Control of Single-Phase Grid-Connected Converters with LCL Filters Using Recurrent Neural Network and Conventional Control Methods

Abstract

Single-phase grid-connected inverters are widely used to connect small-scale distributed renewable resources to the grid. However, unlike a three-phase system, control for a single-phase inverter is more challenging, especially when the inverter is used with an LCL filter. This paper proposes a novel recurrent neural network-based vector control method for a single-phase inverter with an LCL filter. The neural network is trained based on adaptive dynamic programming principle, and the objective of the training is to approximate optimal control. The Levenberg-Marquardt plus forward accumulation through time algorithm is developed for training the proposed recurrent neural network controller. The neural network vector control approach is compared with the conventional control methods, including the conventional PI-based vector control method and the PR-based control technique for single-phase inverters. Both the simulations and hardware experiments demonstrate the great advantages of the proposed neural network vector control over the conventional control methods. Compared with conventional control methods, the neural network control allows for low sampling rate and low switching frequency, while maintaining high performance in controlling a single-phase inverter. In addition, no specific damping policy is required to implement the proposed neural network vector control for an LCL-filter based single-phase inverter. The study shows that the neural network vector control is a robust control method, and can provide better control performance even when facing system parameter changes, while under this case, both the conventional PI-based vector control and the PR-based control failed to yield the acceptable results.