Heterogeneous transfer learning on power systems: A merged multi-modal gaussian graphical model

Haoran Li, Yang Weng, Hanghang Tong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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{3}G{2}M) with a physical data merging process for knowledge transfer. To solve the maximum likelihood estimation of M{3}G{2}M 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.

Original languageEnglish (US)
Title of host publicationProceedings - 20th IEEE International Conference on Data Mining, ICDM 2020
EditorsClaudia Plant, Haixun Wang, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1088-1093
Number of pages6
ISBN (Electronic)9781728183169
DOIs
StatePublished - Nov 2020
Event20th IEEE International Conference on Data Mining, ICDM 2020 - Virtual, Sorrento, Italy
Duration: Nov 17 2020Nov 20 2020

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2020-November
ISSN (Print)1550-4786

Conference

Conference20th IEEE International Conference on Data Mining, ICDM 2020
CountryItaly
CityVirtual, Sorrento
Period11/17/2011/20/20

Keywords

  • Expectation-Maximization Algorithm
  • Heterogeneous Transfer Learning
  • Maximum Likelihood Estimation
  • Merged Multi-Modal Gaussian Graphical Model

ASJC Scopus subject areas

  • Engineering(all)

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