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| Triple-Vector Model-Free Predictive Current Control Strategy for Grid-Connected Inverter Based on Adaptive Ultra-Local |
| Rui Tao1, Feng Zhuangzhuang2, Hu Cungang2, Sun Xiaolei2, Lu Geye3 |
1. School of Internet Anhui University Hefei 230601 China;
2. School of Electrical Engineering and Automation Anhui University Hefei 230601 China;
3. School of Electrical Engineering Beijing Jiaotong University Beijing 100091 China |
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Abstract As an innovative power system architecture, a microgrid integrates renewable energy, storage devices, loads, and control systems into an independent unit, enhancing energy efficiency and the system’s flexibility and stability. In microgrids, grid-connected inverters are crucial for linking renewable energy to the grid, with their current performance directly affecting overall operation. Therefore, efficient control of grid-connected current is vital for reliable microgrid operation. Finite-control-set model predictive current control (FCS-MPCC) predicts system control targets in real-time and responds rapidly, which has been widely applied to grid-connected inverter current control. Traditional FCS-MPCC predicts future states using a discrete model of the inverter and selects the optimal state based on a cost function. However, the lack of modulation in FCS-MPCC leads to significant current ripple and variable switching frequency issues. Additionally, FCS-MPCC relies on accurate model parameters of the grid-connected inverter. Mismatches between model and actual parameters can impair current prediction accuracy, causing current distortion and affecting system stability.
This paper proposes a triple-vector model-free predictive current control (MFPCC) strategy based on an adaptive ultra-local model. Firstly, the effects of parameter mismatch on vector duration and current prediction in triple-vector MPCC are analyzed. Then, an ultra-local model of grid-connected inverter is established, the system disturbance in the ultra-local model is estimated through an extended state observer, and an adaptive gain is designed. The adaptive gain is updated in real-time based on the gradient descent method. Then, the triple-vector MFPCC is realized according to the triple-vector synthesis method and the ultra-local model. The proposed method can observe the disturbance of the system in real-time, eliminate the influence of parameters on the triple-vector MPCC, and further improve the noise suppression ability of the system by strengthening factors. Finally, the effectiveness of the proposed triple-vector MFPCC method is verified by simulation and experiment.
The following conclusions can be drawn. This paper proposes an adaptive ultra-local model-free triple- vector predictive current control method for grid-connected inverters. An ultra-local model of the inverter is constructed using an extended state observer to monitor disturbances, and an adaptive gain is designed to mitigate the impact of parameters on calculation time and prediction in triple-vector model predictive current control. Experimental results indicate that the proposed method offers better robustness and superior noise suppression than the traditional triple-vector MFPCC.
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Received: 11 July 2024
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2025, Vol. 40 
