iEDS Intelligent Expert Diagnosis System

Description

The iEDS Intelligent Expert Diagnosis System serves as a professional "physician" for switchgear. It integrates existing diagnostic knowledge, diagnostic strategy analysis methods, industry experts' experience, and massive fault data from test stations, automatically realizing intelligent diagnosis, life prediction, and operation and maintenance guidance for switchgear. This enhances the stability and reliability of switch products put into power grid operation.

Technical parameter

• iEDS intelligent expert diagnosis system is a professional doctor for switchgear, which integrates existing diagnostic knowledge, diagnostic strategy analysis methods, industrial experts' experience, as well as a large number of test station fault data, to automatically realize intelligent diagnosis, service life prediction and operation & maintenance guidance of switchgear and to improve the operating stability and reliability of switchgear products in power grids. 

Features

• iThe iEDS Intelligent Expert Diagnosis System equips switchgear with various sensors to collect temperature, current and operating curves. It integrates professional diagnostic methods and theories, along with switchgear multi-dimensional data and industry equipment fault big data, enabling real-time self-diagnosis and automatic fault identification for medium-voltage switchgear. It analyzes potential faults early, issues warnings and provides solutions to prevent fault escalation and reduce unplanned power outages.
• For existing faults, the system accurately identifies causes and locations, delivers effective solutions to cut expert dispatch costs, shorten fault clearance time and restore power quickly.
• Via IoT communication, it pushes diagnosis results and O&M strategies to clients for integrated remote monitoring and diagnosis—lowering fault diagnosis technical requirements, preventing equipment failures and extending service life. It also updates the fault database through self-learning to improve the accuracy of fault analysis, health monitoring and life prediction.