UHF Sensor Array for Partial Discharge Location in GIS
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Solution Overview
Problem
Current Partial Discharge Monitoring (PDM) systems in Gas Insulated Switchgear (GIS) require manual site visits and cannot automatically detect or locate arcs, limiting their effectiveness in addressing insulation breakdowns and potential safety hazards.
Innovation Solution
Implementing an online monitoring system that uses ultra-high frequency (UHF) sensors and artificial intelligence/machine learning to detect and classify electrical discharges, automatically determining the location of partial discharges and arcs by analyzing signal patterns and attenuation profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual site visits with fast oscilloscope and ToF calculations are used to locate partial discharge, then location accuracy is achieved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary calibration by storing the physical locations of multiple sensors and pre-calculating signal propagation characteristics before actual discharge detection occurs. This preparation enables automatic location calculation without requiring manual site visits or real-time ToF calculations, thus resolving the contradiction between measurement precision and time consumption
Solution Approach 2:
The patent replaces manual mechanical measurement processes (site visits with oscilloscope and ToF calculations) with an automated electronic system that uses signal processing algorithms to automatically determine discharge locations based on sensor data, eliminating the need for manual intervention while maintaining location accuracy
2Extent of automation
If current PDM systems are used for partial discharge detection, then PD presence is identified, but arc detection and automatic location capability are lost
Solution Approach 1:
The system is designed with multi-functional capability to detect and analyze multiple types of electrical discharges (both partial discharge and arc faults) using the same sensor array and processing system. The signal processing algorithms can distinguish between different discharge types and automatically determine locations for both, achieving both automation and reliability simultaneously
3Measurement precision
If multiple sensors are deployed for discharge location determination, then location precision improves, but system complexity and cost increase
Solution Approach 1:
The system segments the GIS structure into multiple zones with sensors strategically positioned at key locations rather than uniformly distributed. This segmentation approach achieves sufficient location precision for each zone while minimizing the total number of sensors required, thus reducing system complexity while maintaining measurement precision
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables quick and accurate identification of discharge locations, simplifying maintenance planning, reducing downtime, and ensuring safety by automatically isolating faulty sections and reenergizing healthy ones, thus preventing extensive power outages and maintaining GIS equipment longevity.
Implementation Method 1
The electrical discharge, such as a partial discharge or an arc, generates an ultra-high frequency (UHF) signal
Data Source
AI summary
A system and method of automatically detecting electrical discharges in a gas insulated switchgear equipment and locating the electrical discharges in real-time is disclosed. The method includes sensing an ultra high frequency (UHF) signal generated from the electrical discharges at multiple channels simultaneously. The UHF signal sensed at multiple channels are reviewed simultaneously to determine whether they are of the same electrical discharge or not. The method also provides the sensed UHF signal at multiple channels to an artificial intelligence training model trained to determine the type of electrical discharge such as a partial discharge or an arc. The method provides a location of the electrical discharge based on an attenuation profile associated with the multiple channels and other information including amplitude information and distance information between adjacent channels.