Scalable Neural Network Control for Nonlinear DC Microgrids Under Plug-and-Play Operations

Abstract

Plug-and-play (PnP) operations of distributed generation units (DGUs) with constant power loads (CPLs) often destabilize dc microgrids (DCmGs). To address this issue, this article proposes a scalable neural network control strategy for nonlinear DCmGs with CPLs, enabling seamless PnP operations of DGUs. A radial basis function neural network is employed to handle the uncertain CPL nonlinearity without requiring any prior knowledge. A structured Lyapunov matrix is utilized to eliminate the coupling effects of power lines by reshaping them into a Laplacian matrix structure. Within this framework, a scalable neural network control approach is proposed, integrating a nominal controller with explicit gain inequalities and an adaptive controller governed by an adaptation law. This approach operates locally, independent of other DGUs and power lines, ensuring PnP operations and maintaining uniformly ultimately bounded stability. The effectiveness of the proposed method is validated through case studies on a modified IEEE 37-bus test system.