Detection of incipient faults in power distribution network (LSTM/Adaptive Wavelet trasnform/Attention).
- Python 3.6
- Tensorflow-gpu 1.14.0
- Keras 2.25
Run command below to train and test the model:
python test_tf.py
Datasets are obtained from a small Incipient Fault dataset in Power Distribution (IFPD) system from [1] (https://dx.doi.org/10.21227/bwjy-7e05), and a relatively large dataset logged by State Grid Corporation of China in AnHui Province (SGAH) from [2] (https://github.com/smartlab-hfut/SGAH-datasets.git).
device: Tesla V100
dataset: IFPD and SGAH
optimizer: Adam(lr=0.001, eps=1e-08)
batch:800
These are the result for the incipient fault detection in two datasets.
Metrics | Accuracy | Precision | Recall | F1score |
---|---|---|---|---|
IFPD | 0.97 | 0.97 | 0.96 | 0.96 |
SGAH | 0.99 | 0.97 | 0.98 | 0.98 |
Fig.1 ROC of AD-TFM-AT model on IFPD.
Fig.2 ROC of AD-TFM-AT model on SGAH.
If you use the codes or the datasets, please cite the following papers:
@unknown{unknown,
author = {Li, Qiyue and Deng, Yuxing and Liu, Xin and Sun, Wei and Li, Weitao and Li, Jie and Liu, Zhi},
year = {2022},
month = {05},
pages = {},
title = {Autonomous Smart Grid Fault Detection},
doi = {10.48550/arXiv.2206.14150}
}
@article{li2022resource,
title={Resource Orchestration of Cloud-edge based Smart Grid Fault Detection},
author={Li, Jie and Deng, Yuxing and Sun, Wei and Li, Weitao and Li, Ruidong and Li, Qiyue and Liu, Zhi},
journal={ACM Transactions on Sensor Networks (TOSN)},
year={2022},
publisher={ACM New York, NY}
}
See LICENSE for details.