Artificial Neural Network-Based Pole-Tracking Method for Online Stabilization Control of Grid-Tied VSC

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Abstract
To cope with the weak grid stability issue of grid-tied voltage source converters (VSCs), this article proposes an artificial neural network (ANN) based approach for online stabilization control of the grid-tied VSC with the pole-tracking feature. First, an ANN is adopted to establish the mapping between the control parameters and the closed-loop poles of the grid-VSC system, serving as a computationally light model surrogate that is favorable for real-time control applications. Then, an online parameter search algorithm enabling simultaneous tuning of multiple controllers and parameters is developed, by which the system's poles under a new grid condition can be pulled to the reference ones, i.e., achieving the pole-tracking-based stabilization control of this article. Finally, the efficacy of the proposed method along with its stabilization effect is verified by MATLAB simulations and experimental results.
Funding Information
  • National Natural Science Foundation of China (51837007)

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