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A Fault Diagnosis Method for Deep Wavelet Siamese Networks for Modular Multilevel Converters

A modular multi-level, twin network technology, applied in the power grid field, can solve problems such as complex topology, unsatisfactory fault diagnosis effect, and difficult data

Active Publication Date: 2022-07-12
XIHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition, after the model is trained offline, the model parameters cannot be relearned according to the actual situation, which may lead to unsatisfactory fault diagnosis results after the circuit parameters change.
However, due to the complex topology of MMC, there are many types of open-circuit faults of power devices, it is difficult to collect a large amount of data in each fault situation, and MMC is inevitably affected by the environment during operation, resulting in drift of device parameters

Method used

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  • A Fault Diagnosis Method for Deep Wavelet Siamese Networks for Modular Multilevel Converters
  • A Fault Diagnosis Method for Deep Wavelet Siamese Networks for Modular Multilevel Converters
  • A Fault Diagnosis Method for Deep Wavelet Siamese Networks for Modular Multilevel Converters

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Experimental program
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Effect test

Embodiment 1

[0078] A deep wavelet twinning network fault diagnosis method for modular multilevel converters, such as figure 1 shown, including the following steps:

[0079] S1. Obtain bridge arm current data of open-circuit faults of different sub-modules in MMC rectification and inverter modes, and perform data enhancement processing on the data to obtain an extended data set;

[0080] When the MMC is running normally, there are four modes for the internal current path of the sub-module, as shown in Table 1.

[0081] Table 1 Submodule current path

[0082]

[0083] From the analysis of Table 1, if T 1 If the fault occurs, the circuit will only be affected in mode 2; if T 2 If the fault occurs, the circuit will be affected only in mode 3. As shown in Figure 3, T in Mode 2 1 Open and Mode 3 T 2 Open circuit submodule internal operation. From Figure 3(a), T in mode 2 1 The open circuit causes the capacitor to not pass through the T 1 Discharge; from Figure 3(b), T in mode 3 2 o...

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Abstract

The invention discloses a deep wavelet twinning network fault diagnosis method for a modular multilevel converter. First, the fault data in the MMC rectification mode and the inverter mode are fused and enhanced, and then the powerful feature extraction capability of the deep wavelet twinning network is used. The data is mapped to a low-dimensional space, and fault identification is performed based on Euclidean distance. Secondly, incremental learning is introduced to relearn and update the model parameters of the fault diagnosis model after the MMC parameters drift, so as to improve the generalization ability of the fault diagnosis model. Finally, the MMC rectification and inverter simulation models are built to verify the effectiveness of the proposed method. The invention combines the advantages of twin network and incremental learning, can quickly diagnose faults, improve the operating reliability of the converter, and improve the accuracy of fault diagnosis of MMC in the case of small samples and parameter drift.

Description

technical field [0001] The invention relates to the field of power grids, in particular to a deep wavelet twin network fault diagnosis method for modular multilevel converters. Background technique [0002] Modular multilevel converters (MMC) are widely used in flexible DC transmission, new energy grid-connected and power Electronic transformer and other fields. The modular structure of MMC makes it possible to bypass or replace the faulty sub-module through the redundant protection strategy when the sub-module fails, thereby improving the reliability and safety of the system operation, and accurate fault diagnosis is to carry out redundant protection. the premise. Therefore, it is of great significance to diagnose the fault quickly and accurately when the sub-module fails. [0003] The fault diagnosis process based on machine learning is usually divided into two parts: feature extraction and fault identification. First, the features representing the fault state are extra...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/54G01R31/08G06F30/27G06K9/62G06N3/04G06N3/08G06F119/10
CPCG01R31/54G01R31/086G01R31/088G06F30/27G06N3/084G06F2119/10G06N3/045G06F18/2135G06F18/2411G06F18/214Y02E60/60
Inventor 张彼德洪锡文彭平冯京李万顺张锦余海宁罗荣秋刘铠
Owner XIHUA UNIV