Lightning arrester leakage current monitoring system and method based on multi-modal data fusion

Through the multimodal data fusion system and combined with the deep reinforcement learning algorithm, the problems of single data, insufficient environmental interference compensation and poor dynamic adaptability of the lightning arrester leakage current monitoring system are solved, and high-precision lightning arrester status diagnosis and life prediction are achieved, reducing the false alarm rate and extending the service life of the equipment.

CN120334798APending Publication Date: 2025-07-18SHANGHAI OUMIAO ELECTRIC INSPECTION CO LTD
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Patent Information

Application Number
CN202510587729.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-08
Publication Date
2025-07-18

AI Technical Summary

Technical Problem

The existing lightning arrester leakage current monitoring system has problems such as single data dimensions, insufficient environmental interference compensation, poor dynamic adaptability and difficulty in tracing the source of faults, resulting in low monitoring accuracy and high false alarm rate.

Method used

A multimodal data fusion system is adopted to achieve real-time diagnosis and life prediction of lightning arrester status by fusing multi-dimensional sensor data such as temperature and humidity, local discharge, rainfall and line electric field, and combined with deep reinforcement learning algorithms. The system includes a multimodal perception unit, an edge computing unit and a cloud analysis platform, and uses Lora and 5G dual-mode communication, HTTP and MQTT communication to interact and process data.

Benefits of technology

It significantly improves monitoring reliability under complex operating conditions, reduces false alarm rate, extends the service life of the equipment, improves monitoring accuracy and adaptability, and saves data transmission traffic and energy consumption.

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Abstract

The invention discloses a lightning arrester leakage current monitoring system based on multi-modal data fusion and a method thereof. The system comprises a multi-modal sensing unit, an edge calculation unit and a cloud analysis platform. The multi-mode sensing unit realizes data interaction with the edge computing unit through Lora and 5G dual-mode communication; and the edge computing unit realizes data interaction with the cloud analysis platform through HTTP and MQTT communication. The method has the following beneficial effects: 1, a deep coupling model of multi-dimensional environmental parameters and electrical characteristics is constructed, and the current powerful reinforcement learning algorithm with a memory enhancement mechanism is combined, so that the monitoring reliability under a complex working condition is remarkably improved; 2, the system has the functions of dynamic threshold adjustment, environmental interference compensation and equipment degradation prediction, the false alarm rate can be effectively reduced, the service life of equipment can be prolonged, and a new solution thought is provided for anti-interference of an online monitoring system;
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