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 language | English (US) |
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Pages (from-to) | 4249-4260 |
Number of pages | 12 |
Journal | IEEE Transactions on Neural Networks and Learning Systems |
Volume | 34 |
Issue number | 8 |
DOIs | |
State | Published - Aug 1 2023 |
Keywords
- Deep reinforcement learning (DRL)
- short-term voltage stability (STVS)
- spatial-temporal information fusion
- under voltage load shedding
ASJC Scopus subject areas
- Software
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications