Forecasting-aided state estimation based on deep learning for hybrid AC/DC distribution systems

Manyun Huang, Zhinong Wei, Yuzhang Lin

Research output: Contribution to journalArticlepeer-review


To accommodate a higher penetration of distributed energy resources, distribution systems are moving toward hybrid AC/DC configurations for secure and economic operation. In this regard, this paper proposes a forecasting-aided state estimator (FASE) for hybrid AC/DC distribution systems to obtain accurate estimates for online security monitoring and control. The proposed FASE is designed in a distributed framework, with decomposition into several subproblems and solution by a constrained ensemble Kalman filter algorithm. In the proposed methodology, a deep neural network-based state forecasting model is developed to imitate the complex temporal and spatial relationship between system states, avoiding the state transition model built by unfounded explicit formulations. Furthermore, smart meter data is integrated by deep regression learning to obtain power injections of consumers and address the system observability issue. Extensive comparisons with two alternatives are carried out on a sample 33-node hybrid AC/DC distribution system to show the effectiveness and benefits of the proposed FASE, and on a larger 106-node hybrid AC/DC distribution system to demonstrate scalability.

Original languageEnglish (US)
Article number118119
JournalApplied Energy
StatePublished - Jan 15 2022


  • Deep learning ensemble Kalman filter
  • Forecasting-aided state estimation
  • Hybrid AC/DC distribution systems

ASJC Scopus subject areas

  • Mechanical Engineering
  • General Energy
  • Management, Monitoring, Policy and Law
  • Building and Construction
  • Renewable Energy, Sustainability and the Environment


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