首页|基于自适应随机森林的变电站设备故障分类研究

基于自适应随机森林的变电站设备故障分类研究

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随着新时代新兴技术的发展,对变电站设备的故障诊断技术提出了更高的要求.变电站一旦出现设备故障,如果没有提前预警和处理,将会造成巨大影响.为此提出主动学习随机森林模型,对原始变电站设备数据提取数据样本集,并进行特征提取,将其用于构建自适应随机树,根据每棵随机树对应的权重,通过提出的投票机制,完成对已知故障的分类和对未知故障的检测.因此这种方法可以自动识别新的故障、自动调整模型,同时在随机森林模型的基础上创新地引入自适应投票机制提升模型分类准确率.分析结果验证了该方法的有效性,此外与传统的随机森林模型进行比较,其分类准确率、误识率和拒识率都表现优异.
Research on Fault Classification of Substation Equipment Based on Adaptive Random Forest
With the development of emerging technologies in the new era,higher requirements are put forward for the fault di-agnosis technology of substation equipment.Once equipment failure occurs in the substation,if there is no advance warning and treatment,it will have a huge impact.The active learning random forest model proposed in this paper extracts a data sample set from the original substation equipment data,performs feature extraction,and uses it to construct an adaptive random tree.Ac-cording to the corresponding weight of each random tree,the classification of known faults and the detection of unknown faults are completed through the proposed voting mechanism.The method proposed in this paper can automatically identify new faults,adjust the model automatically,and introduce an adaptive voting mechanism to improve the classification accuracy of the model.The analysis results verify the effectiveness of the method.In addition,compared with the traditional random forest model,its classification accuracy,misrecognition rate and rejection rate are all excellent.

fault diagnosisfault typerandom forestsimulation experiment

苏华权、周昉昉、易仕敏、廖鹏、杨朝谊

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广东电网有限责任公司信息中心,广东,广州 510000

广东电力信息科技有限公司,广东,广州 510000

广东电网有限责任公司,广东,广州 510000

故障诊断 故障类型 随机森林 仿真实验

2024

微型电脑应用
上海市微型电脑应用学会

微型电脑应用

CSTPCD
影响因子:0.359
ISSN:1007-757X
年,卷(期):2024.40(12)