Assessment of MV XLPE cable aging state based on PSO-XGBoost algorithm

•Cable aging is a major risk to the operation of power systems.•A PSO-XGBoost state assessment model is proposed to evaluate the aging state of MV XLPE cables.•The PSO algorithm is used to optimize the parameters of the XGBoost model.•The experimental results show that the proposed model has good ac...

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Veröffentlicht in:	Electric power systems research Jg. 221; S. 109427
Hauptverfasser:	Pan, Qiaosheng, Zhang, Chi, Wei, Xinyuan, Wan, Aode, Wei, Zeping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.08.2023
Schlagworte:	Cable aging status assessment Extreme gradient boosting algorithm Particle swarm optimization algorithm Cable aging status assessment Extreme gradient boosting algorithm Particle swarm optimization algorithm
ISSN:	0378-7796
Online-Zugang:	Volltext
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Zusammenfassung:	•Cable aging is a major risk to the operation of power systems.•A PSO-XGBoost state assessment model is proposed to evaluate the aging state of MV XLPE cables.•The PSO algorithm is used to optimize the parameters of the XGBoost model.•The experimental results show that the proposed model has good accuracy. Cable aging is one of the main security risks to power systems. With the widely used cables in power systems, the accurate assessment of cable aging status is increasingly important. This study proposes an efficient assessment model based on the PSO-XGBoost algorithm, which integrates the particle swarm optimization (PSO) algorithm and the extreme gradient boosting (XGBoost) algorithm. The XGBoost model is established to assess the cable aging status with the inputs of partial discharge, operating life, corrosion condition and load condition. The PSO algorithm automatically optimizes parameters during XGBoost model training. Then, the standard performance evaluation metrics of the proposed assessment model are compared with four advanced classification models. The accuracy, precision, recall and F1-score of the assessment model are above 98%, indicating that the proposed PSO-XGBoost model can accurately assess the cable aging state. Furthermore, these calculation results of the proposed model are better than the other four benchmark models, which shows that the proposed model performs better in cable aging status assessment than the existing models.
ISSN:	0378-7796
DOI:	10.1016/j.epsr.2023.109427