Reactive Power Control of Three-Phase Grid-Connected PV System During Grid Faults Using Takagi-Sugeno-Kang Probabilistic Fuzzy Neural Network Control

An intelligent controller based on the Takagi-Sugeno-Kang-type probabilistic fuzzy neural network with an asymmetric membership function (TSKPFNN-AMF) is developed in this paper for the reactive and active power control of a three-phase grid-connected photovoltaic (PV) system during grid faults. The...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2015-09, Vol.62 (9), p.5516-5528
Main Authors: Lin, Faa-Jeng, Lu, Kuang-Chin, Ke, Ting-Han, Yang, Bo-Hui, Chang, Yung-Ruei
Format: Article
Language:English
Subjects:
Asymmetric membership function (AMF)
Control systems
Faults
Fuzzy control
Fuzzy logic
Fuzzy neural networks
grid faults
Inverters
low voltage ride through (LVRT)
Networks
Photovoltaic cells
photovoltaic system
Probabilistic logic
Reactive power
reactive power control
Regulations
Solar cells
Solar energy
Takagi- Sugeno-Kang type probabilistic fuzzy neural network (TSKPFNN)
Voltage control
Voltage fluctuations
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Summary:An intelligent controller based on the Takagi-Sugeno-Kang-type probabilistic fuzzy neural network with an asymmetric membership function (TSKPFNN-AMF) is developed in this paper for the reactive and active power control of a three-phase grid-connected photovoltaic (PV) system during grid faults. The inverter of the three-phase grid-connected PV system should provide a proper ratio of reactive power to meet the low-voltage ride through (LVRT) regulations and control the output current without exceeding the maximum current limit simultaneously during grid faults. Therefore, the proposed intelligent controller regulates the value of reactive power to a new reference value, which complies with the regulations of LVRT under grid faults. Moreover, a dual-mode operation control method of the converter and inverter of the three-phase grid-connected PV system is designed to eliminate the fluctuation of dc-link bus voltage under grid faults. Furthermore, the network structure, the online learning algorithm, and the convergence analysis of the TSKPFNN-AMF are described in detail. Finally, some experimental results are illustrated to show the effectiveness of the proposed control for the three-phase grid-connected PV system.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2015.2407851