Power Grid Data Quality Filter and Machine Learning for Event Classification

Conference: Symposium on Data Science and Statistics (SDSS) 2023
05/26/2023: 10:30 AM - 10:35 AM CDT
Lightning 

Description

The stability and reliability of the power grid are of great importance to the nation's economic system and national security. The power grid is a complex system that has many interconnected networks. With the advent of phasor measurement unit (PMU) data, system operators can view the status of the power system from a wide-area interconnection level. Since there are constant disturbances happening in the power grid, data analytics techniques could be valuable for applications on PMU data that inform the operators regarding interesting and significant power system events. In this study, we develop a data processing and machine learning approach that handles near-real-time PMU data for detecting and classifying power system events. This paper provides details regarding the techniques we use for filtering out common PMU data quality issues like frequency channel extreme values, locked frequency channels, missing data that leads to false spikes, and unreliable derived frequency. After data pre-processing, an atypicality engine is used to flag atypical minutes in the PMU data. The atypicality score is mainly based on principal component analysis and clustering. Also presented is a machine learning classifier using gradient boosting machine (GBM) that distinguishes between generator trips and other types of power system events since generator trips are usually more significant than the other events for system operators to take notice. This classifier works on extracted features from the time series PMU channels like frequency and phase angle difference. Among the other types of power system events, we also develop metrics based on k-means clustering for the characterization of these events in order to discover interesting events. There are six metrics created that focus on sudden changes and gradual shifting in the data. Testing is conducted on real world PMU data showing that the approach works as expected and achieves satisfactory results.

Keywords

PMU

Machine Learning

Event Classification 

Presenting Author

Tianzhixi Yin, Pacific Northwest National Laboratory

First Author

Tianzhixi Yin, Pacific Northwest National Laboratory

CoAuthor(s)

Nick Betzsold, Pacific Northwest National Laboratory
James Follum, Pacific Northwest National Laboratory
Shuchismita Biswas, Pacific Northwest National Laboratory

Target Audience

Mid-Level

Tracks

Machine Learning
Symposium on Data Science and Statistics (SDSS) 2023