This page has only limited features, please log in for full access.
A Petri recurrent wavelet fuzzy neural network (PetriRWFNN) and a simple pre-synchronization estimation are proposed for operations of seamless switching and grid reconnection in microgrid. The microgrid using master\slave control consists of a storage, photovoltaic (PV) and loads, and can be operated in either grid-connected mode or islanded mode. Since the different control algorithm is adopted in the master distributed generator (DG) at different operation mode, the transient deterioration in voltage and active power output of the microgrid are obvious during the mode switching. Moreover, when the microgrid is operated in islanded mode and the power grid returns to normal operation, the microgrid cant directly reconnect with the power grid to avoid a large inrush of current and failed grid reconnection due to the asynchronous angle. Hence, a novel PetriRWFNN is proposed to improve the transient responses of the voltage and active power of microgrid during mode switching. Furthermore, a simple and fast pre-synchronization estimation for grid reconnection during the switching from islanded mode to grid-connected mode is also proposed in this study. Finally, some experimental results are provided to certify the effectiveness of microgrid using proposed PetriRWFNN and pre-synchronization estimation for operations of seamless switching and grid reconnection.
Kuang-Hsiung Tan; Tzu-Yu Tseng. Seamless Switching and Grid Reconnection of Microgrid Using Petri Recurrent Wavelet Fuzzy Neural Network. IEEE Transactions on Power Electronics 2021, 36, 11847 -11861.
AMA StyleKuang-Hsiung Tan, Tzu-Yu Tseng. Seamless Switching and Grid Reconnection of Microgrid Using Petri Recurrent Wavelet Fuzzy Neural Network. IEEE Transactions on Power Electronics. 2021; 36 (10):11847-11861.
Chicago/Turabian StyleKuang-Hsiung Tan; Tzu-Yu Tseng. 2021. "Seamless Switching and Grid Reconnection of Microgrid Using Petri Recurrent Wavelet Fuzzy Neural Network." IEEE Transactions on Power Electronics 36, no. 10: 11847-11861.
An intelligent control method using recurrent wavelet fuzzy neural network (RWFNN) is proposed to improve the low-voltage ride through (LVRT) performance of a two-stage photovoltaic (PV) power plant under grid faults for the weak grid conditions. The PV power plant comprises an interleaved DC/DC converter and a three-level neutral-point clamped (NPC) smart inverter, in which the output active and reactive powers of the inverter can be predetermined in accordance with grid codes of the utilities. Moreover, for the purpose of improving the control performance of the PV power plant to handle the grid faults for the weak grid conditions, a new RWFNN with online training is proposed to replace the traditional proportional-integral (PI) controller for the active and reactive powers control of the smart inverter. Furthermore, the proposed controllers are implemented by two floating-point digital signal processors (DSPs). From the simulation and experimental results, excellent control performance for the tracking of active and reactive powers under grid faults for the weak grid conditions can be achieved by using the proposed intelligent control method.
Faa-Jeng Lin; Kuang-Hsiung Tan; Wen-Chou Luo; Guo-Deng Xiao. Improved LVRT Performance of PV Power Plant Using Recurrent Wavelet Fuzzy Neural Network Control for Weak Grid Conditions. IEEE Access 2020, 8, 69346 -69358.
AMA StyleFaa-Jeng Lin, Kuang-Hsiung Tan, Wen-Chou Luo, Guo-Deng Xiao. Improved LVRT Performance of PV Power Plant Using Recurrent Wavelet Fuzzy Neural Network Control for Weak Grid Conditions. IEEE Access. 2020; 8 (99):69346-69358.
Chicago/Turabian StyleFaa-Jeng Lin; Kuang-Hsiung Tan; Wen-Chou Luo; Guo-Deng Xiao. 2020. "Improved LVRT Performance of PV Power Plant Using Recurrent Wavelet Fuzzy Neural Network Control for Weak Grid Conditions." IEEE Access 8, no. 99: 69346-69358.
A microgrid with virtual inertia using master-slave control is proposed in this study to overcome the drawbacks of traditional inverter-based distributed generators for lack of inertia and without grid-forming capability. The microgrid using master-slave control is composed of a storage system, a photovoltaic (PV) system and a varying resistive three-phase load. The storage system and PV system are regarded as the master unit and the slave unit respectively in the microgrid. Moreover, in order to improve the reactive power control in grid-connected mode and the transient response of microgrid during the switching between the grid-connected mode and islanding mode, an online trained recurrent probabilistic wavelet fuzzy neural network (RPWFNN) is proposed to replace the conventional proportional-integral (PI) controller in the storage system. Furthermore, when the microgrid is operated in islanding mode, the load variation will have serious influence on the voltage control of the microgrid. Thus, the RPWFNN control is also proposed to improve the transient and steady-state responses of voltage control in the microgrid. Finally, according to some experimental results, excellent control performance of the microgrid with virtual inertia using the proposed intelligent controller can be achieved.
