Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Deep Reinforcement Learning for Electric Transmission Voltage Control (2006.06728v2)

Published 11 Jun 2020 in cs.LG, cs.SY, eess.SP, eess.SY, and stat.ML

Abstract: Today, human operators primarily perform voltage control of the electric transmission system. As the complexity of the grid increases, so does its operation, suggesting additional automation could be beneficial. A subset of machine learning known as deep reinforcement learning (DRL) has recently shown promise in performing tasks typically performed by humans. This paper applies DRL to the transmission voltage control problem, presents open-source DRL environments for voltage control, proposes a novel modification to the "deep Q network" (DQN) algorithm, and performs experiments at scale with systems up to 500 buses. The promise of applying DRL to voltage control is demonstrated, though more research is needed to enable DRL-based techniques to consistently outperform conventional methods.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (2)
  1. Brandon L. Thayer (1 paper)
  2. Thomas J. Overbye (11 papers)
Citations (10)

Summary

We haven't generated a summary for this paper yet.