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A Sample-Efficient OPF Learning Method Based on Annealing Knowledge Distillation
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DOI：10.1109/ACCESS.2022.3207146.png)
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Abstract
En 中文
To quickly respond to variations in the state of network load demand, a solution using data-driven techniques to predict optimal power flow (OPF) has emerged in recent years. However, most of the existing methods are highly dependent on large data volumes. This limits their application on the newly established or expanded systems. In this regard, this work proposes a sample-efficient OPF learning method to maximize the utilization of limited samples. By decomposing the OPF task before knowledge distillation, deep learning complexity is reduced. Thereafter, knowledge distillation is used to integrate decoupled tasks and improve accuracy in low-data setups. Unsupervised pre-training is introduced to alleviate the demand for labeled data. Additionally, the focal loss function and teacher annealing strategy are adopted to achieve higher accuracy without extra samples. Numerical tests on different systems corroborate the advanced accuracy and training speed over other training methods, especially on fewer-sample occasions.
Keywords:
Encoding
Knowledge engineering
Load modeling
Deep learning
Data models
Power generation
Annealing
Noise reduction
Optimal control
Annealing
Optimal power flow
sample efficiency
annealing knowledge distillation
focal loss function
stacked denoising autoencoder
deep learning
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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