A smart overvoltage monitoring and hierarchical pattern recognizing system for power grid with HTS cables

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.