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.

Realization of an electrical aircraft demands a low-weight electric distribution and propulsion system. The use of high voltage and power electronics operated at high switching frequencies is essential to achieve this objective. However, printed circuit boards (PCBs) in electrified aircraft are in a harsh working environment, which can make PCBs more susceptible to generate partial discharges (PDs). The current PD detection technology has poor immunity to the electromagnetic interference and acoustic interference in the operating environment of aircraft. Therefore, this article proposes an optical-based PD detection method for PCBs, which is effectively immune to electromagnetic and acoustic interference. This method uses fluorescent fiber as a PD optical signal sensor and then collects the optical signal by the avalanche photodiode (APD). Experiments have verified that the detection sensitivity, sensing range, and anti-interference performance of this method are well satisfied with the PD detection. In addition, single PD pulse, optical phase resolved PD (PRPD) patterns, and PD inception voltage (PDIV) under different air pressure and voltage conditions are investigated. Finally, the relationship between the optical signal and PD amplitude is found to be proportional, which proves that the severity of the PD on PCBs can be effectively detected and evaluated by this method.

Partial discharge (PD) is an important phenomenon that reflects the insulation condition of electrical equipment. In order to protect the safety of power grids, it is of significance to diagnose the type of insulation defects inside the equipment accurately and early through PD pattern recognition. In this article, phase resolved pulse sequence (PRPS) graphs in 3D were constructed by the PD pulse data of the gas-insulated switchgear (GIS) acquired, then the histogram of oriented gradient (HOG) features were extracted directly from the 3D PRPS graphs, and finally the attribute selective Naïve Bayes classifier was used to recognize the discharge pattern. In addition, this method was compared with two traditional methods, i.e., the statistical method and the grayscale gradient co-occurrence matrix method, from three aspects. The result shows that 3D PRPS graphs have different morphology characteristics in vision under different defects, and the similarity among different voltages applied is higher than among different defects, so it is reasonable to use them as the basis for PD pattern recognition. The contrast indicates that the HOG method not only has the highest accuracy with the least requirement for pretreatment and training, but it also has robustness when the voltage applied changes. Consequently, this method has the universality for PD pattern recognition that is based on 3D PRPS graphs.

Partial discharge (PD) is one of the main causes rendering the deterioration of the insulation state in gas‐insulated transmission line (GIL). Accurate and timely localization of the PD source is essential to ensure the safe and stable operation of the GIL. At present, optical PD detection technology shows advantages in terms of high sensitivity and strong anti‐interference performance. Therefore, an optical PD localization method based on optical simulation fingerprint database is proposed. The introduction of simulation conquers the difficulty of obtaining a PD fingerprint database in field experiments. This method constructs a simulation fingerprint database by performing PD simulation in a GIL simulation model of the same size as the actual GIL. The natural neighbour interpolation algorithm is applied to expand the simulation fingerprint database to cover all locations in the GIL. The two‐level localization method proposed is to match the PD fingerprint to be tested with the expanded fingerprint database, which can reduce the amount of calculation while maintaining the localization accuracy. The experimental results show that the average localization error of this method is only 9.7 mm, and the localization time is reduced by about 11 times compared with the normal one‐level localization method.

Sensitive signal perception and accurate fault diagnosis are key to partial discharge (PD) optical detection. To improve the effectiveness of the PD optical detection and diagnosis, this paper uses optical sensing technology based on a light guide rod (LGR) to measure the typical PDs in SF ₆ . The characteristics of optical PD signals are analyzed and a fault diagnosis method for the PD signals detected by the LGR is proposed. An optical and electrical PD experimental platform is built, four different discharge defects are designed, the time-domain signal characteristics of PD optical signals are analyzed, and phase-resolved pulse sequence (PRPS) patterns are plotted. On this basis, a PD optical signal fault diagnosis model based on the pyramid histogram of oriented gradient (PHOG) and optimized support vector machine (SVM) algorithm is established. The results show that the accuracy of this model reaches 96.7%, which is about 10% higher than other algorithms, verifying the effectiveness of detecting PD with an LGR and the reliability of the optical signal diagnostic method based on PHOG features. The results of this study can provide references for PD optical detection and diagnosis in gas insulated switchgear (GIS) with an LGR.

Optical partial discharge (PD) detection is an efficient means of diagnosing the insulation status of power equipment. C ₄ F ₇ N/CO ₂ gas mixture is a very potential environmentally-friendly SF ₆ substitute gas, and its PD optical characteristics need to be studied to guide the PD diagnosis of novel C ₄ F ₇ N/CO ₂ equipment. Therefore, this article proposes a multispectral microarray detection technology, which can achieve high-sensitivity detection and PD diagnosis by simultaneously collecting the spectral characteristics of multiple bands. By setting up an experimental platform, the PD experiments of four typical defects in the C ₄ F ₇ N/CO ₂ gas mixture with five different proportions and pure SF ₆ are carried out. Based on the analysis of PD multispectral features, the correlation between different gases and the difference between different defects are obtained. Finally, by combining multispectral detection with a ${t}$ -distributed stochastic neighbor embedding (T-SNE) feature extraction algorithm, a PD diagnosis method that can adapt to both C ₄ F ₇ N/CO ₂ gas mixture and SF ₆ is proposed, which provides a reference for the PD detection of novel C ₄ F ₇ N/CO ₂ equipment application.