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基于日盲紫外和脉冲电流信号的异常放电融合诊断方法

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异常放电是绝缘劣化和设备故障的主要原因,不同放电检测方法各有优缺点.提出一种基于日盲紫外和脉冲电流信号的异常放电融合诊断方法.首先,在实验室环境下制作典型绝缘缺陷模型,利用高频线圈和日盲紫外传感器采集异常放电信号;然后,通过相位分析图谱提取放电特征,构建包含放电相位信息的数据集;最后,使用神经网络构建放电类型识别模型,与不同数据源的异常放电诊断效果进行对比.结果表明,基于改进反向传播神经网络模型的识别准确率可达 96.4%;相比于单一检测数据,使用光电融合数据诊断效果更优.
Integrated Diagnosis of Abnormal Discharge Utilizing Solar-blind Ultraviolet and Pulse Current Signals
Abnormal discharge is the main cause of insulation degradation and equipment failure,and each independent de-tection method has specific pros and cons.This work studied an integrated method of diagnosing abnormal discharge by simultaneously utilizing signals of solar-blind ultraviolet and pulse current.First the actual models of typical insulation de-fects were prepared in the laboratory,and the abnormal discharge signals were collected through high-frequency current transformer and solar-blind ultraviolet sensor.Then the features of abnormal discharge were extracted through phase-re-solved partial discharge pattern,and the dataset containing phase information of discharge was obtained.Finally a dis-charge type identification model was established using a neural network,and a comparison of abnormal discharge diagnosis performance was conducted using different data sources.The results showed that the identification accuracy of the modified back propagation neural network model could reach 96.4%.Compared with single-source detection data,the use of optical-electrical integrated data achieved superior diagnosis performance.

abnormal dischargeintegrated diagnosisfeature extractiontype identification

段生江、项恩新、陈文良、商经锐、叶超奇、陆得群、任明

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云南电网有限责任公司德宏供电局,云南 德宏 678400

云南电网有限责任公司电力科学研究院,云南 昆明 650217

西安交通大学电工材料电气绝缘全国重点实验室,陕西 西安 710049

异常放电 融合诊断 特征提取 类型识别

2024

电工技术
重庆西南信息有限公司(原科技部西南信息中心)

电工技术

影响因子:0.177
ISSN:1002-1388
年,卷(期):2024.(21)