  Insights in power grid pixel maps (PGPMs) refer to important facility
operating states and unexpected changes in the power grid. Identifying insights
helps analysts understand the collaboration of various parts of the grid so
that preventive and correct operations can be taken to avoid potential
accidents. Existing solutions for identifying insights in PGPMs are performed
manually, which may be laborious and expertise-dependent. In this paper, we
propose an interactive insight identification and annotation framework by
leveraging an enhanced variational autoencoder (VAE). In particular, a new
architecture, DenseU-Hierarchical VAE (DUHiV), is designed to learn
representations from large-sized PGPMs, which achieves a significantly tighter
evidence lower bound (ELBO) than existing Hierarchical VAEs with a Multilayer
Perceptron architecture. Our approach supports modulating the derived
representations in an interactive visual interface, discover potential insights
and create multi-label annotations. Evaluations using real-world PGPMs datasets
show that our framework outperforms the baseline models in identifying and
annotating insights.
