Abstract
The increasing uncertain components of power systems foster the wide applications of Machine Learning (ML) techniques. While traditional ML models demand a large set of data, data-scarce dilemmas exist for new meters, devices, and new grids. Further, for rich historical measurements, valuable data may still be limited, especially for targets like identifying system events that rarely occur in the power system. To enhance the event type differentiation and localization for a data-limited grid, we propose a Transfer Learning (TL) framework to transfer knowledge from a data-rich grid (source grid) to the target grid, using measurements from Phasor Measurement Units (PMUs). The transferring process is challenging because of (1) high-volume data with redundant information, (2) different measurement dimensionalities, (3) dissimilar data distributions, and (4) disjoint event-location-label spaces for two grids. To handle the challenges of (1) to (3), we propose a joint optimization to reduce dimensionality and maximize common knowledge in a shared low-dimensional feature space, where the commonality lies in the same dimensions and close data distributions. Such an optimization-based procedure is verified via rigid mathematical theorems given the same label space, i.e., event-type-label space. However, for event localization, challenge (4) obstructs the optimization. Therefore, we design a label space alignment method to relabel the event location by the event zone location and build an event zone estimation problem. Then, the framework is generalized to both tasks. Finally, comprehensive experiments demonstrate the advantages of the proposed methods over state-of-the-art transfer learning models.
Highlights
Power systems are dramatically integrating highly uncertain components for cleaner, more efficient, and lowercost energy generations and consumption
We aim to propose a general Transfer Learning (TL) framework for power system transfer learning with both strong theoretical foundations and high generalizability
We focus on event type differentiation and localization, these two tasks are representative for situations of data space homogeneity/heterogeneity and label space identicalness/disjointing
Summary
Power systems are dramatically integrating highly uncertain components for cleaner, more efficient, and lowercost energy generations and consumption. To enhance the system monitoring under changing operating points, Machine Learning (ML) methods are more and more popular due to their high capacity of extracting informative features from dynamic data streams. This advantage is especially expanded with the increasing deployment of Phasor Measurement Units (PMUs) to provide high-resolution phasor data. PMU-based ML models achieve great success in state estimation [1], event identification [2]–[4], resilience improvement [5], and cyber-attack detection [6], etc These applications, strictly assume that there are enough data for training
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