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.

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.

In recent years, the environmentally friendly gas, C₄F₇N, has been recognized as a potential SF₆ alternative gas due to its excellent insulation. To study the streamer discharge characteristics of different electrode shapes in C₄F₇N, a fluid model was established by COMSOL, and simulations were performed. Streamer discharge as a function of varying electrode shapes was analyzed, and the variation law of electric field intensity and photon flux was obtained, which serves as a theoretical basis for optical detection and reliable diagnosis of partial discharge. Simulation results demonstrate that under the same discharge conditions, the electric field inhomogeneity of the needle–plane model, rod–plane model, and ball–plane model decreased successively and the breakdown difficulty increased. With the increase in the applied voltage, the peak electric field intensity of the streamer head was found to increase, whereas an increase in the electrode spacing was found to decrease this property. The change in photon flux was found to be similar to the change in the electric field intensity.

Optical detection of partial discharge (PD) is an important means to diagnosis the insulation status of equipment. However, the current optical detection is either unable to perform simultaneous detection of multispectral features or it can only be applied under laboratory conditions. Moreover, the PD of different gases will correspond to different multispectral characteristic distributions, but the current research is only aimed at the study of SF ₆ . Therefore, this article proposes a gas-insulated equipment PD detection method based on the multispectral array sensor and Gaussian mixture model (GMM) clustering algorithm in C ₄ F ₇ N/CO ₂ gas mixture and SF ₆ . This method adopts the silicon photomultiplier (SiPM) array and multispectral grid to realize the collection of PD optical signals of different wavelength bands, which has a high degree of integration and can be applied in actual equipment. Then through the GMM model, cluster analysis of different multispectral features can effectively diagnose different types of PD defects. In the experiment, four kinds of PD defects under the conditions of five various ratios C ₄ F ₇ N/CO ₂ gas mixture and pure SF ₆ gas are used for test. The similarities and differences of the PD multispectral characteristics in phase distribution, energy distribution, and radar chart distribution are studied, which compares the PD multispectral characteristics in C ₄ F ₇ N/CO ₂ gas mixture and SF ₆ . According to the experiment result, the diagnosis accuracy in different gases is generally higher than 84%, and the highest can reach 95% in SF ₆ . This result shows that the method can well diagnose the PD in both SF ₆ and C ₄ F ₇ N/CO ₂ power equipment, which has a wide range of application prospects.