A Data-Driven Method for Power System Emergency Control to Improve Short-Term Voltage Stability. Issue 1 (1st February 2023)

• Record Type: Journal Article
• Authors: Li, Meng; Lin, Zhangsui; Lin, Yi; Tang, Yuchen; Jiang, Changxu; Liu, Chenxi; Shao, Zhenguo
• Is Part Of: Journal of physics. Volume 2433 Issue 1(2023)
• Publication Date: 2023-02-01
• Subjects: Deep reinforcement learning -- data-driven -- edge-conditioned graph convolutional network -- load shedding -- short-term voltage stability; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2433/1/012016
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The power system contains a variety of uncertainties of different types of sources and loads, as well as random contingencies. Under these uncertainties and rapidly changing operating conditions, traditional rule-based methods cannot dynamically handle short-term voltage instability. To alleviate this situation, this paper proposes a novel power system emergency control scheme using a data-driven method, which combines edge-conditioned graph convolutional networks and deep reinforcement learning. The edge-conditioned graph convolutional network is utilized to extract the characteristics from not only power system nodes but also transmission lines. Deep reinforcement learning is introduced to perform load shedding actions, so as to guarantee the safety and stability of the electric power system. The IEEE 39-bus network is utilized for simulations to validate the effectiveness of the proposed data-driven method. The outcomes demonstrate the proposed method can generate a superior strategy in a number of the short-term voltage instability(STVI) circumstances.