基于知识推理的变压器局部放电故障检测技术

局部放电故障诊断是用于检测电力系统设备中的高压绝缘内的缺陷。但是由于相关的背景知识和专业领域知识有限,从原始的监测数据中提取有价值的故障信息就面临了很大的挑战。文中开发了一个基于知识推理系统的变压器的局部放电故障检测技术。对局部放电传感器所采集的信息进行处理,获得相位解析的三维图,并通过对三维图进行分类、提取显著特征的方法对变压器故障进行诊断和定位。系统可以通过对大量广泛的局部放电行为的诊断和缺陷源的分类,支持在线设备状态评估和故障诊断。同时文中用此方法对一个未知的混合放电行为进行诊断,发现诊断精度高于传统的模式识别检测技术。

Transformer Partial Discharge Fault Detection Technology Based on Knowledge Reasoning

Partial discharge fault diagnosis is used to diagnose the defects in high voltage insulation in power system equipment. However, due to the limitation of experience and professional knowledge, it is of great difficulty to extract valuable fault information from the original monitoring data. In this paper, a partial discharge fault detection technology for transformer based on knowledge inference system is proposed and developed. The information collected by the partial discharge sensor is processed to obtain a three-dimensional map of phase analysis. The transformer fault is diagnosed and located by classifying the three-dimensional map and extracting the salient features. The proposed system can diagnose a variety of partial discharge behaviors, classifies defect sources, and supports online device status assessment and fault diagnosis. In addition, an unknown mixed discharge behavior was tested and results show that the diagnostic accuracy based on the proposed method is higher than traditional technologies.