电缆故障识别的归一化STDR分析方法

Normalized STDR analysis method for cable fault identification

序列时域反射法(STDR)是常用的电缆故障检测方法,但传统的STDR方法只能识别基本的短路与开路故障,而不能识别诸如高阻、低阻等其他类型的故障,在精确的电缆故障诊断应用中受限。本文提出一种电缆故障识别的归一化STDR分析方法,该方法根据入射信号(m序列)的自相关峰值及入射信号与反射信号的互相关峰值之间的时间差来确定故障点的位置,并建立基于互相关峰值归一化的电缆负载阻抗估算方法,通过精确计算故障点处的负载阻抗从而实现故障类型的精确识别。电缆故障实验研究表明,电缆故障定位误差≤0.25%,电缆负载阻抗的估算值与真实值的相关度≥98.80%,验证了该方法的有效性。电缆故障识别的归一化STDR分析方法既保证了故障定位精度,也实现了对故障点负载阻抗的精确估算,从而实现对短路、开路、高阻、低阻等多种故障类型的精确识别,突破了传统STDR方法只能识别短路与开路故障的局限。

Sequence time domain reflectometry(STDR) is a common cable fault detection method, but the traditional STDR method can only identify the basic short-circuit and open-circuit faults, and cannot identify other types of faults with high or low resistance loads, which is limited in the application of accurate cable fault diagnosis. This paper proposes a normalized STDR analysis method for cable fault identification, which firstly determines the cable fault location according to the time difference between the autocorrelation peak of the input signal(m-sequence) and the cross-correlation peak of the input and reflected signals, then establishes a cable load impedance estimation method based on the normalization of cross-correlation peaks, and ultimately the fault type can be accurately identified through the estimated load impedance at the fault point. Cable fault experiments show that the error of cable fault location is less than 0.25%, and the correlation coefficient between the estimated cable load impedance and the true load impedance is more than 98.80%, which verifies the effectiveness of the proposed method. The proposed normalized STDR analysis method not only ensures the accuracy of fault location, but also realizes the accurate estimation of load impedance at fault point, so as to realize the accurate identification of different fault types such as short circuit, open circuit, high-resistance load and low resistance load, and breaks through the limitation of the traditional STDR method which can only identify the short circuit and open circuit faults.