Transient voltage evaluation method and system based on data enhancement and bidirectional gating circulation

By adopting the method of data augmentation and bidirectional gated cyclic network structure in the power system transient stability evaluation, the sensitivity of traditional evaluation models to data noise and sample imbalance is solved, and the accuracy and robustness of the evaluation are improved.

CN119719894APending Publication Date: 2025-03-28CHENGDU CHENGDIAN ELECTRIC POWER ENG DESIGN
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Patent Information

Application Number
CN202411770446.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-04
Publication Date
2025-03-28

AI Technical Summary

Technical Problem

The evaluation model of the traditional power system transient stability evaluation method is greatly affected by data noise and missing values, and is highly dependent on transient data. There are problems such as natural imbalance between the voltage instable sample and the voltage stable sample, resulting in defects in model accuracy.

Method used

The transient voltage evaluation method based on data augmentation and bidirectional gating cycle is adopted to enrich the diversity of samples by data augmenting the original data set, and a bidirectional gating cycle unit network based on attention mechanism is used to extract potential information to improve feature learning ability.

Benefits of technology

Through data enhancement and improvement of bidirectional gated cyclic network structure, the accuracy and robustness of the model for the evaluation of transient voltage in the power system are improved, and the tendency and misjudgment problems caused by sample imbalance are alleviated.

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Abstract

The invention discloses a transient voltage evaluation method and system based on data enhancement and bidirectional gating circulation. Relates to the technical field of power system transient evaluation. The method level and the network structure level are improved on the basis of a traditional power system transient stability evaluation technology, data enhancement is carried out on an original data set based on an improved deep learning network model, and the diversity of samples is enriched; meanwhile, in order to cope with inherent characteristics of multi-characteristic time sequence sample data, potential information is extracted in multiple directions by utilizing a transient voltage evaluation model of a bidirectional gating circulation unit network based on an attention mechanism, and the attention mechanism improves the characteristic learning ability by automatically distributing attention according to the importance of input information; and finally, based on an improved focus loss function, the problems of tendency and misjudgment caused by sample imbalance in the training process of the transient voltage stability evaluation model are relieved to a certain extent through a weight coefficient and a modulation factor.
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