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Multivariate Time Series Modeling for Long Term Partial Discharge Measurements in Medium Voltage XLPE Cables

机译：中压交联聚乙烯电缆中长期局部放电测量的多元时间序列建模

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摘要

A multivariate time series analysis was performed for a system of several PD response variables, i.e. average charge, number of discharge pulses, average charge current, and largest repetitive discharge magnitude over the data acquisition period. Experimental lifelong PD data obtained from cable samples subjected to accelerated degradation was used to study the dynamic trends and relationships among those aforementioned response variables. Stochastically formulated cointegrated variables recognized by those tests can be combined to form new stationary variables to estimate the parameters for the Vector Auto Regression (VAR) and Vector-Error Correction (VEC) models. The validity of both models was evaluated by generating Monte Carlo and Minimum Mean Squared Error (MMSE) simulated forecasts. True observed data and forecasted data mean values lie within the 95th percentile confidence interval responses which demonstrates the soundness and accuracy of both models. A life-predicting model based on the cointegrating relations between the multiple response variables, correlated with experimentally evaluated time-to-breakdown values, can be used to set an emergent alarming trigger and as a step towards establishing long-term continuous monitoring of partial discharge activity.
机译：对具有多个PD响应变量的系统进行了多元时间序列分析，即在数据采集期间平均充电，放电脉冲数，平均充电电流和最大重复放电幅度。从经受加速降解的电缆样品获得的终身实验PD数据用于研究动态趋势以及上述响应变量之间的关系。这些测试所识别的随机公式化的协整变量可以结合起来以形成新的平稳变量，以估计向量自回归（VAR）和向量误差校正（VEC）模型的参数。通过生成蒙特卡洛和最小均方误差（MMSE）模拟预测来评估两个模型的有效性。真实的观测数据和预测的数据平均值在95之内 第 百分位数置信区间响应，证明了两个模型的正确性和准确性。基于多个响应变量之间的协整关系的寿命预测模型，与通过实验评估的分解时间值相关，可用于设置紧急警报触发，并作为建立长期连续监测局部放电的步骤活动。

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