引用本文: | 马利洁,朱永利,郑艳艳.基于并行变量预测模型的变压器故障诊断及优化研究[J].电力系统保护与控制,2019,47(6):82-89.[点击复制] |
MA Lijie,ZHU Yongli,ZHENG Yanyan.Research on transformer fault diagnosis and optimization based on parallel variable prediction model[J].Power System Protection and Control,2019,47(6):82-89[点击复制] |
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摘要: |
针对传统变压器故障诊断方法存在小样本问题下分类效果差、海量监测数据的识别效率低下等问题,提出基于Spark计算框架的并行化变量预测模型。首先采用HDFS作为内存式存储系统,面向行存储的RowMatrix作为分布式矩阵存储结构,利用广播变量、调整分区数进行并行度优化。其次训练4种数学模型获取故障类型的最佳模型及相关参数完成故障诊断。实验结果表明,并行变量预测模型识别精度高于支持向量机,计算效率优于单机环境,对高维特征向量有较好的适应性。 |
关键词: 故障诊断 小样本 变量预测模型 Spark计算框架 内存式存储 |
DOI:10.7667/PSPC180399 |
投稿时间:2018-04-11修订日期:2018-05-29 |
基金项目:国家自然科学基金项目资助(5167702) |
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Research on transformer fault diagnosis and optimization based on parallel variable prediction model |
MA Lijie,ZHU Yongli,ZHENG Yanyan |
(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China) |
Abstract: |
Traditional transformer fault diagnosis method has poor classification effect in the condition of small example and low recognition efficiency of mass monitoring data. Aiming at the problems above, this paper proposes a parallel variable predictive model based on the Spark computing framework. Firstly, HDFS is used as memory storage system, RowMatrix is stored as a distributed matrix storage structure, and broadcast variable and adjustment of the partition number are used to optimize the degree of parallelism. Secondly, the optimal model type and model parameters are obtained by training four mathematical models to diagnose the transformer fault. The experimental results show that the proposed algorithm has higher accuracy than support vector machine, better computing efficiency than stand-alone environment, and good adaptability to high dimensional feature vector. This work is supported by National Natural Science Foundation of China (No. 5167702). |
Key words: fault diagnosis small sample variable predictive model Spark computing framework memory storage system |