An Adaptive Variable-Parameter MAF-MATCH Algorithm for Grid-Voltage Detection Under Non-Ideal Conditions

With the increasing penetration of renewable energy and the rising demand for power quality, the dynamic performance and accuracy of grid-voltage detection have become crucial for the control of grid-following devices such as dynamic voltage restorers (DVRs). However, the conventional moving average filter (MAF) in grid-voltage detection suffers from inherent limitations in dynamic response. To address this issue, this paper proposes a voltage-detection method, which is based on an adaptive variable-parameter filtering algorithm termed MAF-MATCH-V. First, a cascaded filter model is constructed by integrating a zero-pole matcher (MATCH) with the MAF. Frequency-domain analysis demonstrates that the MATCH compensates for the mid- and high-frequency magnitude attenuation and reduces the phase delay of the MAF, thereby accelerating the dynamic response while preserving its harmonic-rejection capability. Second, the influence of the matching coefficient on the time-domain response is investigated, and a time-varying adaptive strategy is designed to balance rapid disturbance recognition with steady-state convergence. Finally, experimental results under various non-ideal grid conditions demonstrate that the proposed method achieves superior overall performance compared with conventional approaches. Specifically, MAF-MATCH-V realizes millisecond-level event recognition and zero steady-state error convergence, making it a practical solution for the real-time control of grid-following equipment in modern power systems.