Defects Identification of Partial Discharge Map in GIS

Yi Long Zhang; Yi Hui Zheng; Li Xue Li; Xin Wang; Gang Yao; Yang Zhang

doi:10.4028/www.scientific.net/AMM.448-453.1947

Paper Titles

Research on Mathematical Model of Nuclear Power Plant for Different Processes in the Power System Simulation
p.1920

A Method and Analysis for Improving Thermal Efficiency of Nuclear Power Plant
p.1928

Fault Diagnosis of Grounding Grid Based on Self-Adaptive Particle Swarm Optimization
p.1937

Study on Interactions of DFACTS Devices Operating in Distribution System
p.1941

Defects Identification of Partial Discharge Map in GIS
p.1947

Calculation of Lighting-Induced Overvoltage on Transmission Line Based on Simplified Wagner Field Cancellation Method
p.1951

Pattern Classification for GIS Base on GK Clustering Algorithm
p.1955

Reassigned Time Frequency Analysis for PD Signals in GIS
p.1959

Study of GIS PD Defect Classification Based on Adaptive Affinity Propagation Clustering
p.1963

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 448-453Defects Identification of Partial Discharge Map in...

Defects Identification of Partial Discharge Map in GIS

Abstract:

With GIS being widely used, partial discharge detecting and defect pattern recognition become more and more meaningful and important. To realize defects identification of partial discharge map in GIS, a novel method based on Radical Basis Function (RBF) neural network is proposed. Firstly, a model is constructed to simulate the discharge pattern map by the use of random function randint. Secondly, based on the model above, a lot of data which meet the condition can be collected to provide for pattern recognition. Then, a RBF network is introduced to identify the pattern recognition. It can be trained by using the data above. Finally, through changing training error, high correct rate can be got. These indicate that the method is effective.