Partial discharge (PD) detection and localization is one of the most effective methods in gas-insulated transmission lines (GIL) insulation fault diagnosis, which is important to the early troubleshooting and safe operation. Compared with the widely used ultrahigh frequency (UHF) and acoustic PD localization methods, the PD localization method based on optical signals has good resistance to electromagnetic and acoustic interference. However, the current optical localization method comes with several shortcomings: narrow detection range, requiring field experiments to accumulate data, and installing a large number of sensors, which renders low feasibility. Therefore, this article proposes a PD localization method utilizing virtual sensors (VSs) and optical simulation fingerprint. Based on the idea of the digital twin, this method constructs a fingerprint database through optical PD simulation in an equal-sized GIL simulation model, which solves the difficulty of fingerprint collection in actual equipment. Meanwhile, this article predicts the detection value of the VS through the adaptive neuro-fuzzy inference system (ANFIS) to greatly reduce the installation of the actual sensor (AS). Finally, through the support vector machines (SVMs) algorithm, the detection fingerprint that includes the virtual and actual detection values matches with the fingerprint database. The location corresponding to the optimal matching result is recorded as the localization result. As experiments have shown, the average localization error of the proposed method is 19.69 mm, which is 54.8% lower than the one without VSs under the same conditions. The results reveal that this method compensates for the loss of localization accuracy caused by the reduction of ASs, which embodies value in practice.

中文翻译：

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一种在 GIL 中使用 AFIS 虚拟传感器和仿真指纹的局部放电源新型光学定位方法


局部放电（PD）检测和定位是气体绝缘输电线路（GIL）绝缘故障诊断最有效的方法之一，对于早期故障排除和安全运行具有重要意义。与广泛使用的超高频（UHF）和声学局放定位方法相比，基于光信号的局放定位方法具有良好的抗电磁和声学干扰能力。然而，目前的光学定位方法存在探测范围窄、需要现场实验积累数据、安装大量传感器等缺点，可行性较低。因此，本文提出了一种利用虚拟传感器（VS）和光学模拟指纹的局放定位方法。该方法基于数​​字孪生的思想，在等尺寸的GIL仿真模型中通过光学PD仿真构建指纹数据库，解决了实际设备中指纹采集的困难。同时，本文通过自适应神经模糊推理系统（ANFIS）预测VS的检测值，大大减少了实际传感器（AS）的安装。最后，通过支持向量机（SVM）算法，将包含虚拟检测值和实际检测值的检测指纹与指纹数据库进行匹配。将最优匹配结果对应的位置记录为定位结果。实验表明，相同条件下，该方法的平均定位误差为19.69 mm，比无VSs的方法降低了54.8%。结果表明，该方法弥补了由于AS减少而造成的定位精度损失，体现了实际应用价值。