JACIII Vol.25 No.3 pp. 310-316
doi: 10.20965/jaciii.2021.p0310


Single-Phase Photovoltaic Grid-Connected Inverter Based on Fuzzy Neural Network

Shenping Xiao*,**,†, Zhouquan Ou*,**, Junming Peng*,**, Yang Zhang*,**, and Xiaohu Zhang*,**

*College of Electrical and Information Engineering, Hunan University of Technology
88 Taishan Road, Tianyuan District, Zhuzhou City, Hunan 412007, China

**Key Laboratory for Electric Drive Control and Intelligent Equipment of Hunan Province
88 Taishan Road, Tianyuan District, Zhuzhou City, Hunan 412007, China

Corresponding author

September 29, 2020
February 16, 2021
May 20, 2021
single-phase inverter, fuzzy neural network, PID controller

Based on a single-phase photovoltaic grid-connected inverter, a control strategy combining traditional proportional–integral–derivative (PID) control and a dynamic optimal control algorithm with a fuzzy neural network was proposed to improve the dynamic characteristics of grid-connected inverter systems effectively. A fuzzy inference rule was established after analyzing the proportional, integral, and differential coefficients of the PID controller. A fuzzy neural network was applied to adjust the parameters of the PID controller automatically. Accordingly, the proposed dynamic optimization algorithm was deduced in theory. The simulation and experimental results showed that the method was effective in making the system more robust to external disruption owing to its excellent steady-state adaptivity and self-learning ability.

Topology diagram of the fuzzy neural network

Topology diagram of the fuzzy neural network

Cite this article as:
S. Xiao, Z. Ou, J. Peng, Y. Zhang, and X. Zhang, “Single-Phase Photovoltaic Grid-Connected Inverter Based on Fuzzy Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.25 No.3, pp. 310-316, 2021.
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Last updated on Apr. 22, 2024