Spectral analysis‐based fault diagnosis algorithm for 3‐phase passive rectifiers in renewable energy systems

Sharan, Bindu; Jain, Tushar

doi:10.1049/iet-pel.2020.0510

Cited by 7 publications

(1 citation statement)

References 29 publications

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“…Liu et al [4] studied the fault analysis of distributed power grids based on the Bayesian algorithm combined with evidence theory and found through simulation experiments that the method had high fault tolerance and performed well in fault analysis under the situation of incomplete information. Sharan et al [5] designed a method based on spectrum analysis to monitor open-circuit faults in grid-connection and verified the effectiveness of the method by simulation analysis. Stallon et al [6] designed a method called kernel principal component analysisenhanced spider monkey optimization to achieve the analysis of grid faults and found through experiments that the method obtained 98% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Study on the Analysis of Fault Data During Intelligent Operation of Power Equipment With Cluster Analysis

2023

IJOMAM

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Many fault-related data are generated in the intelligent operation of power equipment, and analyzing these data can help understand the operating condition of the equipment. This paper studied the clustering analysis methods and introduced K-means, kernel C-means (KCM), and fuzzy kernel C-means (FKCM) methods. The gray wolf optimization (GWO) algorithm was used to optimize the initial clustering center and kernel parameters of the FKCM algorithm to obtain the GWO-FKCM algorithm. Experiments were conducted taking transformer data as an example. It was found that the GWO-FKCM algorithm obtained the optimal clustering results at the 763rd iteration, and its accuracy rates for different fault types were all above 90%, with an average value of 92.92%, which was higher than the other clustering analysis methods. The results prove the reliability of the GWO-FKCM algorithm for equipment fault data analysis. This algorithm can be promoted and applied in actual power equipment.

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Section: Introductionmentioning

confidence: 99%

Study on the Analysis of Fault Data During Intelligent Operation of Power Equipment With Cluster Analysis

2023

IJOMAM

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Signal-based fault diagnosis in Q-pulse diode rectifier with fast and accurate detection of faulty switch

Nejad

Dastfan

2023

Measurement

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Research on Fault Diagnosis Algorithm for Power Supply Lightning Protection Online Monitoring System

Zhang,

Ran

et al. 2023

Procedia Computer Science

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Spectral analysis‐based fault diagnosis algorithm for 3‐phase passive rectifiers in renewable energy systems

Cited by 7 publications

References 29 publications

Study on the Analysis of Fault Data During Intelligent Operation of Power Equipment With Cluster Analysis

Study on the Analysis of Fault Data During Intelligent Operation of Power Equipment With Cluster Analysis

Signal-based fault diagnosis in Q-pulse diode rectifier with fast and accurate detection of faulty switch

Research on Fault Diagnosis Algorithm for Power Supply Lightning Protection Online Monitoring System

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