| 引用本文: | Yaojian Wang,Jie Gu,Lyuzerui Yuan.[J].电力系统保护与控制,2023,(2):508-523.[点击复制] |
| Yaojian Wang,Jie Gu,Lyuzerui Yuan.Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling[J].Power System Protection and Control,2023,(2):508-523[点击复制] |
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| DOI：10.1186/s41601-023-00306-w |
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| 基金项目:This work was supported in part by the National Key Research Program of
China (2016YFB0900100) and Key Project of Shanghai Science and Technology
Committee (18DZ1100303). |
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| Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling |
| Yaojian Wang,Jie Gu,Lyuzerui Yuan |
| () |
| Abstract: |
| Because there is insufcient measurement data when implementing state estimation in distribution networks, this
paper proposes an attention-enhanced recurrent neural network (A-RNN)-based pseudo-measurement modeling
metho. First, based on analyzing the power series at the source and load end in the time and frequency domains,
a period-dependent extrapolation model is established to characterize the power series in those domains. The
complex mapping functions in the model are automatically represented by A-RNNs to obtain an A-RNNs-based
period-dependent pseudo-measurement generation model. The distributed dynamic state estimation model of the
distribution network is established, and the pseudo-measurement data generated by the model in real time is used
as the input of the state estimation model together with the measurement data. The experimental results show that
the method proposed can explore in depth the complex sequence characteristics of the measurement data such that
the accuracy of the pseudo-measurement data is further improved. The results also show that the state estimation
accuracy of a distribution network is very poor when there is a lack of measurement data, but is greatly improved by
adding the pseudo-measurement data generated by the model proposed. |
| Key words: s State estimation, Pseudo measurement, Recurrent neural network, Attention mechanism, Time-frequency
domain analysis, Distribution network |
