Machine Learning (ML) is gaining increasing popularity to tackle uncertainty in physical systems, such as modern power systems. However, ML models can be hardly trained for newly- built power grids with limited data, especially when different power grids have different dimensionalities and distributions for measurement data. To tackle this problem, we propose a novel Heterogeneous Transfer Learning (HTL)-based method to boost the data volume of the target grid. Specifically, we propose a Merged Multi-Modal Gaussian Graphical Model (M^3G^2M) with a physical data merging process for knowledge transfer. To solve the maximum likelihood estimation of M^3G^2M with imbalanced data from two grids, we propose a novel Expectation-Maximization algorithm.

Finally, we quantify the negative transfer via the KL-Divergence to measure the distribution similarity between the source grid and the target grid for the transferring confidence. We demonstrate the advantages and the generalizability of our proposed
models in diversified data sets for power systems and human action-sensing systems.

Bio