Kuang-Hsiung Tan

Active islanding detection method using wavelet fuzzy neural network

A novel active islanding detection method using d-axis disturbance signal injection with intelligent control is proposed in this study. The proposed active islanding detection method is based on injecting a disturbance signal into the system through the d-axis current which leads to a frequency deviation at the terminal of the RLC load when the grid is disconnected. The feasibility of the proposed method is evaluated under the UL1741 anti-islanding test configuration. The proposed d-axis disturbance signal injection method is intended to achieve a reliable detection with quasi zero non-detection zone (NDZ), minimum effects on power quality and easy implementation without additional sensing devices or equipments. Moreover, to further improve the performance of islanding detection method, a wavelet fuzzy neural network (WFNN) intelligent controller is proposed to replace the proportional-integral (PI) controller used in traditional injection method for islanding detection. Furthermore, the network structure and the on-line learning algorithm of the WFNN are introduced in detail. Finally, the feasibility and effectiveness of the proposed d-axis disturbance signal injection method is verified with experimental results.

Squirrel-cage induction generator system using hybrid wavelet fuzzy neural network control for wind power applications

An intelligent controlled three-phase squirrel-cage induction generator (SCIG) system for grid-connected wind power applications using hybrid wavelet fuzzy neural network (WFNN) is proposed in this study. First, the indirect field-oriented mechanism is implemented for the control of the SCIG system. Then, an AC/DC power converter and a DC/AC power inverter are developed to convert the electric power generated by a three-phase SCIG to power grid. Moreover, the dynamic model of the SCIG system and an ideal computed torque controller are developed for the control of the square of DC-link voltage. Furthermore, an intelligent hybrid WFNN controller and two WFNN controllers, which are computation intensive approaches, are proposed for the AC/DC power converter and the DC/AC power inverter, respectively, to improve the transient and steady-state responses of the SCIG system at different operating conditions. In the intelligent hybrid WFNN controller, to relax the requirement of the lumped uncertainty in the design of the ideal computed torque controller, a WFNN is designed as an uncertainty observer to adapt the lumped uncertainty online. Finally, the feasibility and effectiveness of the SCIG system for grid-connected wind power applications are verified with experimental results.

Active islanding detection method via current injection disturbance using Elman neural network

Islanding detection is an essential protection requirement for distributed generators for personnel and equipment safety. This study presents a new active islanding detection method via current injection disturbance using intelligent tracking control of the active and reactive power commands. First, an active islanding detection method based on injecting a disturbance signal into the system through the d-axis current which leads to a frequency deviation at the terminal of the RLC load when the grid is disconnected is proposed. The feasibility of the proposed method is evaluated under the UL1741 anti-islanding test configuration. Then, to further improve the performance of the islanding detection method, an Elman neural network intelligent controller is adopted to replace the proportional-integral controller used in the traditional injection method for islanding detection. Moreover, the network structure and the online learning algorithm are described in detail. Finally, the feasibility and effectiveness of the proposed current injection disturbance method are verified with experimental results.

Intelligent-controlled doubly fed induction generator system using PFNN

An intelligent-controlled doubly fed induction generator (DFIG) system using probabilistic fuzzy neural network (PFNN) is proposed in this study. This system can be applied as a stand-alone power supply system or as the emergency power system when the electricity grid fails for all sub-synchronous, synchronous, and super-synchronous conditions. The rotor side converter is controlled using the field-oriented control to produce three-phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the grid side converter, which is also controlled using field-oriented control, is primarily implemented to maintain the magnitude of the DC-link voltage. Furthermore, an intelligent PFNN controller is proposed for both the rotor and grid side converters to improve the transient and steady-state responses of the DFIG system at different operating conditions. The network structure, online learning algorithm, and convergence analyses of the PFNN are introduced in detail. Finally, the feasibility of the proposed control scheme is verified using some experimental results.

Control of Doubly-Fed Induction Generator System Using PIDNNs

An intelligent control stand-alone doubly-fed induction generator (DFIG) system using proportional-integral-derivative neural network (PIDNN) is proposed in this study. This system can be applied as a stand-alone power supply system or as the emergency power system when the electricity grid fails for all sub-synchronous, synchronous and super-synchronous conditions. The rotor side converter is controlled using the field-oriented control to produce three-phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the stator side converter, which is also controlled using field-oriented control, is primarily implemented to maintain the magnitude of the DC-link voltage. Furthermore, the intelligent PIDNN controller is proposed for both the rotor and stator side converters to improve the transient and steady-state responses of the DFIG system for different operating conditions. Both the network structure and on-line learning algorithm are introduced in detail. Finally, the feasibility of the proposed control scheme is verified through experimentation.

