage three-phase grid-connected photovoltaic (PV) system during grid faults. The presented PV system utilizes a single-stage three-phase current-controlled voltage-source inverter to achieve the maximum power point tracking (MPPT) control of the PV panel with the function of low voltage ride through (LVRT). Moreover, a formula based on positive sequence voltage for evaluating the percentage of voltage sag is derived to determine the ratio of the injected reactive current to satisfy the LVRT regulations. To reduce the risk of overcurrent during LVRT operation, a current limit is predefined for the injection of reactive current. Furthermore, the control of active and reactive power is designed using a two-dimensional recurrent fuzzy cerebellar model articulation neural network (2D-RFCMANN). In addition, the online learning laws of 2D-RFCMANN are derived according to gradient descent method with varied learning-rate coefficients for network parameters to assure the convergence of the tracking error. Finally, some experimental tests are realized to validate the effectiveness of the proposed control scheme.

scholarly journals Full-Observable Three-Phase State Estimation Algorithm Applied to Electric Distribution Grids

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1327 ◽  
Author(s):  
Thiago Soares ◽  
Ubiratan Bezerra ◽  
Maria Tostes
Keyword(s):  
State Estimation ◽  
Real Time ◽  
Distribution Systems ◽  
Phase State ◽  
Reactive Power ◽  
Estimation Algorithm ◽  
Load Flow ◽  
Electrical Network ◽  
The Real ◽  
Three Phase

This paper proposes the development of a three-phase state estimation algorithm, which ensures complete observability for the electric network and a low investment cost for application in typical electric power distribution systems, which usually exhibit low levels of supervision facilities and measurement redundancy. Using the customers´ energy bills to calculate average demands, a three-phase load flow algorithm is run to generate pseudo-measurements of voltage magnitudes, active and reactive power injections, as well as current injections which are used to ensure the electrical network is full-observable, even with measurements available at only one point, the substation-feeder coupling point. The estimation process begins with a load flow solution for the customers´ average demand and uses an adjustment mechanism to track the real-time operating state to calculate the pseudo-measurements successively. Besides estimating the real-time operation state the proposed methodology also generates nontechnical losses estimation for each operation state. The effectiveness of the state estimation procedure is demonstrated by simulation results obtained for the IEEE 13-bus test network and for a real urban feeder.

scholarly journals Electronic Differential and Neuro-Fuzzy Sliding Mode Control with Extended State Observer for an Electric Vehicle System

2018 ◽  
Vol 61 ◽  
pp. 00007
Author(s):  
Ibrahim Farouk Bouguenna ◽  
Ahmed Azaiz ◽  
Ahmed Tahour ◽  
Ahmed Larbaoui
Keyword(s):  
Sliding Mode Control ◽  
Hybrid Control ◽  
Sliding Mode ◽  
Current Loop ◽  
Fuzzy Sliding Mode Control ◽  
Mode Control ◽  
Neuro Fuzzy ◽  
Control Scheme ◽  
Fuzzy Sliding Mode ◽  
Hybrid Control Scheme

In this paper a neuro-fuzzy-sliding mode control (NFSMC) with extended state observer (ESO) technique; is designed to guarantee the traction of an electric vehicle with two distinct permanent magnet synchronous motor (PMSM). Each PMSM systems (source-convertermotor) are attached to an electronic differential (ED), in order to adjust the senses of direction of the vehicle, and sustain a stable speed by adapting the difference in velocity of each motor-wheel according to the direction in the case of a turn. Two types of controllers are employed by a hybrid control scheme to assure the control and the performance of the vehicle. This hybrid control scheme guarantees the stability of the vehicle by ED, reduces the chattering phenomena in the PMSM electric motor, and improves the disturbance rejection ability which employs tow types of controllers. The neuro-fuzzy sliding mode control on the direct current loop and ESO controller on the speed loop, and the quadratic current loop; taking into account the dynamic of the vehicle. Simulation runs under Matlab/Simulink to assess the efficiency, and strength of the recommended control method on the closed loop system.

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