The arc suppression device based on ground fault transfer (GFT) has been preliminarily applied in the medium voltage distribution network (MVDN). An accurate travelling wave (TW) fault location method is proposed to extend the use of the ground fault transfer device. D-PMU is used as a travelling wave detection tool to record the transient voltage travelling waves of fault grounding and bus active grounding during arc suppression. Then, the faulty section is identified through the time difference of travelling wave arrival at the upstream and downstream measurement points. On this basis, the fault location equations of the arrival time and distance of the upstream travelling wave are established, and an accurate fault location method based on the arrival time difference of the travelling wave is proposed. The simulation model is established by PSCAD/EMTDC, and the results show that the method has high location accuracy, and the absolute error is less than 30 m. It is not affected by the TW velocity, the fault conditions, or the distributed power sources.

The sampling rate of the wide-area synchronous phasor measuring device (D-PMU) in the distribution network is insufficient, and the localization sensitivity of the traditional localization method based on the time-domain Bergeron equation in the distribution network is insufficient. In this paper, an accurate location method of the distribution line grounding fault based on the time-domain’ synchronous information calculation is proposed to solve the problem of limited location accuracy caused by a low sampling rate and the insufficient sensitivity of traditional methods. The method preprocesses the measurement data through low-frequency time-domain signal reconstruction and cubic spline interpolation. The fault current’s different location criterion is constructed by using the voltage and current constraints at the fault point. By calculating the fault current difference at a limited number of calculated points, the accurate fault location under a low sampling rate is realized, which is beneficial to the rapid maintenance of faults and the shortening of the power outage time.

In order to adapt to the development of the industrial Internet of Things, the relationship between the internal components of electromechanical equipment is getting closer and closer, such as motor bearings. Nowadays, timely diagnosis of motor bearing faults is urgently needed. Most traditional methods for motor bearing fault diagnosis use a single learner and emphasize the role of feature extraction, which usually requires a large amount of sample support and computer runtime to obtain satisfactory performance. In this article, the Bayesian optimized decision tree with ensemble classifiers after feature extraction of the original data is finally proposed which has good performance. We use multiple feature extraction to establish the feature matrix and construct a decision tree model with the ensemble method for AdaBoost and a Bayesian optimized decision tree model with ensemble classifiers to conduct experiments on the accuracy, prediction speed, etc., of the model. We derived four sets of experimental data. The results show that the optimal method is the Bayesian optimized decision tree with ensemble classifiers after feature extraction. The accuracy of this method is as high as 99.9. At the same time, unlike previous studies, we found in our study that feature extraction does not improve the accuracy of diagnosis for the decision tree with ensemble method for AdaBoost and there is a precipitous decline. In the industrial Internet of Things, the conclusion can improve certain reference value for the future fault diagnosis of motor bearings.

The Rotating Asynchronous Machine (RAM) system of a nuclear power plant provides a stable and reliable power supply for the control rod drive mechanism (CRDM) through two parallel generators. At present, the main protection of the generators in the RAM system is only configured with longitudinal differential protection, which cannot reflect the interturn short circuit fault and interbranch short circuit fault of the stator winding. Based on the characteristics of parallel connection of RAM generators in nuclear power plants, this article proposes a dual-generator split phase transverse differential protection and a dual-generator incomplete longitudinal differential protection. Through the internal fault set of the generator and the internal fault potential splitting model of the generator, the quantitative analysis of different main protection configuration schemes is carried out. It is concluded that the existing longitudinal differential protection and the incomplete longitudinal differential protection of two parallel generators can correctly reflect the vast majority of internal short-circuit faults. Finally, the dynamic experiment is carried out to prove the feasibility of the protection method in the actual unit operation.

Energy is the foundation of economic development and technological development. Facing the present situation of non-renewable energy decline, wind power generation has been developed rapidly. However, the problem of unstable output voltage of wind power generation due to unstable wind speed needs to be solved, and traditional solutions cannot make the generated electric energy meet the national standards for grid-connected and off-grid operation. In this paper, the electric energy generated by wind turbine is rectified by bridge rectifier circuit and using large capacity capacitor filtering to generate DC with flat waveform. Then, using SPWM inversion technology, the normal rotation wave with the same frequency as the power grid is used as the modulation wave and according to the required voltage amplitude, and the triangle wave with appropriate duty ratio is calculated by SPWM smoothing control theory as carrier wave. Subsequently, accurate filtering is carried out and grid-connected and off-grid operation is carried out through the self-aligning device. Finally, with the help of MATLAB simulation platform, the wind turbine is simulated to work under different wind conditions, and whether the generated electric energy meets the grid-connected and off-grid operation standards is judged, which further determines the reliability and authenticity of the theory.