Centralized control system and method for intelligent power distribution station
By establishing a communication network between power distribution rooms and constructing a multi-node collaborative monitoring system, the reliability issues of monitoring and operation and maintenance in scenarios involving aging equipment and collaborative monitoring of multiple power distribution room nodes were resolved. This enabled efficient and accurate detection and early warning of abnormal data, thereby improving operation and maintenance efficiency and fault handling capabilities.
Patent Information
- Application Number
- CN202510920387.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-04
- Publication Date
- 2025-08-01
- Estimated Expiration
- 2045-07-04
AI Technical Summary
Existing technologies have low reliability in monitoring and maintenance scenarios involving aging equipment or collaborative monitoring of multiple power distribution room nodes. They are unable to accurately identify progressive abnormal data and regional power supply anomalies. Furthermore, the lack of data collaboration mechanisms in distributed deployment scenarios with multiple power distribution rooms increases the risk of missed detections.
By establishing a communication network between various power distribution rooms, a time series feature extraction model and a neural network fusion operation prediction model are constructed to achieve short-term and long-term predictions. Combined with multi-node data cross-validation and a distributed computing architecture, the aging feature weights are dynamically updated to conduct collaborative analysis of the aging status of multi-node devices. Furthermore, adjacent nodes assist in handling computing and transmission bottlenecks, thereby optimizing the scheduling of operation and maintenance resources and data transmission.
It improves the accuracy and processing speed of abnormal data detection, reduces the misjudgment rate and decision-making error rate, enhances operation and maintenance efficiency and fault handling efficiency, and meets the real-time early warning needs of the power distribution network.