The presence of a low density area is beneficial to the facilitation of the underwater pulsed spark discharge, which can be achieved by artificially injecting gas bubble in between the inter-electrode gap. The generation of intensive acoustic waves by such gas-bubble-guided spark discharges makes them promising underwater acoustic sources in multiple practical applications. This paper is aimed at comprehensive investigation of the guiding effect of the injected bubble on the pre-breakdown process of underwater pulsed spark discharges and potential correlations between their subsequent electrical and acoustic parameters with the purpose of optimizing the acoustic emission. The breakdown probability and the pre-breakdown delay were used to evaluate the general facilitation effect brought by the injected bubble. Experimental and numerical works have been conducted and allow observation on the dynamics of the injected bubble under the influence of the applied voltage. Different guiding modes of the injected bubble for plasma streamers' propagation have been observed regarding its relative position. The characteristics of the electrical properties of gas-bubble-guided spark discharges, including the plasma resistance and the plasma energy density, were analyzed by relating them with the breakdown voltage. The dependency of the acoustic wave amplitude and the acoustic efficiency on these electrical parameters was verified, which provides solid regulation principles for the optimization of the plasma-acoustic system for target practical applications.

The mechanical fault diagnosis of a disconnector operating mechanism using a single signal is not sufficiently accurate and reliable. To address this problem, this paper proposes a new fault diagnosis method based on the vibration signal and the motor current signal. First, based on the analysis of the motor stator current signal envelope, segmented envelope RMS values are extracted. Then, the vibration signal of the operating mechanism is processed with VMD (Variational Mode Decomposition). In this paper, the number of modal decompositions K is selected according to the envelope entropy. Second, the effective value of the current segment envelope is fused with the energy entropy value of each IMF component to construct the feature parameters for fault identification. Finally, a fusion weighting algorithm using AdaBoost is proposed to train an SVM as a strong classifier to improve the correct fault diagnosis rate. In this paper, the proposed new diagnosis method is applied to a 220 kV disconnector operating mechanism. The algorithm can effectively identify three operating states of a disconnector operating mechanism.

To address the problem of mechanical defect identification in a high-voltage circuit breaker (HVCB), this paper studies the circuit breaker vibration signal and proposes a method of feature extraction based on phase-space reconstruction of the vibration substages. To locate mechanical defects in circuit breakers, vibration signals are divided into different substages according to the time sequence of the parts of the circuit breakers. The largest Lyapunov exponent (LLE) of the vibration signals’ substages is calculated, and then the substages are reconstructed in high-dimensional phase space. The geometric features of the phase trajectory mean center distance (MCD) and vector diameter offset (VDO) are calculated, and the LLE, MCD, and VDO are selected as the three fault identification features of the vibration substages. The eigenvalue anomaly rate of each substage of the vibration signal under defect state are calculated and analyzed to locate the vibration substage of the mechanical defect. Finally, a fault diagnosis model is constructed by a support vector machine (SVM), and the common mechanical defects of circuit breakers simulated in the laboratory are effectively identified.

In response to the lack of generality in feature extraction using modal decomposition methods and the susceptibility of diagnostic performance to parameter selection in traditional mechanical fault diagnosis of high-voltage circuit breaker operating mechanisms, this paper proposes a Global-Local feature extraction method based on Generalized S-Transform (S-Translate) combined with Gray Level Co-Occurrence Matrix (GLCM) and complemented by Maximum Relevance and Minimum Redundancy (mRMR) feature selection. The GL (Global-Local)-mRMR-KELM fault diagnosis model is proposed, which employs the Kernel Extreme Learning Machine (KELM). In this model, the original time-frequency domain features and the time-frequency features of the Generalized S-Transform matrix of vibration signals under different states of the circuit breaker are first extracted as global features. Then, the GLCM is obtained to extract texture features as local features. Finally, the mRMR and KELM are comprehensively applied to perform feature selection and classification on the dataset, thereby accomplishing the fault diagnosis of the circuit breaker’s operating mechanism. In this study, the 72.5 kV SF₆ circuit breaker operating mechanism is taken as the research object, and three types of mechanical faults are simulated to obtain a vibration signal. Experimental results verify the effectiveness of the proposed GL-mRMR-KELM model, achieving a diagnostic accuracy of 96%. This research provides a feasible approach for the fault diagnosis of circuit breaker operating mechanisms.

C ₅ F ₁₀ O has outstanding insulation properties and low global warming potential, which could be potentially used as alternative gas for SF ₆ and has attracted much attention for researchers. However, there are few reports focusing on its decomposition characteristics under atrocious conditions which is closely related to its insulation performance, as well as its safety issue. In this paper, CO ₂ is selected as the buffer gas. Based on ReaxFF, the decomposition path and reaction mechanism of C ₅ F ₁₀ O/CO ₂ gas mixture were analyzed from the molecular level. The results show that under high temperature conditions C ₅ F ₁₀ O cleavage produces CF ₃ , F, C ₄ F ₇ O, C ₃ F ₆ , CF ₂ , CO, CF ₄ and other by-products. The break and recombination of C-C single bond and C-F single bond are the main reasons for the formation of fluorocarbons in the products. The addition of the electric field makes the C-F single bond and the C=O double bond in C ₅ F ₁₀ O become more active and reactive. The conclusion is complementary to the characteristics of environmental protection insulating gas C ₅ F ₁₀ O in insulation and provides reference for related engineering applications.