Artificial intelligence identification method of external faults of conversion current bus of HVDC system

A technology for commutating busbars and external faults, applied to measuring devices, instruments, biological neural network models, etc., can solve problems such as the influence of AC and DC system stability, system operation collapse, and hazards, and achieve good model classification ability and accuracy good, believable effect

A technology for commutating busbars and external faults, applied to measuring devices, instruments, biological neural network models, etc., can solve problems such as the influence of AC and DC system stability, system operation collapse, and hazards, and achieve good model classification ability and accuracy good, believable effect

CN103837784AActive Publication Date: 2014-06-04STATE GRID CORP OF CHINA +2
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  • Artificial intelligence identification method of external faults of conversion current bus of HVDC system
  • Artificial intelligence identification method of external faults of conversion current bus of HVDC system
  • Artificial intelligence identification method of external faults of conversion current bus of HVDC system

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Experimental program
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Embodiment

[0035] combine figure 1 , which illustrates the flow chart of the artificial intelligence identification method for the external faults of the HVDC system converter bus. identify.

[0036] 1. The structure of BP neural network

[0037] 1. Select the fault signal

[0038] When the HVDC AC system fails, electrical quantities such as commutation voltage, DC voltage, DC current and firing angle will all change suddenly. In the present invention, the DC voltage U on the inverter side is selected d as a fault signal.

[0039] Select respectively the DC voltage signal U d , as training samples for the neural network. Among them, the different operating modes of the system mainly include: A, the bipolar wiring operation mode of the system; B, the single pole ground circuit operation mode of the system; C, the full voltage operation mode; D, the step-down operation mode (20%, 30% step-down) ; E, bipolar unbalanced operation mode. N1, F1~F4 faults mainly refer to: N1-normal stat...

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Abstract

The invention provides an artificial intelligence identification method of external faults of a conversion current bus of an HVDC system. The artificial intelligence identification method is characterized in that a wavelet-transform multi-scale decomposition algorithm is utilized to decompose system fault signals into different frequency sections, different wavelet energy skewnesses of different frequency-section signals are used as fault diagnosis feature vectors, and the fault diagnosis feature vectors are combined with a BP neural network to achieve accurate identification of fault types of an AC system. Compared with an AC system fault type identification method in the prior art, the method only needs to extract characteristics of a fault signal from the aspect of energy distribution, is not affected by the system operation mode, and is simple in operation, good in accuracy, and high in reliability.

Description

technical field [0001] The invention relates to an artificial intelligence identification method for external faults of a commutation bus in an HVDC system, and belongs to the field of high-voltage direct current transmission. Background technique [0002] The operation of the HVDC (high voltage direct current) transmission system is affected by the failure of the DC line, converter or AC system. In order to ensure the safe and reliable operation of the entire AC and DC system, it is necessary to detect the existence, type and occurrence of the fault at the initial stage of the fault. s position. [0003] The influence of the AC system fault on the DC system is effected by the change of the commutation voltage applied to the converter. When the AC system fails, the rate, amplitude and phase of the AC voltage drop will affect the operation of the DC system. When the AC system on the inverter side fails, the AC bus voltage of the inverter station decreases, which reduces the...

Claims

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Application Information

Patent Timeline
04 Jun 2014
Publication
CN103837784A
IPC
G01R31/00; G06N3/02
Inventors
王渝红; 朱艳