Unsupervised power line fault segmentation and classification using periodic time series analysis techniques | |
| Author | Singha, Abhinav |
| Call Number | AIT RSPR no.CS-21-04 |
| Subject(s) | Time-series analysis Machine learning |
| Note | A research study submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Computer Science |
| Publisher | Asian Institute of Technology |
| Abstract | Automated anomaly detection has the potential to increase the efficiency of human work in electrical grids. Early detection and accurate classification of faults would help opera tors avoid escalation of an issue and recover quickly, and would enable efficient and ef fective analysis of problems across the entire grid. In this research study, I develop meth ods for detecting anomalies in time series data from digital fault recorders and classifying those anomalies. For this purpose I use a variant of the Periodic Curve Anomaly Detec tion (PCAD) algorithm, which is an unsupervised learning algorithm for anomaly detection in asynchronous periodic time series data. Taking inspiration from the method used by Rebbapragada et al. (2009), I devised a method that uses phase shift and k-means cluster ing with a Euclidean distance measure to segregate anomalies in the data. The segregated anomalies are then aggregate and mapped to clusters for classification. The method is able to detect anomalous segments in the time series recorded by digital fault recorders with an accuracy of 98.56%. The classification method however, is not accurate enough for pro duction use in identifying fault signatures. I recommend implementing the PCAD method for fault segmentation followed by a supervised classifier or an unsupervised method with a more sophisticated distance metric for classification. |
| Year | 2021 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Dailey, Mathew N. |
| Examination Committee(s) | Attaphongse Taparugssanagorn;Chutiporn Anutariya |
| Scholarship Donor(s) | AIT Fellowship |
| Degree | Research Studies Project Report (M. Eng.) - Asian Institute of Technology, 2021 |