Energy storage power station abnormity alarm method, device and equipment based on multi-modal data

Through the feature fusion and dimensionality reduction processing of multimodal data, combined with the abnormal monitoring model, the problem of energy storage power station monitoring relying on single mode data is solved, and more accurate and comprehensive abnormal alarms are achieved, which improves the safety of energy storage power stations.

CN119669959APending Publication Date: 2025-03-21CHINA YANGTZE POWER +1
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Patent Information

Application Number
CN202411633504.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-15
Publication Date
2025-03-21

AI Technical Summary

Technical Problem

Energy storage power stations face many safety risks during operation. Traditional monitoring systems rely on single-modal data sources, which can easily lead to false alarms or missed alarms, and cannot fully reflect the true status of energy storage power stations.

Method used

The abnormal alarm method of energy storage power stations is adopted based on multimodal data. By acquiring the data of multiple data acquisition devices, resampling frequency alignment processing, extracting and fusion characteristics, and dimensionality reduction processing are performed, and the abnormality monitoring model is finally input to generate an alarm signal.

Benefits of technology

It realizes comprehensive monitoring of abnormal operation of energy storage power plants, improves energy use safety, reduces the occurrence of false alarms and underreports, and ensures the accuracy of abnormal detection.

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Abstract

The invention relates to the technical field of energy storage, and discloses an energy storage power station abnormity alarm method, device and equipment based on multi-modal data, and the method comprises the steps: obtaining the original sampling data of an energy storage power station, and carrying out the sampling frequency alignment processing of the original sampling data based on the resampling frequency, so as to obtain the multi-modal data; performing feature extraction and fusion processing on the multi-modal data to obtain a comprehensive feature vector, and performing dimension reduction processing on the comprehensive feature vector to obtain a key feature vector; and inputting the key feature vector into a pre-trained anomaly monitoring model, and generating an alarm signal under the condition that the energy storage power station is monitored to run abnormally according to an output result of the anomaly monitoring model. The method has the beneficial effects that the multi-modal data of different data sources are kept consistent in the sampling time dimension, the availability of the multi-modal data is improved, and the accuracy of subsequent feature extraction and analysis is also ensured.
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