Grid-connected fault comprehensive evaluation strategy for network-constructed hybrid energy storage power station
By combining the analytic hierarchy process (AHP), convolutional neural networks (CNNs), and long short-term memory (LSTM) networks, the accuracy and adaptability issues of grid-connected fault assessment for grid-connected hybrid energy storage power stations were resolved. This approach enabled dynamic weighted quantitative assessment of grid-connected faults, thereby improving both the accuracy and adaptability of the assessment.
CN121235488BActive Publication Date: 2026-07-24GUANGZHOU INST OF ENERGY CONVERSION CHINESE ACAD OF SCI
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Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- GUANGZHOU INST OF ENERGY CONVERSION CHINESE ACAD OF SCI
- Filing Date
- 2025-09-19
- Publication Date
- 2026-07-24
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Figure CN121235488B_ABST
Abstract
The application discloses a grid-connected fault comprehensive evaluation strategy for a network-constructed hybrid energy storage power station. By adopting a hybrid evaluation framework that fuses an analytic hierarchy process, a convolutional neural network, a long short-term memory network and an attention mechanism, the evaluation precision of a complex fault scene is improved while the model interpretability is maintained, and accurate quantification of the grid-connected fault severity of the network-constructed hybrid energy storage power station is realized.
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