Traveling wave inversion method of power line faults. (July 2023)

• Record Type: Journal Article
• Authors: Xia, Yixiang; Li, Zewen; Wang, Shuai; Sun, Jian; Guo, Xinyu
• Is Part Of: International journal of electrical power & energy systems. Volume 149(2023)
• Publication Date: 2023-07
• Subjects: Waveform inversion -- Incident wave -- Substation -- Particle swarm optimization algorithm; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2023.109025
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: In this paper, an incident wave inversion model based on substation components is proposed, which realizes the accurate inversion of incident wave signal in substation. A denoising and extraction method of mixed wave based on improved EMD and FFT was proposed to filter noise out and obtain the power frequency signal under the noise with a signal-to-noise ratio above 10 dB. The proposed method is capable of extracting mixed waves; Using the equivalent circuit of multiple outgoing lines, bus-to-ground stray capacitances, transformers, and line traps in the substation, we established an inversion model of incident wave based on substation component parameters. The inversion calculation of the incident wave in the complex frequency domain effectively avoids the difficulty of solving high-order differential equations. We then solved the parameter optimization model based on the PSO to ensure that the population is always distributed in the feasible region where we modified the particle swarm position update formula. This method accurately identifies the parameters of the substation's equivalent circuit components. Abstract: In substations, the fault signal at the measuring point includes a variety of signal aliasing. This is partly due to the short length of the substation lines and the exitance of multiple types of equipment that cause the reflection and refraction of the traveling waves. Compared with the incident wave, the distortion in the fault signal is often significant hence interfering with the fault identification and location. To address this issue, we propose an inversion method of the incident wave. To do this, we first investigate the impact of multiple outlets of the substation bus, equivalent stray capacitance of the bus to the ground, transformers, line trap, and other types of equipment on traveling wave transmission. Modeling the aliasing effect of the traveling wave measuring point signal we then propose a fault signal filtering algorithm based on the improved Empirical Mode Decomposition (EMD) and Fast Fourier Transform (FFT). Using this algorithm we then formulate an incident wave inversion model based on substation component parameters. The component parameters in the equivalent circuit model of substation are then accurately identified to achieve an accurate inversion of the incident waves. Identification of parameters is based on an optimization model of substation component parameters based on the Particle Swarm Optimization algorithm (PSO). Our results show that compared with the measured mixed wave signal, the fault characteristics of the inverted incident waves are more accurate, hence improving the fault location accuracy.