Defect Pattern Recognition Based on Partial Discharge Characteristics of Oil-Pressboard Insulation for UHVDC Converter Transformer

Wen Si,Simeng Li,Yalin Shi,Tongqiao Zhang,Qingquan Li,Huaishuo Xiao

doi:10.3390/en11030592

Wen Si, Simeng Li + Show 4 more

Open Access

https://doi.org/10.3390/en11030592

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Journal: Energies	Publication Date: Mar 8, 2018
Citations: 9	License type: CC BY 4.0

Affiliation: Shandong University, Shanghai Electric (China)

Abstract

The ultra high voltage direct current (UHVDC) transmission system has advantages in delivering electrical energy over long distance at high capacity. UHVDC converter transformer is a key apparatus and its insulation state greatly affects the safe operation of the transmission system. Partial discharge (PD) characteristics of oil-pressboard insulation under combined AC-DC voltage are the foundation for analyzing the insulation state of UHVDC converter transformers. The defect pattern recognition based on PD characteristics is an important part of the state monitoring of converter transformers. In this paper, PD characteristics are investigated with the established experimental platform of three defect models (needle-plate, surface discharge and air gap) under 1:1 combined AC-DC voltage. The different PD behaviors of three defect models are discussed and explained through simulation of electric field strength distribution and discharge mechanism. For the recognition of defect types when multiple types of sources coexist, the Random Forests algorithm is used for recognition. In order to reduce the computational layer and the loss of information caused by the extraction of traditional features, the preprocessed single PD pulses and phase information are chosen to be the features for learning and test. Zero-padding method is discussed for normalizing the features. Based on the experimental data, Random Forests and Least Squares Support Vector Machine are compared in the performance of computing time, recognition accuracy and adaptability. It is proved that Random Forests is more suitable for big data analysis.

Highlights

With the rapid growth of electricity consumption, in many countries and regions around the world, there is an imbalance between electricity load and power generation
In order to study the defect pattern recognition based on Partial discharge (PD) characteristics, the PD characteristics of oil-pressboard insulation with three defect models under 1:1 combined AC-DC voltage is described in this paper
Random Forests (RF) is used for PD pattern recognition

Summary

Introduction

With the rapid growth of electricity consumption, in many countries and regions around the world, there is an imbalance between electricity load and power generation. The advantages of the UHVDC transmission system in terms of high capacity and low loss are evident when the energy should be delivered from the remote power generation areas to the load-intensive areas over long distance. In countries such as China and Brazil which are large in area and have uneven population distribution, in addition to fossil-fuel power generation, new energy sources such as wind and solar power generation bases are located far away from large cities, the development of UHVDC transmission system is more rapid in these places [1,2]. The PD behavior of typical defects in oil-pressboard insulation under combined AC-DC voltage and the application ways to reflect the insulation state of the converter transformer are significant topics

Results

Discussion

Conclusion