通过双层堆叠集合优化和数据增强实现智能电表故障诊断的方法

IF 5.7 1区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Leijiao Ge;Tianshuo Du;Zhengyang Xu;Luyang Hou;Jun Yan;Yuanliang Li
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引用次数: 0

摘要

准确识别智能电表(SM)故障类型对于提高运行和维护(O&M)效率以及电力采集系统的可靠性至关重要。然而,由于特征的复杂性和故障类别之间的不平衡性,智能电表故障类型的智能分类面临着巨大挑战。为解决这些问题,本研究提出了一种包含三个不同模块的 SM 故障诊断方法。第一个模块采用标准化、数据估算和特征提取相结合的方法来提高数据质量,从而促进分类器的训练和学习。为了提高分类性能,数据估算方法考虑了特征相关性测量和顺序估算,而特征提取器则利用了判别增强型稀疏自动编码器。为了解决具有离散和连续特征的数据类间不平衡问题,第二个模块引入了辅助分类器生成对抗网络,其中包括离散特征生成模块。最后,我们开发了一种用于 SM 故障诊断的新型 Stacking 集合分类器。与以往研究不同的是,我们构建了一个双层启发式优化框架,以解决 Stacking 集合分类器的组合和超参数的同步动态优化问题,从而更好地处理使用 SM 数据的复杂分类任务。通过两层堆叠集合优化和数据增强提出的 SM 故障诊断方法,利用 2010 年至 2018 年在中国浙江省收集的 SM 故障数据进行了训练和验证。实验结果表明,所提出的方法能有效提高 SM 故障诊断的准确性,尤其是对少数类别的故障诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Fault Diagnosis Method for Smart Meters via Two-Layer Stacking Ensemble Optimization and Data Augmentation
The accurate identification of smart meter (SM) fault types is crucial for enhancing the efficiency of operation and maintenance (O&M) and the reliability of power collection systems. However, the intelligent classification of SM fault types faces significant challenges owing to the complexity of features and the imbalance between fault categories. To address these issues, this study presents a fault diagnosis method for SM incorporating three distinct modules. The first module employs a combination of standardization, data imputation, and feature extraction to enhance the data quality, thereby facilitating improved training and learning by the classifiers. To enhance the classification performance, the data imputation method considers feature correlation measurement and sequential imputation, and the feature extractor utilizes the discriminative enhanced sparse autoencoder. To tackle the interclass imbalance of data with discrete and continuous features, the second module introduces an assisted classifier generative adversarial network, which includes a discrete feature generation module. Finally, a novel Stacking ensemble classifier for SM fault diagnosis is developed. In contrast to previous studies, we construct a two-layer heuristic optimization framework to address the synchronous dynamic optimization problem of the combinations and hyper-parameters of the Stacking ensemble classifier, enabling better handling of complex classification tasks using SM data. The proposed fault diagnosis method for SM via two-layer stacking ensemble optimization and data augmentation is trained and validated using SM fault data collected from 2010 to 2018 in Zhejiang Province, China. Experimental results demonstrate the effectiveness of the proposed method in improving the accuracy of SM fault diagnosis, particularly for minority classes.
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来源期刊
Journal of Modern Power Systems and Clean Energy
Journal of Modern Power Systems and Clean Energy ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
12.30
自引率
14.30%
发文量
97
审稿时长
13 weeks
期刊介绍: Journal of Modern Power Systems and Clean Energy (MPCE), commencing from June, 2013, is a newly established, peer-reviewed and quarterly published journal in English. It is the first international power engineering journal originated in mainland China. MPCE publishes original papers, short letters and review articles in the field of modern power systems with focus on smart grid technology and renewable energy integration, etc.
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