BP Neural Network-Based Adaptive Phase-Locked Loop for Impedance Measurement with Dynamic Operating Conditions

With the development of modern power systems, impedance measurement has become a key approach for stability assessment of power-electronic-dominated grids. In particular, frequency-sweep–based impedance measurement is widely adopted due to its effectiveness in characterizing system dynamics. However, owing to the limited adaptability of conventional phase-locked loop (PLL), grid-following (GFL) converters encounter significant challenges in maintaining stable operation during frequency-sweep signal injection, especially under non-power-frequency excitation and grid disturbances. To address these issues, this paper proposes an adaptive PLL scheme based on a backpropagation (BP) neural network. This scheme fully leverages the nonlinear mapping and adaptive learning capabilities of the BP neural network, which significantly enhances the robustness and stability adaptability of the synchronization component against small disturbances during the impedance measurement process. A hybrid strategy combining offline parameter tuning and online adaptive adjustment is adopted to improve reliability under varying operating conditions. Comprehensive simulation studies and hardware-in-the-loop (HIL) experimental results verify the effectiveness of the proposed method in improving stability and impedance measurement accuracy in frequency-sweep–based applications.