Deep Learning based Model-free Robust Load Restoration to Enhance Bulk System Resilience with Wind Power Penetration

Jin Zhao, Fangxing Fran Li, Xi Chen, Qiuwei Wu

Research output: Contribution to journalJournal articleResearchpeer-review

4 Downloads (Pure)


This paper proposes a new deep learning (DL) based model-free robust method for bulk system on-line load restoration with high penetration of wind power. Inspired by the iterative calculation of the two-stage robust load restoration model, the deep neural network (DNN) and deep convolutional neural network (CNN) are respectively designed to find the worst-case system condition of a load pickup decision and evaluate the corresponding security. In order to find the optimal result within a limited number of checks, a load pickup checklist generation (LPCG) algorithm is developed to ensure the optimality. Then, the fast robust load restoration strategy acquisition is achieved based on the de-signed one-line strategy generation (OSG) algorithm. The pro-posed method finds the optimal result in a model-free way, holds the robustness to handle uncertainties, and provides real-time computation. It can completely replace conventional robust optimization and supports on-line robust load restoration which better satisfies the changeable restoration process. The effectiveness of the proposed method is validated using the IEEE 30-bus system and the IEEE 118-bus system, showing high computational efficiency and considerable accuracy.
Original languageEnglish
JournalIEEE Transactions on Power Systems
Number of pages10
Publication statusAccepted/In press - 2022


  • Convolutional neural network (CNN)
  • Convolutional neural networks
  • Deep learning (DL)
  • Load modeling
  • Load restoration
  • Mathematical models
  • Optimization
  • Power system resilience
  • Uncertainty
  • Voltage
  • Wind power generation
  • Wind power integration


Dive into the research topics of 'Deep Learning based Model-free Robust Load Restoration to Enhance Bulk System Resilience with Wind Power Penetration'. Together they form a unique fingerprint.

Cite this