Single-Phase Photovoltaic Grid-Connected Inverter Based on Fuzzy Neural Network
| Autor: | Shenping Xiao, Xiaohu Zhang, Junming Peng, Zhouquan Ou, Yang Zhang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science Photovoltaic system PID controller 02 engineering and technology Human-Computer Interaction 020901 industrial engineering & automation Grid connected inverter Artificial Intelligence Control theory 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Single phase |
| Zdroj: | Journal of Advanced Computational Intelligence and Intelligent Informatics. 25:310-316 |
| ISSN: | 1883-8014 1343-0130 |
| DOI: | 10.20965/jaciii.2021.p0310 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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