Robust Transient and Anomaly Detection in Voltage Waveforms Using Curve-Fitted Sinusoidal Models

This paper presents a novel hybrid methodology for robust detection of transients and anomalies in waveform data by synergizing frequency- and time-domain analysis. The approach begins with the Fast Fourier Transform (FFT) to extract dominant spectral components, which inform the initialization of a non-linear sinusoidal curve-fitting process in the time domain. This fitting yields precise estimates of amplitude, angular frequency, and phase shift, enabling enhanced tracking of waveform dynamics. An adaptive, datadriven detection threshold is then established, defined as 5% of the fitted amplitude, allowing for sensitive and context-aware identification of anomalous deviations. Unlike conventional methods that rely on fixed thresholds or operate solely in one domain, our method dynamically adapts to signal characteristics, improving detection accuracy under varying conditions. Validation results demonstrate the method's effectiveness in accurately identifying transient events and anomalies, underscoring its potential in critical applications such as power system monitoring and fault detection.