Interpretable intelligent integration method for photovoltaic array fault diagnosis

被引：0
|
作者：
Chen Z. [1 ]
Liu W. [1 ]
Wang K. [1 ]
Yu T. [1 ,2 ]
Huang Z. [1 ,2 ]
机构：
[1] School of Electric Power, South China University of Technology, Guangzhou
[2] Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2024年 / 44卷 / 06期
基金：
中国国家自然科学基金;
关键词：
fault diagnosis; interpretable intelligent method; photovoltaic array; Shapley additive explanation method; Stacking ensemble;
D O I：
10.16081/j.epae.202401006
中图分类号：
学科分类号：
摘要：
Aiming at the problems of weak generalization and poor interpretability of existing intelligent methods in photovoltaic array fault detection and diagnosis，an interpretable intelligent integration method is proposed. The feature mining is performed on the collected output time-series of voltage and current waveforms of the photovoltaic array，and multiple mature intelligent algorithms that have been applied to photovoltaic fault diagnosis are used as different base learners and meta learners to construct a Stacking ensemble learning model that combines the advantages of different intelligent algorithms and is more generalized. Then，taking the Shapley additive explanation method as the overall framework，combined with the local approximate interpretable method，the model training process and results are explained and analyzed. By obtaining the contributions of each feature，analyzing the decision-making mechanism of the integrated model，and understanding how to diagnose it，the reliability and credibility of the model are improved. The experimental results of case study show that the proposed interpretable intelligent integration method achieves high-precision fault diagnosis in testing on datasets of different sizes. The interpretability results of the model indicate that the mapping of fault features and diagnostic results established by the intelligent integration model follows physical insights，enhancing the credibility and transparency of the intelligent method. © 2024 Electric Power Automation Equipment Press. All rights reserved.
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页码：18 / 25
页数：7
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