Abstract
As one part of the power system, high-temperature superconducting (HTS) cables may be subject to various system faults, such as overvoltage. When overvoltage occurs, HTS cables may quench and the resistance of HTS tapes will increase rapidly, which will result in reduction of transmission capacity, increase of power loss and even electrical insulation breakdown. To protect the operation safety of power system, the level of overvoltage should be investigated in the system. This paper proposes a non-contact variable frequency sampling and hierarchical pattern recognizing system for overvoltage. Lightning and internal overvoltage signals are captured by specially designed non-contact voltage sensors. The sensors are installed at the grounding tap of transformer bushings and the cross arm of transmission towers. A variable sampling technique is employed to solve the conflict between sampling speed and storage capacity. A hierarchical pattern recognizing system is proposed to subdivide each overvoltage into specific types. Seven common overvoltages are discussed and analyzed. Wavelet theory and S-transform singular value decomposition (SVD) theory are adopted to extract the feature parameters of different overvoltages. Particle swarm optimization is employed to maintain a high classification rate and improve the initial set of the support vector machine (SVM) used as recognition algorithm. Field-acquired overvoltage data from an 110 kV substation validate the effectiveness of the proposed recognition system.
Highlights
High-temperature superconductor (HTS) power technique has achieved rapid progress in the last decade, and is one of the most promising power techniques [1]
high-temperature superconducting (HTS) cables and conventional transmission lines are in parallel connection
The power flow distribution of conventional transmission lines and HTS cables can be adjusted by means of auxiliary control equipment, in order to meet the test requirements of different operating conditions of HTS cables
Summary
High-temperature superconductor (HTS) power technique has achieved rapid progress in the last decade, and is one of the most promising power techniques [1]. The HTS cable, as well as the conventional transmission line, always suffers from the problems of lightning and internal overvoltage, which are crucial for the security of power grids. Overvoltage from parallel connected transmission lines will affect the HTS cable which will reduce the stability and reliability of the power grid. The modification of this single-layer system is difficult because the new modified feature parameters are hard to find To address such limitations, this paper proposes a smart overvoltage monitoring and hierarchical pattern recognizing system based on non-contact sensors. The energy distribution characteristics of different overvoltage frequency components observed by WT and singular feature parameters observed by S-transform SVD theory are employed as feature parameters in the classifiers. In this optimization hierarchical structure, are employed feature parameters in the classifiers.
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