Ultra-high-voltage direct-current transmission line neural network double end fault location method based on high frequency amount attenuation characteristic

A technology of UHV DC and neural network, which is applied in the field of relay protection of HVDC transmission system, can solve the problems of high precision requirements of clock synchronization devices at both ends and difficulty in determining wave velocity, etc.

Inactive Publication Date: 2014-06-11
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

[0003] The invention provides a double-terminal fault location method based on the high-frequency quantity attenuation characteristic of the UHV DC transmission line neural network, which is used to overcome the difficulty in determining the wave velocity in the traditional double-terminal traveling wave distance measurement method and the two-terminal clock Synchronization device precision requirements are too high and other issues

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  • Ultra-high-voltage direct-current transmission line neural network double end fault location method based on high frequency amount attenuation characteristic
  • Ultra-high-voltage direct-current transmission line neural network double end fault location method based on high frequency amount attenuation characteristic
  • Ultra-high-voltage direct-current transmission line neural network double end fault location method based on high frequency amount attenuation characteristic

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Embodiment 1

[0038] Embodiment 1: as Figure 1-3 As shown, a UHV DC transmission line neural network double-terminal fault location method based on high-frequency quantity attenuation characteristics, the specific steps of the method are as follows:

[0039] A. When a ground fault occurs on the UHV DC transmission line, the data acquisition devices at the distance measuring device on the rectification side and the distance measuring device on the inverter side collect the fault voltage data within the time window 5ms after the arrival of the first wave of the fault voltage traveling wave;

[0040] B. For different transition resistances and different fault distances, respectively perform wavelet transformation on the fault data extracted from the distance measuring devices on the rectification side and the inverter side, and obtain the high-frequency bands at scales 1, 2, 3, and 4 on the rectification side The amplitude of the first wave head of the fault voltage traveling wave U a1 , U...

Embodiment 2

[0049] Embodiment 2: as Figure 1-3 As shown, a UHV DC transmission line neural network double-terminal fault location method based on high-frequency quantity attenuation characteristics, the sampling frequency during simulation f s =200kHz, in order to enhance the generalization ability of the neural network model for fault location, select the head amplitude of the first traveling wave of the fault voltage on the rectifier side and the inverter side in the high-frequency band corresponding to the first, second, third, and fourth scales after wavelet decomposition The value ratio is used as the input sample set of the ranging network, and the fault distance is used as the output sample set to train the neural network to form a fault ranging neural network model. After the fault location neural network model is formed, the fault location can be realized by inputting the characteristic data reflecting the fault location into the trained network model. The input sample set for...

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Abstract

The invention relates to an ultra-high-voltage direct-current transmission line neural network double end fault location method based on the high frequency amount attenuation characteristic, and belongs to the technical field of relaying protection on high-voltage direct-current transmission systems. According to the method, on the basis of the attenuation characteristic generated when faults are spread on a line with high frequency amount, the mathematic relations between the fault distances and the ratio of a head wave amplitude value of a traveling wave of a fault voltage reaching a rectifying side range unit to a head wave amplitude value of a traveling wave of a fault voltage reaching an inversion side range unit are derived; the ratio of head wave amplitude values of traveling waves of fault voltages detected by rectifying side range units in different frequency bands to head wave amplitude values of traveling waves of fault voltages detected by inversion side range units in different frequency bands are selected to serve as an input sample set of a BP neural network, the fault distances serve as an output sample set, the neural network is trained to generate a fault location neural network model; after the fault location neural network model is formed, testing samples are input the trained model to obtain the fault location result. The method avoids the problem that wave speed is difficult to determine with a traditional double-end traveling wave fault location method.

Description

technical field [0001] The invention relates to a double-terminal fault distance measuring method based on the high-frequency quantity attenuation characteristic of a UHV direct current transmission line neural network, and belongs to the technical field of relay protection of a high voltage direct current transmission system. Background technique [0002] At present, distance measurement after DC transmission line fault mainly relies on traveling wave fault location technology, which can be divided into single-terminal traveling wave distance measurement method and double-terminal traveling wave distance measurement method. The single-ended traveling wave method needs to accurately identify the second reflected traveling wave head, and it is difficult to correctly identify the second reflected traveling wave head when there is a high-impedance ground fault; the key to accurate positioning of the double-ended traveling wave ranging method lies in correct identification and ca...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/08G01R31/02
CPCH04B3/548G01R31/085
Inventor 陈仕龙谢佳伟毕贵红张杰曹蕊蕊荣俊香李兴旺罗璐
Owner KUNMING UNIV OF SCI & TECH
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