Kuang-Hsiung Tan; Faa-Jeng Lin; Cheng-Ming Shih; Che-Nan Kuo. Intelligent Control of Microgrid With Virtual Inertia Using Recurrent Probabilistic Wavelet Fuzzy Neural Network. IEEE Transactions on Power Electronics 2019, 35, 7451 -7464.
AMA StyleKuang-Hsiung Tan, Faa-Jeng Lin, Cheng-Ming Shih, Che-Nan Kuo. Intelligent Control of Microgrid With Virtual Inertia Using Recurrent Probabilistic Wavelet Fuzzy Neural Network. IEEE Transactions on Power Electronics. 2019; 35 (7):7451-7464.
Chicago/Turabian StyleKuang-Hsiung Tan; Faa-Jeng Lin; Cheng-Ming Shih; Che-Nan Kuo. 2019. "Intelligent Control of Microgrid With Virtual Inertia Using Recurrent Probabilistic Wavelet Fuzzy Neural Network." IEEE Transactions on Power Electronics 35, no. 7: 7451-7464.
In this study, an intelligent controlled distributed generator (DG) system is proposed for tracking control and islanding detection. First, a DC/AC inverter with DC power supply is adopted to emulate a DG system and control the active and reactive power outputs. Moreover, in order to comply with the standard for interconnection with the power grid, a novel active islanding detection method is proposed for the inverter-based DG system. In the proposed active islanding detection method, a perturbation signal is designed to inject into the d-axis current of the DG system which causes the frequency at the terminal of the RLC load to deviate when the power grid breaks down. The feasibility of the proposed active islanding detection method is verified according to the UL 1741 test configuration. Furthermore, in order to improve the tracking control of the active and reactive powers of the inverter-based DG system, and to effectively reduce the detection time of islanding phenomenon, two probabilistic fuzzy neural network (PFNN) controllers are adopted to take the place of the conventional proportional-integral (PI) controllers. In addition, the network structure and the online learning algorithm of the adopted PFNN are presented in details. Finally, some experimental results of the proposed active islanding detection method using PFNN controllers are proposed to validate the effectiveness and feasibility of the tracking control and islanding detection.
Kuang-Hsiung Tan; Chien-Wu Lan. DG System Using PFNN Controllers for Improving Islanding Detection and Power Control. Energies 2019, 12, 506 .
AMA StyleKuang-Hsiung Tan, Chien-Wu Lan. DG System Using PFNN Controllers for Improving Islanding Detection and Power Control. Energies. 2019; 12 (3):506.
Chicago/Turabian StyleKuang-Hsiung Tan; Chien-Wu Lan. 2019. "DG System Using PFNN Controllers for Improving Islanding Detection and Power Control." Energies 12, no. 3: 506.
Faa-Jeng Lin; Kuang-Hsiung Tan; Yu-Kai Lai; Wen-Chou Luo. Intelligent PV Power System With Unbalanced Current Compensation Using CFNN-AMF. IEEE Transactions on Power Electronics 2018, 34, 8588 -8598.
AMA StyleFaa-Jeng Lin, Kuang-Hsiung Tan, Yu-Kai Lai, Wen-Chou Luo. Intelligent PV Power System With Unbalanced Current Compensation Using CFNN-AMF. IEEE Transactions on Power Electronics. 2018; 34 (9):8588-8598.
Chicago/Turabian StyleFaa-Jeng Lin; Kuang-Hsiung Tan; Yu-Kai Lai; Wen-Chou Luo. 2018. "Intelligent PV Power System With Unbalanced Current Compensation Using CFNN-AMF." IEEE Transactions on Power Electronics 34, no. 9: 8588-8598.
A distribution static compensator (DSTATCOM) is proposed in this study to improve the power quality, which includes the total harmonic distortion (THD) of the grid current and power factor (PF), of a mini grid with nonlinear and linear inductive loads. Moreover, the DC-link voltage regulation control of the DSTATCOM is essential especially under load variation conditions. Therefore, to improve the power quality and keep the DC-link voltage of the DSTATCOM constant under the variation of nonlinear and linear loads effectively, the traditional proportional-integral (PI) controller is substituted with a new online trained compensatory fuzzy neural network with an asymmetric membership function (CFNN-AMF) controller. In the proposed CFNN-AMF, the compensatory parameter to integrate pessimistic and optimistic operations of fuzzy systems is embedded in the CFNN. Furthermore, the dimensions of the Gaussian membership functions are directly extended to AMFs for the optimization of the fuzzy rules and the upgrade of learning ability of the networks. In addition, the network structure and online learning algorithm of the proposed CFNN-AMF are introduced in detail. Finally, the effectiveness and feasibility of the DSTATCOM using the proposed CFNN-AMF controller to improve the power quality and maintain the constant DC-link voltage under the change of nonlinear and linear inductive loads have been demonstrated by some experimental results.
