A method of residual current detection employing adaptive Gaussian filtering

Abstract As the power grid continues to evolve, the waveforms of the residual currents within the grid are becoming increasingly complex. Timely detection and processing of these residual currents is crucial for ensuring personal safety, protecting property, and maintaining the stability of electrical systems. This paper proposes a residual current detection method based on adaptive Gaussian filtering to address residual current signals with complex waveforms. By adaptively adjusting the standard deviation of the Gaussian function, the method can significantly enhance the denoising performance, including reducing mean square error and improving the signal-to-noise ratio. Simulation results demonstrate the effectiveness, robustness and adaptability of the proposed method in the filtering performance as compared with the least mean squares algorithm, Kalman filter and the variational mode decomposition algorithm. These findings validate the effectiveness of the proposed detection method, which makes it highly potential for accurate residual current detection under complex waveform conditions and practical applications.