The transformer bushing hot spot temperature (HST) seriously affects its performance and design. In this paper, a novel method for calculating the hot spot temperature of bushing is proposed, in which the principle of constant joule heat is adopted to transform the fluctuating current into the steady-state current. Firstly, the fluctuating current based on time is segmented. Then, the fluctuating current in each period of time is transformed into steady-state current which is used as heat source. Next, the Finite Element Method (FEM) is used to determine a typical bushing's temperature distribution and specify its hot spot. According to the calculation results of hot spot temperature rise, the time interval is adjusted. Finally, the optimal time interval and the steady-state equivalent current are obtained by solving the hot spot temperature of the transformer bushing iteratively. In addition, the method is used to calculate the hot spot temperature of a 220kV Oil Impregnated Paper (OIP) bushing. Compared with the results of traditional transient calculation method, the validity of the proposed method is verified, and the computing time is greatly reduced.

Abstract-Live-line work methods using helicopters in the vicinity of 1000 kV transmission lines were simulated with finite element modeling of the electric field distributions. The effect of approach path of helicopter live-line work platform towards the energized conductor was computed. Predicted electric fields on the line worker head, body, arms and legs were compared with measured results from a single-phase ultra high voltage (UHV) mock-up, and with calculation results from simulation of an extra high voltage (EHV) 500 kV transmission line geometry. The UHV conductor-to-work platform discharge occurred at a distance of about 2 m, in line with standard flashover models. Additional shielding efficiency of about 4 dB is recommended for the electrically conductive clothing when working on the UHV lines in China, compared to EHV lines.

Focusing on the shortage of mechanical defect detection and diagnosis technology for disconnectors, a wireless monitoring method for the mechanical state of disconnectors is proposed. The splitcore current sensors and improved voltage sensors are used to measure the motor currents and voltages of the disconnector under typical mechanical states at different working voltages. The wireless communication network is used to upload the acquisition data to the cloud server quickly, and the received data are processed by the software system. By comparing and analyzing the curves of current, input power, and output power under different states, it is concluded that the motor output power can adequately reflect the mechanical state of the disconnector. Twenty-three time-domain features of the output power time curve are extracted to form the original feature vector. Kernel principal component analysis (KPCA) method is used to reduce the dimension of the nonlinear features, and the Fisher's criterion function is constructed to determine the width parameter of the kernel function in the feature optimization. Grid search algorithm is used to optimize the kernel parameters of the support vector machine (SVM), and the trained SVM model is used to classify the mechanical state data whose working voltage part is known, and part is unknown, with a classification accuracy of 100%. The results show that the proposed wireless monitoring method can effectively diagnose the mechanical state of the disconnector and has a good generalization ability.

Currently, there is no effective detection and diagnosis technology for the frequently happened mechanical defects of disconnectors. A porcelain column high-voltage disconnector is taken as the object to study the influence of mechanical defects, including bearing jamming, axle pin jamming, and three-phase position asynchrony on the operating torque through geometric mechanics deduction. An operating torque detection technology is proposed, which could accurately measure the operating torque of the disconnector without destroying its original shafting. Using this technology, the operating torque curves of the disconnector in normal state and typical mechanical defect simulation states are measured and compared. The results indicate that the operating torque changes with the mechanical state of disconnector, and these changes could be quantified through the proposed detection technology. And the types of mechanical defects can be distinguished according to their different effects on the operating torque curve. Based on this, a practical mechanical defects diagnosis method is summarized to provide reference for maintainers to diagnose the mechanical defects of disconnectors.

The trip mechanism is a weakness in circuit breakers. Traditional fault identification based on the coil current is difficult to report early mechanical defects such as coil‐plunger jam. Here, the vibration signal during the trip process was extracted. Based on the coil current signal and vibration signal, the characteristics of the trip mechanism are analyzed. The phase space reconstruction (PSR) method is used to extract features from the vibration signal. Combined with the features from the coil current waveform, the feature set representing the health condition of the trip mechanism is proposed. The fault simulation tests are carried out and the variation of current vibration characteristics under fault conditions is studied. The fault identification model based on a support vector machine (SVM) is proposed and compared with the identification results when features are extracted from a single signal. When the power supply voltage is dispersed, the prediction accuracy of fault identification is 83.3% considering only the features of the current waveform or vibration signal. And the identification accuracy rises to 96.7% while using the feature set of current and vibration signals. On basis of the current signal, the method further combines the vibration signal so that the robustness of defect identification improves.