Kuang-Hsiung Tan; Faa-Jeng Lin; Chao-Yang Tsai; Yung-Ruei Chang. A Distribution Static Compensator Using a CFNN-AMF Controller for Power Quality Improvement and DC-Link Voltage Regulation. Energies 2018, 11, 1996 .
AMA StyleKuang-Hsiung Tan, Faa-Jeng Lin, Chao-Yang Tsai, Yung-Ruei Chang. A Distribution Static Compensator Using a CFNN-AMF Controller for Power Quality Improvement and DC-Link Voltage Regulation. Energies. 2018; 11 (8):1996.
Chicago/Turabian StyleKuang-Hsiung Tan; Faa-Jeng Lin; Chao-Yang Tsai; Yung-Ruei Chang. 2018. "A Distribution Static Compensator Using a CFNN-AMF Controller for Power Quality Improvement and DC-Link Voltage Regulation." Energies 11, no. 8: 1996.
Due to the instantaneous power following into or out of the DC-link capacitor in a threephase shunt active power filter (APF), the DC-link voltage regulation control plays an important role in the shunt APF especially under nonlinear load change. In this study, for the purpose of improving the DC-link voltage regulation control in the shunt APF under nonlinear load variation and reducing the total harmonic distortion (THD) of the current effectively, a novel recurrent probabilistic fuzzy neural network with an asymmetric membership function (RPFNN-AMF) controller is developed to substitute for the conventional proportional-integral (PI) controller. Moreover, the network structure, the online learning algorithm and the convergence analysis of the proposed RPFNN-AMF are detailedly introduced. Finally, the effectiveness and feasibility of the shunt APF using the proposed RPFNN-AMF controller for the DC-link voltage regulation control and the compensation of harmonic current are verified by some experimental results.
Kuang-Hsiung Tan; Faa-Jeng Lin; Jun-Hao Chen. DC-Link Voltage Regulation Using RPFNN-AMF for Three-Phase Active Power Filter. IEEE Access 2018, 6, 37454 -37463.
AMA StyleKuang-Hsiung Tan, Faa-Jeng Lin, Jun-Hao Chen. DC-Link Voltage Regulation Using RPFNN-AMF for Three-Phase Active Power Filter. IEEE Access. 2018; 6 (99):37454-37463.
Chicago/Turabian StyleKuang-Hsiung Tan; Faa-Jeng Lin; Jun-Hao Chen. 2018. "DC-Link Voltage Regulation Using RPFNN-AMF for Three-Phase Active Power Filter." IEEE Access 6, no. 99: 37454-37463.
A three-phase four-leg inverter-based shunt active power filter (APF) is proposed to compensate three-phase unbalanced currents under unbalanced load conditions in grid-connected operation in this study. Since a DC-link capacitor is required on the DC side of the APF to release or absorb the instantaneous apparent power, the regulation control of the DC-link voltage of the APF is important especially under load variation. In order to improve the regulation control of the DC-link voltage of the shunt APF under variation of three-phase unbalanced load and to compensate the three-phase unbalanced currents effectively, a novel Petri probabilistic fuzzy neural network (PPFNN) controller is proposed to replace the traditional proportional-integral (PI) controller in this study. Furthermore, the network structure and online learning algorithms of the proposed PPFNN are represented in detail. Finally, the effectiveness of the three-phase four-leg inverter-based shunt APF with the proposed PPFNN controller for the regulation of the DC-link voltage and compensation of the three-phase unbalanced current has been demonstrated by some experimental results.
Kuang-Hsiung Tan; Faa-Jeng Lin; Jun-Hao Chen. A Three-Phase Four-Leg Inverter-Based Active Power Filter for Unbalanced Current Compensation Using a Petri Probabilistic Fuzzy Neural Network. Energies 2017, 10, 2005 .
AMA StyleKuang-Hsiung Tan, Faa-Jeng Lin, Jun-Hao Chen. A Three-Phase Four-Leg Inverter-Based Active Power Filter for Unbalanced Current Compensation Using a Petri Probabilistic Fuzzy Neural Network. Energies. 2017; 10 (12):2005.
Chicago/Turabian StyleKuang-Hsiung Tan; Faa-Jeng Lin; Jun-Hao Chen. 2017. "A Three-Phase Four-Leg Inverter-Based Active Power Filter for Unbalanced Current Compensation Using a Petri Probabilistic Fuzzy Neural Network." Energies 10, no. 12: 2005.