Deep Reinforcement Learning for Load Shedding Against Short-Term Voltage Instability in Large Power Systems

Jingyi Zhang, Yonghong Luo, Boya Wang, Chao Lu, Jennie Si, Jie Song

Research output: Contribution to journalArticlepeer-review

Abstract

We introduce an innovative solution approach to the challenging dynamic load-shedding problem which directly affects the stability of large power grid. Our proposed deep Q-network for load-shedding (DQN-LS) determines optimal load-shedding strategy to maintain power system stability by taking into account both spatial and temporal information of a dynamically operating power system, using a convolutional long-short-term memory (ConvLSTM) network to automatically capture dynamic features that are translation-invariant in short-term voltage instability, and by introducing a new design of the reward function. The overall goal for the proposed DQN-LS is to provide real-time, fast, and accurate load-shedding decisions to increase the quality and probability of voltage recovery. To demonstrate the efficacy of our proposed approach and its scalability to large-scale, complex dynamic problems, we utilize the China Southern Grid (CSG) to obtain our test results, which clearly show superior voltage recovery performance by employing the proposed DQN-LS under different and uncertain power system fault conditions. What we have developed and demonstrated in this study, in terms of the scale of the problem, the load-shedding performance obtained, and the DQN-LS approach, have not been demonstrated previously.

Original languageEnglish (US)
JournalIEEE Transactions on Neural Networks and Learning Systems
DOIs
StateAccepted/In press - 2021

Keywords

  • Deep reinforcement learning (DRL)
  • Induction motors
  • Load shedding
  • Power system dynamics
  • Power system stability
  • Reactive power
  • short-term voltage stability (STVS)
  • spatial-temporal information fusion
  • Torque
  • under voltage load shedding.
  • Voltage

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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