Squirrel-cage induction generator system using intelligent control for wind power applications

An intelligent controlled three-phase squirrel-cage induction generator (SCIG) system for grid-connected power application using wavelet fuzzy neural network (WFNN) is proposed in this study. First, the indirect field-oriented mechanism is implemented for the control of the SCIG system. Then, an AC/DC power converter and a DC/AC power inverter are developed to convert the electric power generated by a three-phase SCIG from variable-voltage and variable-frequency to constant-voltage and constant-frequency. Moreover, the intelligent WFNN controller is proposed for both the AC/DC power converter and DC/AC power inverter to improve the transient and steady-state responses of the SCIG system at different operating conditions. Three online trained WFNNs using backpropagation learning algorithm are implemented as the tracking controllers for the DC-link voltage of the AC/DC power converter and the active power and reactive power outputs of the DC/AC power inverter. Furthermore, the network structure and the online learning algorithm of the WFNN are introduced in detail. Finally, some experimental results are provided to demonstrate the effectiveness of the proposed SCIG system.

Squirrel-cage induction generator system using probabilistic fuzzy neural network for wind power applications

An intelligent controlled three-phase squirrel-cage induction generator (SCIG) system for grid-connected power application using probabilistic fuzzy neural network (PFNN) is proposed in this study. First, an AC/DC power converter and a DC/AC power inverter are developed to convert the electric power generated by a three-phase SCIG to power grid. Then, the characteristics of wind turbine emulator are described in detail. Moreover, in order to improve the transient and steady-state responses of the DC-link voltage of the SCIG system, the intelligent PFNN controller is proposed for DC/AC power inverter to replace the conventional proportional-integral (PI) controller. The online trained PFNN using back propagation learning algorithm is implemented as the tracking controller for the DC-link voltage of the DC/AC power inverter. Furthermore, the network structure and the online learning algorithm of the PFNN are introduced in detail. Finally, the feasibility and effectiveness of the SCIG system for grid-connected wind power applications is verified with experimental results.

Control of doubly-fed induction generator system using PFNN

An intelligent controlled doubly-fed induction generator (DFIG) system using probabilistic fuzzy neural network (PFNN) is proposed in this study. This system can be applied as a stand-alone power supply system or as the emergency power system when the electricity grid fails for all sub-synchronous, synchronous and super-synchronous conditions. The rotor side converter is controlled using the field-oriented control to produce three-phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the stator side converter, which is also controlled using field-oriented control, is primarily implemented to maintain the magnitude of the DC-link voltage. Furthermore, an intelligent PFNN controller is proposed for both the rotor and stator side converters to improve the transient and steady-state responses of the DFIG system for different operating conditions. The network structure, on-line learning algorithm and convergence analyses of the PFNN are introduced in detail. Finally, the feasibility of the proposed control scheme is verified using some experimental results.

Intelligent controlled shunt active power filter for voltage and current harmonic compensation in microgrid system

ABSTRACT An integrated microgrid with a novel shunt active power filter (APF) using Elman neural network (ENN) is proposed in this study. The microgrid consists of a storage system, a photovoltaic (PV) system, the shunt APF, a linear load, and a nonlinear load. Moreover, the master/slave control algorithm is adopted in the microgrid. The storage system, which is considered as the master unit, is adopted to control the active and reactive power outputs (P/Q control) in grid-connected mode and the voltage and frequency of the microgrid (V/f control) in islanded mode. Furthermore, the PV system is considered as the slave unit to implement P/Q control in both grid-connected and islanded modes. In addition, the proposed shunt APF possesses dual functions of voltage and current harmonic compensation for microgrid under voltage harmonic propagation and nonlinear load to reduce the voltage and current total harmonic distortions (THD) effectively. Additionally, an ENN controller is adopted in the proposed shunt APF to improve the transient and steady-state responses of DC-link voltage during the switching between the grid-connected mode and islanded mode. Finally, some simulation results are provided to verify the feasibility and the effectiveness of the integrated microgrid with the intelligent controlled shunt APF.