HVDC power transmission line fault intelligent identification method

Abstract

The invention provides an HVDC power transmission line fault intelligent identification method. The method comprises the following steps: collecting fault current traveling waves after faults of a rectification side and an inversion side of an HVDC power transmission line; establishing internal and external fault feature vectors of a multi-scale S transformation fluctuation index region and fault pole feature vectors of multi-scale S transformation energy and ratios; establishing a combined feature sample vector capable of simultaneously reflecting the internal and external fault features and the fault pole features; establishing a random forest fault intelligent identification model; and identifying whether the current HVDC power transmission line suffers from internal and external fault features of the region or not by using the random forest fault intelligent identification model. According to the invention, fluctuation indexes are used for reflecting internal and external fault characteristics of the region, and energy and ratios are used for reflecting fault pole characteristics, a combined characteristic sample set is used as an input vector of a random forest intelligent algorithm, internal and external fault judgment of the region and fault pole selection are achieved through the same network, and threshold value setting is not needed.

Description

technical field [0001] The invention relates to a method for identifying faults of transmission lines, in particular to a method for intelligently identifying faults of HVDC transmission lines. Background technique [0002] my country has a vast territory, and the unbalanced distribution of energy and load centers determines the wide application of HVDC transmission technology in my country to achieve rational utilization and optimal allocation of resources. High-voltage direct current transmission lines are long, and the probability of failure is high. The correct identification and diagnosis of faults is particularly critical to the power system. [0003] For the bipolar DC line parallel to the same pole, there is electromagnetic coupling between the two pole lines. By analyzing the amplitude-frequency characteristics of the real physical boundary composed of smoothing reactors and DC filters on both sides of the HVDC transmission line, it is found that the real physical ...

AI Technical Summary

Problems solved by technology

These methods can effectively identify faults inside and outside the zone, but there is no theoretical basis for threshold setting, which requires a lot of simulation verification

[0004] In addition, the existing technology also uses the measurement of wave impedance and wave impedance phase to realize fault identification inside and outside the zone, but these algorithms have high requirements on sampling frequency and hardware equipment

In the existing technology, the method of calculating the waveform similarity can realize the discrimination of faults inside and outside the zone, but when the data is disturbed, the error of the similarity is large, the anti-interference ability is poor, and misjudgment is prone to occur.

There is also the existing technology that utilizes the characteristics of the opposite polarity of the current mutation of the two-pole line when a fault occurs to realize fault identification, but when data loss occurs, the error is large

There is also an existing technology that proposes a method for identifying internal and external faults of HVDC transmission lines based on multi-resolution singular spectral entropy and support vector machines, using small sample data to distinguish faults outside the local area, internal areas, and external areas on the opposite side , but the method uses fewer training samples and test samples, and its anti-transition resistance ability and anti-interference ability have yet to be verified

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