To achieve comprehensive insulation deterioration motoring of power equipment and early fault warning in air-insulated substations, a data-driven partial discharge (PD) source localisation method employing noisy ultra-high frequency (UHF) received signal strength indicator (RSSI) and particle filter is proposed in this study. Compared with the existing UHF time-difference-based techniques, UHF wireless sensor arrays and RSSI-based methods provide an economical and high-adaptability solution. However, owing to the multi-pathing and shadowing effects, UHF signal attenuation cannot be modelled. Therefore, a Kalman filter was employed to smoothen the RSSI signal. Furthermore, a semi-parametric regression model is proposed to achieve a more accurate relationship between the RSSI and the transmission distance. Finally, in contrast to traditional localisation algorithms directly based on the RSSI ranging model, a particle filter was used to achieve higher accuracy. It predicted the best distribution of the position of PD by learning and considering all the system states of the previous moment. The laboratory test was performed within an area of 6 m × 6 m, and the results demonstrate that the mean PD source localisation error was 1.16 m, which gives a potential application for the identification of power equipment with insulation deterioration in a substation, while the accuracy is still needed to be verified further by field tests.

为了实现空气绝缘变电站的电力设备的全面绝缘劣化驱动和早期故障预警，采用了带噪声的超高频（UHF）接收信号强度指示器（RSSI）和粒子滤波器的数据驱动局部放电（PD）源定位方法在这项研究中提出。与现有的基于UHF时差的技术相比，UHF无线传感器阵列和基于RSSI的方法提供了一种经济且高适应性的解决方案。但是，由于多径效应和阴影效应，无法对UHF信号衰减进行建模。因此，采用卡尔曼滤波器来平滑RSSI信号。此外，提出了一种半参数回归模型，以实现RSSI与传输距离之间更精确的关系。最后，与直接基于RSSI测距模型的传统定位算法相比，粒子滤波器用于获得更高的精度。通过学习并考虑前一时刻的所有系统状态，可以预测PD位置的最佳分布。实验室测试是在6 m×6 m的区域内进行的，结果表明，平均PD源定位误差为1.16 m，这对于识别变电站中绝缘劣化的电力设备具有潜在的应用价值，而仍需要通过现场测试进一步验证其准确性。通过学习并考虑前一时刻的所有系统状态，可以预测PD位置的最佳分布。实验室测试是在6 m×6 m的区域内进行的，结果表明，平均PD源定位误差为1.16 m，这对于识别变电站中绝缘劣化的电力设备具有潜在的应用价值，而仍需要通过现场测试进一步验证其准确性。通过学习并考虑前一时刻的所有系统状态，可以预测PD位置的最佳分布。实验室测试是在6 m×6 m的区域内进行的，结果表明，平均PD源定位误差为1.16 m，这对于识别变电站中绝缘劣化的电力设备具有潜在的应用价值，而仍需要通过现场测试进一步验证其准确性。