Abstract
This study proposes a novel and computationally efficient method for real-time identification and localization of power quality (PQ) disturbances in microgrids using dynamic Lissajous patterns formed by voltage and current waveforms. Each power disturbance-such as sag, swell, harmonic distortion, and transients-induces a unique geometric deformation in the Lissajous figure, which serves as a visual signature of the event. Key geometric and statistical features, including area, skewness, kurtosis, and centroid deviation, are extracted from these dynamic patterns to construct robust indices for classification. Adaptive thresholds for each feature are determined dynamically, enabling accurate event detection without prior knowledge of the microgrid configuration. Numerical analysis demonstrates that the proposed method achieves high precision in detecting and distinguishing PQ disturbances, even under overlapping or noisy conditions. For instance, the method records Euclidean distance values of 0.7071 for swell and 0.5657 for harmonic distortion, while maintaining zero deviation for pure signals, validating its classification accuracy. The system exhibits strong resilience to unstable grid conditions and is computationally lightweight, making it suitable for real-time deployment in embedded devices within microgrids. Comparative evaluation reveals superior performance in terms of speed, accuracy, and false positive minimization, highlighting the potential of dynamic Lissajous patterns as a powerful tool for advanced PQ monitoring in smart grid infrastructures.