.png){kind=icon}
.png){kind=icon}
Return
Design of grid-connected photovoltaic inverter based on RBF neural network parameter self-tuning PI algorithm
.png){kind=icon}
PRE
AI
D
C
Z
Y
Y
DOI：10.1063/5.0277164.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
For the requirement of small distortion of photovoltaic (PV) grid-connected inverter output waveform, this paper designs a PV grid-connected inverter with a three-layer radial basis function neural network control algorithm for online adjustment of proportional-integral (PI) parameters. Through online learning of control parameters, the system response is accelerated, and harmonic pollution of the public grid is avoided. Simulation experiments are conducted using Simulink to compare the algorithm with PI control, and the steady-state error as well as the dynamic error is smaller than the latter. The experimental results show that the controller has good adaptability in the case of alternating current grid access; has good stability; and has the advantage of not needing to debug PI parameters, which can meet the needs of PV grid connection.
Journal
IF:
1.9
Papers:
373
Citations:
4.4K
Organization
Cited Papers