The accurate detection of high-frequency transient fault currents in overhead transmission lines is the basis of malfunction detection and diagnosis. This paper proposes a novel differential winding printed circuit board (PCB) Rogowski coil for the detection of transient fault currents in overhead transmission lines. The interference mechanism of the sensor surrounding the overhead transmission line is analyzed and the guideline for the interference elimination is obtained, and then a differential winding printed circuit board (PCB) Rogowski coil is proposed, where the branch and return line of the PCB coil were designed to be strictly symmetrical by using a joining structure of two semi-rings and collinear twisted pair differential windings in each semi-ring. A serial test is conducted, including the frequency response, linearity, and anti-interference performance as well as a comparison with commercial sensors. Results show that a PCB Rogowski coil has good linearity and resistance to various external magnetic field interferences, thus enabling it to be widely applied in fault-current-collecting devices.

Partial discharge (PD) detection and location based on ultra‐high frequency (UHF) sensor array have been employed for the power equipment in the whole substation for assessing the insulating condition of electrical equipment and determining a precondition for further implementing insulation diagnosis. At present, one of the widely used localisation methods is time difference of arrival (TDOA) based, localisation result of which is extremely sensitive to time delay estimation and usually time‐consuming to solve. Motivated by this, a data‐driven method using the deep neural network (DNN) is proposed in this study to significantly speed up the solving process of non‐linear TDOA equations and simultaneously guarantee the accuracy of results. It works with sequences of time delay measured from the UHF sensor array as the input of the network and with the corresponding coordinates of PD source as output to train the network. Simulation results demonstrate that the proposed method shows relatively higher accuracy and efficiency in PD location. In addition, many factors such as array shape, error type added to time delay, and detailed structure and parameters of network are taken into consideration for error analysis, laying foundation to more reliable localisation of PD.

Power electronic devices are widely used in renewable power systems, and the characteristics of partial discharge (PD) signals under repetitive impulse excitation are vastly different from those observed under power frequency excitation, causing many pattern recognition methods to fail. This paper proposes a improved identification method based on representation learning that can directly process PD sequences received by ultra-high frequency (UHF) sensors. First, a representation learning architecture based on the Transformer Encoder module is proposed and the model is pretrained in an unsupervised manner to extract features from the raw input discharge sequences. Then, the extracted features are processed using the ridge regression classifier to achieve end-to-end PD source identification. Finally, the effectiveness of the proposed model is validated through an experimental study, achieving an improved accuracy of 98.6%, which is better than classical PD identification deep learning models under repetitive impulse voltage. Furthermore, the model exhibits strong robustness against noise interference and sampling rates. The effectiveness of the model was also tested on an inverter-fed motor stator device. To further validate its practical applicability, it is essential to collect diverse PD data from various electrical equipment.

Partial discharge accelerates insulation degradation and is a major contributing factor to insulation failures in electrical equipment. Effectively detecting partial discharge signals and accurately identifying discharge types are crucial for assessing insulation conditions. However, in multi-source partial discharge scenarios, signal overlap in the time domain significantly complicates diagnosis. Furthermore, the characteristics of UHF signals received by sensors are influenced by both the type of insulation defects and the propagation path between the discharge sources and the sensors. Therefore, incorporating prior localization information is essential for accurate discharge type identification. This paper addresses the challenges of multi-source partial discharge type identification under repetitive pulse excitation by proposing a deep learning model. First, the Time-Reassigned Multi-Synchrosqueezing Transform (TMSST) is employed to mitigate the impact of signal overlap on feature extraction. Robust features are then extracted using the ResNeSt model with its Split-Attention mechanism. Considering the influence of UHF propagation paths on the received signal characteristics, prior localization information is encoded as auxiliary features and integrated with the time-frequency features. Finally, the Transformer Encoder backbone is utilized to effectively capture dependencies between time-frequency features and prior localization knowledge, enhancing both accuracy and interpretability. Experimental results demonstrate that the proposed model achieves an mAP of 98.7%, outperforming classical deep learning methods, and exhibits strong robustness to noise, highlighting its potential for engineering applications in multi-source partial discharge identification.

High-frequency repetitive pulse excitation is prone to induce partial discharge (PD) of power equipment, resulting in premature insulation failure. PD detection plays an important role in insulation condition monitoring. However, the severe switching electromagnetic interference (EMI) pulses of power electronic devices and the concentrated PD behavior pose difficulties for accurate PD detection. A novel method for detecting weak signals of PD pulses with EMI based on time-reassigned multi-synchrosqueezing transform (TMSST) and similarity distance measurement is proposed in this paper. Firstly, The TMSST method is employed to reassign the signal energy distribution in the time-frequency domain, which can significantly enhance the time-frequency resolution of pulse signals. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method based on the time-frequency energy distribution is employed to detect individual pulse signals. A mask-filtering technique is utilized to filter the noise and reconstruct the pulse signals. Next, the similarity of each extracted pulse signal is measured using the complexity-invariant distance (CID) criterion to identify and remove the EMI pulses. Finally, the performance of the proposed method is verified through simulated data and experimental tests under repetitive pulse excitation. The results demonstrate that the proposed method in this paper exhibits good performance in handling weak PD pulse signals and effectively eliminating the switching EMI pulse.