Journal
SENSORS
Volume 21, Issue 13, Pages -Publisher
MDPI
DOI: 10.3390/s21134466
Keywords
data-driven; power system; line trip fault; long sequence prediction; stacked-informer networks; gradient centralization
Funding
- National Natural Science Foundation of China [61663008, 62020106003]
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Monitoring electrical equipment and power grid systems is essential for power transmission and distribution, with the ability to predict faults and ensure safe operation. An improved data-driven method using a stacked-Informer network shows superior feature extraction and faster training, enhancing fault sequence prediction accuracy in real scenarios.
The monitoring of electrical equipment and power grid systems is very essential and important for power transmission and distribution. It has great significances for predicting faults based on monitoring a long sequence in advance, so as to ensure the safe operation of the power system. Many studies such as recurrent neural network (RNN) and long short-term memory (LSTM) network have shown an outstanding ability in increasing the prediction accuracy. However, there still exist some limitations preventing those methods from predicting long time-series sequences in real-world applications. To address these issues, a data-driven method using an improved stacked-Informer network is proposed, and it is used for electrical line trip faults sequence prediction in this paper. This method constructs a stacked-Informer network to extract underlying features of long sequence time-series data well, and combines the gradient centralized (GC) technology with the optimizer to replace the previously used Adam optimizer in the original Informer network. It has a superior generalization ability and faster training efficiency. Data sequences used for the experimental validation are collected from the wind and solar hybrid substation located in Zhangjiakou city, China. The experimental results and concrete analysis prove that the presented method can improve fault sequence prediction accuracy and achieve fast training in real scenarios.
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