BP-Neural-Network-Based Adaptive Parameter Control for Grid-Following Inverters with Frequency-Band-Coordinated Regulation

The large-scale integration of renewable energy causes grid strength to vary over a wide range, exposing grid-following (GFL) inverters to both mid- and high-frequency resonance and subsynchronous oscillation (SSO). Conventional fixed-parameter designs cannot simultaneously maintain stability and dynamic performance because the phase-locked loop (PLL) and grid-voltage feedforward (GVF) dominate different frequency bands. This paper therefore proposes a backpropagation-neural-network (BPNN)-based adaptive parameter control strategy with frequency-band-coordinated regulation. First, a q-axis small-signal output-admittance model incorporating the current loop, digital delay, PLL, and GVF is established. The model reveals that the GVF coefficient primarily shapes mid- and high-frequency admittance under strong and moderately weak grids, whereas the PLL bandwidth becomes the dominant factor in low-frequency and subsynchronous stability under ultra-weak grids. Based on this mechanism, a BPNN is constructed with the grid short-circuit ratio (SCR) as the input and the GVF coefficient and PLL bandwidth as the outputs. Training targets are generated offline using parameter sweeps and performance screening based on current total harmonic distortion, Point of Common Coupling (PCC) voltage error, and settling time. During operation, the GVF coefficient is adjusted first, and the PLL bandwidth is reduced only when the grid becomes ultra-weak. Simulation results over SCR=1.25–10 demonstrate that the proposed strategy preserves stable operation while providing better transient and harmonic performance than fixed-parameter and single-parameter tuning schemes in the cases studied.