Dynamic Lissajous patterns for real time identification and localization of power quality disturbance

Pampa Sinha; Snehalika Snehalika; Ranjith Kumar Gatla; P Shashavali; Devineni Gireesh Kumar; Idamakanti Kasireddy; D S Naga Malleswara Rao; Hassan Abdurrahman Shuaibu; Taha Selim Ustun

doi:10.1038/s41598-025-10218-4

Dynamic Lissajous patterns for real time identification and localization of power quality disturbance

Sci Rep. 2025 Sep 5;15(1):32372. doi: 10.1038/s41598-025-10218-4.

Authors

Pampa Sinha¹, Snehalika Snehalika², Ranjith Kumar Gatla³, P Shashavali⁴, Devineni Gireesh Kumar⁵, Idamakanti Kasireddy⁶, D S Naga Malleswara Rao⁷, Hassan Abdurrahman Shuaibu⁸, Taha Selim Ustun⁹

Affiliations

¹ School of Electrical Engineering, KIIT, Bhubaneswar, 751024, India. pampa.sinhafel@kiit.ac.in.
² School of Electrical Engineering, KIIT, Bhubaneswar, 751024, India.
³ Department of Computer Science and Engineering (Data Science), Institute of Aeronautical Engineering, Hyderabad, Telangana, 500043, India.
⁴ Department of EEE, S K University College of Engineering & Technology, Ananthapur, Andhra Pradesh, 515003, India.
⁵ Department of EEE, B V Raju Institute of Technology, Narsapur, Telangana, 500092, India.
⁶ EEE Department, Vishnu Institute of Technology, Bhimavaram, 534202, India.
⁷ Department of EEE, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, 500090, India.
⁸ Department of Electrical, Telecommunications and Computer Engineering, Kampala International University, Kampala, Uganda. hassan.shvaibu@kiu.ac.ug.
⁹ Fukushima Renewable Energy Institute, Koriyama, 963-0298, Japan.

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.

Keywords: Adaptive thresholds; Current signals; Disturbance localization; Dynamic lissajous patterns; Event detection; Grid stability; Localization; Microgrids; Power quality disturbances; Power quality monitoring; Real-Time identification; Voltage signals.