基于多传感信息融合的电缆附件局部放电诊断算法研究OA
Research on Partial Discharge Diagnosis Algorithm for Cable Accessories Based on Multi Sensor Information Fusion
针对电缆附件的局部放电,提出一种基于多传感信息融合的电缆附件局部放电诊断算法.搭建实验平台,并利用暂态地电压传感器(TEV)、高频电流传感器(HFCT)和超声波传感器(AA)采集电缆附件中局部放电的多模态数据,绘制局部放电相位分布(PRPD)图谱.构建局部放电的数据集,并利用卷积神经网络(CNN)、反向传播神经网络(BPNN)和支持向量机(SVM)算法进行分类识别,得到3种算法的识别可信度.通过可信度融合得到智能融合算法,识别结果表明,智能融合算法在不同场景下均表现出较高的识别准确率,提高了电缆附件局部放电诊断的准确性和可靠性.
A partial discharge diagnosis algorithm for cable accessories based on multi-sensor information fusion is proposed to solve the relative questions.An experimental platform is built.The transient earth voltage sensors(TEV),high-frequency current sensors(HFCT),and ultrasonic sensors(A A)are used to collect multimodal data of partial discharge in cable accessories.A partial discharge phase distribution(PRPD)feature mapis draw.A dataset for partial discharge is constructed.The convolutional neural network(CNN),backpropagation neural network(BPNN),and support vector machine(SVM)algorithms are used for classification and recognition to obtain the recognition credibility of the three algorithms.The intelligent fusion algorithm is obtained through the credibility fusion,and the recognition results show that the intelligent fusion algorithm exhibite high recognition accuracy in the different scenarios to improve the accuracy and reliability of partial discharge diagnosis of cable accessories.
刘浩;侯春光;高有华
沈阳工业大学,辽宁沈阳 110000沈阳工业大学,辽宁沈阳 110000沈阳工业大学,辽宁沈阳 110000
动力与电气工程
局部放电卷积神经网络反向传播神经网络支持向量机多模态数据
partial dischargeconvolutional neural network(CNN)backpropagation neural network(BPNN)support vector machine(SVM)multimodal data
《电器与能效管理技术》 2024 (10)
36-41,6
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