Wavelet Packet Decomposition-Based Fault Diagnosis Scheme for SRM Drives With a Single Current Sensor

Gan, Chun; Wu, Jianhua; Yang, Shiyou; Hu, Yihua; Cao, Wenping

doi:10.1109/tec.2015.2476835

Cited by 60 publications

(24 citation statements)

References 39 publications

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“…Clearly, this method is susceptible to noise, and a single threshold cannot be determined for all cases. An improvement to this would have the threshold being set by assessment of the first traveling wave crest, with the threshold value being used for subsequent data capture.The wavelet-based multi-scale time-frequency analysis method [21,22,23]: The arrival time is identified by the maximum position of the wave crest signal. This method is mainly used for overhead lines, as they have a simpler structure and the method is very susceptible to errors in the accuracy of the line parameters.…”

Section: The Comparison Of Several Popular Algorithms For the Arrimentioning

confidence: 99%

A Novel Traveling-Wave-Based Method Improved by Unsupervised Learning for Fault Location of Power Cables via Sheath Current Monitoring

Liu

Zhu

et al. 2019

Sensors

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In order to improve the practice in maintenance of power cables, this paper proposes a novel traveling-wave-based fault location method improved by unsupervised learning. The improvement mainly lies in the identification of the arrival time of the traveling wave. The proposed approach consists of four steps: (1) The traveling wave associated with the sheath currents of the cables are grouped in a matrix; (2) the use of dimensionality reduction by t-SNE (t-distributed Stochastic Neighbor Embedding) to reconstruct the matrix features in a low dimension; (3) application of the DBSCAN (density-based spatial clustering of applications with noise) clustering to cluster the sample points by the closeness of the sample distribution; (4) the arrival time of the traveling wave can be identified by searching for the maximum slope point of the non-noise cluster with the fewest samples. Simulations and calculations have been carried out for both HV (high voltage) and MV (medium voltage) cables. Results indicate that the arrival time of the traveling wave can be identified for both HV cables and MV cables with/without noise, and the method is suitable with few random time errors of the recorded data. A lab-based experiment was carried out to validate the proposed method and helped to prove the effectiveness of the clustering and the fault location.

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Section: The Comparison Of Several Popular Algorithms For the Arrimentioning

confidence: 99%

A Novel Traveling-Wave-Based Method Improved by Unsupervised Learning for Fault Location of Power Cables via Sheath Current Monitoring

Liu

Zhu

et al. 2019

Sensors

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“…Hence, frequency components that contain high energy are easier to identify at different narrow bands. A wavelet packet function can be defined as [27,28]:…”

Section: Wavelet Packet Energy Distributionmentioning

confidence: 99%

Diagnosis of DC Bias in Power Transformers Using Vibration Feature Extraction and a Pattern Recognition Method

Zhou

et al. 2018

Energies

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DC bias is a great threat to the safe operation of power transformers. This paper deals with a new vibration-based technique to diagnose DC bias in power transformers. With this technique, the DC bias status of power transformers can be automatically recognized. The vibration variation process of a 500 kV autotransformer is tested under the influence of DC bias in the monopole trail operation stage of a ±800 kV HVDC transmission system. Comparison of transformer vibration under normal and DC-biased conditions is conducted. Three features are proposed and are validated by sensitivity analysis. The principal component analysis method is employed for feature de-correlation and dimensionality reduction. The least square support vector machine algorithm is used and verified successful in DC bias recognition. A remote on-line monitoring device based on the proposed algorithm is designed and applied in field DC bias diagnosis of power transformers. The suggested diagnostic algorithm and monitoring device could be useful in targeted DC bias control and improving the safe operation level of power transformers.

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“…In this way, special attention is also being devoted to fault diagnosis, involving SRM drives. New fault diagnosis schemes, based on the analysis of the power converter supply current, were presented in [27]- [30]. In [31] a new algorithm for real-time diagnosis of power converter faults in SRM drives is proposed, using only the measured phase currents and in [32] it is presented an online fault diagnostic algorithm for power converter faults in SRM drives based on high frequency voltage signal injection.…”

Section: Introductionmentioning

confidence: 99%

A Multilevel Fault-Tolerant Power Converter for a Switched Reluctance Machine Drive

et al. 2020

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Reliability in the electrical drives is becoming an important issue in many applications. In this context, the reliability associated to the switched reluctance machine (SRM) is also an important area of research. One of the major problems, that strongly affect its operation, are drive power semiconductors faults. Typical power converter topologies used in SRM drives cannot handle faults in their power semiconductors. So, this paper presents a power converter topology that provides fault-tolerant capabilities to the drive under a switch fault. This power converter will be used considering that a change in the direction of the current that flows in the SRM windings does not affect the behavior of the machine. Besides that, the proposed power converter will allow to generate multilevel phase voltages in order to apply different voltage levels as function of the SRM speed. A laboratory power converter was developed to test the SRM drive in normal and faulty conditions. From the obtained results it was possible to verify the fault-tolerant capability of the drive under switch faults in different devices and failure modes. It was also possible to confirm the multilevel operation of the drive.

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Wavelet Packet Decomposition-Based Fault Diagnosis Scheme for SRM Drives With a Single Current Sensor

Cited by 60 publications

References 39 publications

A Novel Traveling-Wave-Based Method Improved by Unsupervised Learning for Fault Location of Power Cables via Sheath Current Monitoring

A Novel Traveling-Wave-Based Method Improved by Unsupervised Learning for Fault Location of Power Cables via Sheath Current Monitoring

Diagnosis of DC Bias in Power Transformers Using Vibration Feature Extraction and a Pattern Recognition Method

A Multilevel Fault-Tolerant Power Converter for a Switched Reluctance Machine Drive

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