This article demonstrates the use of five different methods to estimate partial discharge (PD) location in an oil insulation system from noisy measurements. The measurements are obtained from three ultrasonic sensors located in three different places. The sensors map the PD location utilizing a nonlinear model. The estimation techniques used in this article are the extended Kalman filter (EKF), the unscented Kalman filter (UKF), the smooth variable structure filter (SVSF), the EK-SVSF, and the UK-SVSF. The last two filters use the combination of EKF or UKF with SVSF, respectively, to consider possible PD model uncertainty. The proposed integrated UK-SVSF algorithm achieves the following objectives. First, the use of the Kaman-based filter enhances the optimality of the filter to system dynamics and measurements noise. Second, the use of the UKF reduces the calculation complexity and errors by replacing the Jacobian calculation with statistical linearization. Finally, the use of the SVSF enhances the estimate’s robustness to model uncertainty. The experimental results verify the claim that the PD location estimate with minimum error is achieved by the UK-SVSF.

中文翻译：

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一种基于混合估计的局部放电定位技术

本文演示了如何使用五种不同的方法通过噪声测量来估计油绝缘系统中的局部放电 (PD) 位置。测量值来自位于三个不同位置的三个超声波传感器。传感器利用非线性模型映射局部放电位置。本文中使用的估计技术有扩展卡尔曼滤波器 (EKF)、无迹卡尔曼滤波器 (UKF)、平滑可变结构滤波器 (SVSF)、EK-SVSF 和 UK-SVSF。最后两个滤波器分别使用 EKF 或 UKF 与 SVSF 的组合，以考虑可能的 PD 模型不确定性。所提出的集成 UK-SVSF 算法实现了以下目标。首先，基于卡曼的滤波器的使用增强了滤波器对系统动态和测量噪声的最优性。第二，UKF的使用通过用统计线性化代替雅可比计算来降低计算复杂度和误差。最后，SVSF 的使用增强了估计对不确定性建模的鲁棒性。实验结果验证了 UK-SVSF 实现了具有最小误差的局部放电位置估计的说法。