Distributed energy cluster state perception method and system fusing multi-modal learning

By using a multimodal learning method, multi-dimensional data from distributed energy clusters are collected and fused in real time. An adaptive baseline interval and transient operating condition perception operator are constructed, which solves the problems of misjudgment and lag under dynamic operating conditions in existing technologies and realizes the requirements of high-precision, millisecond-level state perception and scheduling.

CN122332877APending Publication Date: 2026-07-03NANJING CORNERSTONE DATA TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NANJING CORNERSTONE DATA TECH CO LTD
Filing Date
2026-06-05
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Existing distributed energy cluster status sensing technologies cannot match changes in equipment operating conditions under dynamic operating conditions, resulting in misjudgments, missed judgments, and sensing lags. They cannot meet real-time scheduling requirements, have low sensing accuracy, and lack multimodal data fusion and adaptive dynamic correction capabilities.

Method used

A multimodal learning approach is adopted to collect four-dimensional multimodal data in real time, construct a multimodal coupled feature mapping model, generate an adaptive benchmark interval for operating conditions, extract transient features to construct a transient operating condition perception operator, and combine the benchmark interval and the transient operating condition perception operator to perform state fusion and discrimination.

Benefits of technology

It achieves full-condition adaptive dynamic matching, eliminates perception lag, improves the accuracy and response speed of state perception, meets real-time scheduling requirements, and enhances the system's operational stability and accuracy.

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Abstract

This invention provides a distributed energy cluster state perception method and system that integrates multimodal learning, belonging to the field of state perception technology. It generates a dynamic operating condition baseline range in real time through multimodal fusion features. The judgment boundary can be synchronously and adaptively updated according to equipment operating conditions, environmental conditions, load fluctuations, and changes in renewable energy output. This effectively matches the highly time-varying, highly fluctuating, and nonlinear dynamic operating characteristics of distributed energy clusters, eliminating misjudgments and omissions caused by fixed thresholds, and significantly improving the matching accuracy across all operating conditions. It eliminates the perception lag in scenarios of sudden load changes, achieving millisecond-level real-time state perception. It uses a transient operating condition perception operator to capture transient operating condition features, eliminating the need for the traditional sliding window data accumulation process and completely eliminating perception delay. It can accurately capture transient operating condition changes such as sudden load changes, sudden changes in renewable energy output, and equipment start-up and shutdown, fully meeting the engineering requirements for real-time scheduling and rapid fault early warning of distributed energy clusters.
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