A Fault Detection Method for Transmission Lines Based on Auxiliary Classification Generative Adversarial Network
A fault detection and transmission line technology, applied in the direction of detecting faults, fault locations, and measuring electricity according to conductor types, can solve problems such as fault detection, and achieve high accuracy, high detection accuracy, and simple collection.
Active Publication Date: 2022-03-08
SOUTHWEST JIAOTONG UNIV
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[0005] The purpose of the present invention is to provide a transmission line fault detection method based on auxiliary classification generation confrontation network, which can effectively solve the technical problem of fault detection when the transmission line fails under low PMU coverage
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[0069] The present invention will be further described in detail below with reference to the accompanying drawings and specific examples.
[0070] Such as image 3 The IEEE 39 node system arranged in PMU is displayed, and the transmission line fault detection method based on the auxiliary classification is generated. The steps include:
[0071] Step 1: Build a power transmission network model through the PSCAD software, in which the M bus node is arranged in the transmission network, respectively, the synchronous parent measuring device PMU is arranged, divided into a segment of each line, A = 10, pre-routed on each line Set the fault point, set a single-phase short circuit ground, two-phase short circuit grounding, two-phase short circuit, three-phase short circuit, and perform fault simulation; for each fault scenario, acquire M squares with PMU The node takes the voltage in the first perimeter after the fault, N = 62; the voltage of the M mother line node, the Calon Bauer transf...
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The invention discloses a transmission line fault detection method based on an auxiliary classification generation confrontation network. The invention relates to the technical field of high-voltage transmission line fault detection. By simulating the faults of different fault points and different types of faults in each line in the power grid, using the voltage of a small number of nodes with synchronized phasor measurement devices, through Karen Baur transformation and variational mode decomposition, the fault eigenvectors of each node are obtained, and the combination Form the eigenvector matrix and generate the grayscale image of the whole network; construct the auxiliary classification generation confrontation network for transmission line fault detection, optimize its parameters, and use the grayscale image of the entire network when simulating the fault and the random network generated by the normal distribution noise. Vector, alternately train the discriminator and generator of each network; the network judges the authenticity of the input grayscale image of the whole network, obtains its label number, and obtains the fault line number, and realizes the fault detection of each line in the power grid, reaching High detection accuracy. Used to detect faults in high voltage transmission lines.
Description
Technical field [0001] The present invention relates to the field of failure detection of high pressure transmission lines. Background technique [0002] With the development of synchronous phases, data processing and high-speed communication technology in recent years, the grids of all countries have deployed a certain number of synchronous parent Measurement Equipment (PMU) or relay protection devices with GPS synchronization capabilities. . These PMU deployment provide new possibilities for wide-area fault detection of line fault detection capabilities. Unlike traditional single-ended measurements, wide-area fault detection is used in precise synchronization in time, and the measurement information of distributed PMU is used in the region. The fault detection of each line is used. [0003] The existing transmission line fault detection scheme mostly uses synchronous measurement information or equivalent measurement information at both ends of the line, that is, the equivalent ...
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IPC IPC(8): G01R31/08
CPCG01R31/088G01R31/085Y04S10/22Y04S10/52Y02E40/70Y02E60/00
Inventor 童晓阳张广骁张增
Owner SOUTHWEST JIAOTONG UNIV