Direct lightning strokes account for a large proportion of line faults. Lightning faults can be classified into several kinds, and the corresponding protection methods differ. The type of these lightning faults should be identified to establish targeted measures against lightning and improve lightning protection design. In this study, a non-contact multi-physical parameter lightning monitoring system is proposed. The voltage of the insulator string and the lightning grounding current of the transmission tower are chosen as two physical parameters to show the characteristic quantities of different lightning stroke types. These physical parameters are captured using a non-contact overvoltage sensor installed at the cross arm of the tower and several parallel Rogowski coils installed at the tower ground supports. An equivalent electromagnetic transient model of a 110 kV transmission line is developed to identify features of the signals under different lightning strokes. Based on time-domain and wavelet transform modulus maxima (WTMM) analyses, the polarity of insulator voltage, the polarity of tower current, and the mutation polarity of tower current when insulators flashover are extracted as characteristic quantities of polarity discrimination. Six kinds of direct lightning strokes can be identified based on the polarity discrimination method considering the lightning stroke point and the lightning current polarity. The identification method is verified by simulation data and application to an actual example.
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.
According to field operation records, lightning stroke accounts for 60% of transmission line failures. Therefore, it is of great significance to strengthen lightning protection of the power system. However, there are several lightning faults, and the corresponding protection methods differ. Consequently, identifying lightning stroke faults will be beneficial to take corresponding lightning protection measures. This paper investigates the mechanism of different lightning strike faults, and simulates them by a 110kV transmission line EMTP-ATP model. Analysis and simulation show that the direction of tower current represents lightning’s polarity; the insulator voltage’s direction differs when shielding failure or back striking occurs. If insulator flashovers, the voltage of the insulator drops down to zero, and as the transient process comes to an end, the voltage of the insulator on the nearby tower decreases to zero as well; after the occurrence of back striking flashover, the direction of insulator voltage on nearby tower alters. Based on those features, insulator voltage and tower current are introduced as a characteristic signal, and their direction and rms of them are formed as recognition parameters for lightning stroke identification. The EMTP-ATP simulations demonstrate that the proposed method is correct and effective, and the recognition rate of different lightning faults is 100% under the abovementioned method.
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