High-Voltage Cable Condition Assessment Method Based on Multi-Source Data Analysis

In view of the problem that the weight value given by the previous state evaluation method is fixed and single and cannot analyze the influence of the weight vector deviation on the evaluation result, a method based on the weight space Markov chain and Monte Carlo method (Markov chains Monte Carlo,...

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Bibliographic Details
Published in:Energies (Basel) 2022-02, Vol.15 (4), p.1369
Main Authors: Meng, Xiao-Kai, Jia, Yan-Bing, Liu, Zhi-Heng, Yu, Zhi-Qiang, Han, Pei-Jie, Lu, Zhu-Mao, Jin, Tao
Format: Article
Language:English
Subjects:
Cables
Data analysis
Electricity distribution
Evaluation
high voltage cable
High voltages
Maintenance
Markov chain Monte Carlo
Markov chains
Methods
Ranking
Sampling
Sampling methods
state evaluation
Statistical analysis
Voltage
weight space
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Summary:In view of the problem that the weight value given by the previous state evaluation method is fixed and single and cannot analyze the influence of the weight vector deviation on the evaluation result, a method based on the weight space Markov chain and Monte Carlo method (Markov chains Monte Carlo, MCMC) is proposed. The sampling method is used for evaluating the condition of high-voltage cables. The weight vector set obtained by MCMC sampling and the comprehensive degradation degree of the high-voltage cable sample are weighted and summed then compared in pairs to obtain the comprehensive degradation degree result. The status probability value and overall priority ranking probability of the object to be evaluated are obtained based on probability statistics, and the order of maintenance is determined according to the status probability value and the ranking result. It is realized that the cable line that needs to be identified in the follow-up defect is clarified according to the evaluation result. This is helpful for operational and maintenance personnel to more accurately implement the maintenance plan for the cable and improve the operational and maintenance efficiency.
ISSN:1996-1073
1996-1073
DOI:10.3390/en15041369