Synchrophasor Data Event Detection using Unsupervised Wavelet Convolutional Autoencoders

Jacob Buckelew, Sagnik Basumallik, Vasavi Sivaramakrishnan, Ayan Mukhopadhyay, Anurag K. Srivastava, Abhishek Dubey

2023 IEEE International Conference on Smart Computing (SMARTCOMP) 2023

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Why This Matters

Reliable detection of grid events and anomalies is critical for maintaining power system stability and preventing cascading failures. Existing supervised approaches require extensive labeled datasets that are difficult to obtain in practice. This work is important because it demonstrates how unsupervised learning can automatically identify important features of grid events through wavelet analysis, enabling detection of diverse anomalies without labeled examples. The approach is practical for real-time grid monitoring applications.

What We Did

This paper presents an unsupervised machine learning approach for detecting anomalies in power transmission systems using wavelet-based feature extraction combined with convolutional autoencoders. The method processes phasor measurement unit data using discrete wavelet transforms to extract time-frequency features, which are then fed into an autoencoder for anomaly detection. The approach is validated on hardware-in-the-loop simulations and real IEEE 14-bus system data, achieving high detection accuracy without requiring labeled training data.

Key Results

The wavelet-convolutional autoencoder framework achieves 97.7% accuracy, 98% precision, and 99.5% recall on power system event detection tasks, substantially outperforming baseline approaches. The method successfully detects various types of grid events including faults and disturbances with minimal false positives. The unsupervised approach significantly reduces the burden of obtaining labeled training data, making it practical for deployment in operational grid monitoring systems.

Full Abstract

Cite This Paper

@inproceedings{Buckelew2023,
  author = {Buckelew, Jacob and Basumallik, Sagnik and Sivaramakrishnan, Vasavi and Mukhopadhyay, Ayan and Srivastava, Anurag K. and Dubey, Abhishek},
  booktitle = {2023 IEEE International Conference on Smart Computing (SMARTCOMP)},
  title = {Synchrophasor Data Event Detection using Unsupervised Wavelet Convolutional Autoencoders},
  year = {2023},
  acceptance = {31},
  pages = {326-331},
  abstract = {Timely and accurate detection of events affecting the stability and reliability of power transmission systems is crucial for safe grid operation. This paper presents an efficient unsupervised machine-learning algorithm for event detection using a combination of discrete wavelet transform (DWT) and convolutional autoencoders (CAE) with synchrophasor phasor measurements. These measurements are collected from a hardware-in-the-loop testbed setup equipped with a digital real-time simulator. Using DWT, the detail coefficients of measurements are obtained. Next, the decomposed data is then fed into the CAE that captures the underlying structure of the transformed data. Anomalies are identified when significant errors are detected between input samples and their reconstructed outputs. We demonstrate our approach on the IEEE-14 bus system considering different events such as generator faults, line-to-line faults, line-to-ground faults, load shedding, and line outages simulated on a real-time digital simulator (RTDS). The proposed implementation achieves a classification accuracy of 97.7%, precision of 98.0%, recall of 99.5%, F1 Score of 98.7%, and proves to be efficient in both time and space requirements compared to baseline approaches.},
  contribution = {lead},
  doi = {10.1109/SMARTCOMP58114.2023.00080},
  keywords = {power system monitoring, anomaly detection, wavelet analysis, autoencoders, unsupervised learning, phasor measurement units, grid events, real-time detection}
}
Quick Info
Year 2023
DOI 10.1109/SMARTCOMP58114.2023...
Keywords
power system monitoring anomaly detection wavelet analysis autoencoders unsupervised learning phasor measurement units grid events real-time detection
Research Areas
energy CPS ML for CPS
Search Tags

Synchrophasor, Data, Event, Detection, Unsupervised, Wavelet, Convolutional, Autoencoders, power system monitoring, anomaly detection, wavelet analysis, autoencoders, unsupervised learning, phasor measurement units, grid events, real-time detection, energy, CPS, ML for CPS, 2023, Buckelew, Basumallik, Sivaramakrishnan, Mukhopadhyay, Srivastava, Dubey