Junyong Wu

Theoretical Analysis and Calculation Model of the Electromagnetic Torque of Nonsalient-Pole Synchronous Machines With Interturn Short Circuit in Field Windings

This paper studies the electromagnetic torque (EMT) of a nonsalient-pole synchronous generator with the interturn short circuit of field windings (ISCFW). First, the virtual displacement method serves as a basis for analyzing the postfault steady-state harmonic characteristic of EMT. The interactions among various space magnetic fields with different orders, speeds, and rotation directions generated by the stator and rotor are considered. A general conclusion on the EMT characteristics under the fault is then derived. Second, based on the connection conditions of the rotor and stator windings under the fault, the voltage and flux relationships among each circuit are analyzed and the calculation model of the EMT under the fault is built. Through comparisons among the simulations, experiments, and theoretical analyses, the correctness of the calculation model is verified. In addition, a three-pole-pair nonsalient-pole synchronous generator after transformation is used for the case study. The calculation and analysis results indicate that ISCFW generates ac pulsation components with their harmonics closely linked to the stator winding configuration. This paper deepens the fault mechanism of ISCFW and lays a foundation for fault monitoring based on mechanical characteristics.

Transient Stability Prediction of Power Systems Using Post-disturbance Rotor Angle Trajectory Cluster Features

Abstract A machine learning-based approach is proposed to predict the transient stability of power systems after a large disturbance. The post-disturbance trajectories of generator rotor angles are taken as a whole cluster, and 19 cluster features are defined to depict the overall transient stability characteristics of the power systems. A hybrid approach, which combines the linear support vector machine with the decision tree, is proposed to generate the final transient stability classifier. Comprehensive studies are conducted on the IEEE 39-bus and IEEE 145-bus test systems to verify the performance of the proposed approach. Test results show that by using the cluster features and the proposed approach, the transient stability of the power system can be predicted accurately with a shorter training time. Furthermore, the prediction classifier is robust to unknown load levels and network topologies, especially under situations when some generator measurements are unavailable and the number of input cluster features is independent of the system scale, making the proposed approach more suitable to transient stability prediction of large-scale power systems.

Research on Model and Method of Maturity Evaluation of Smart Grid Industry

Smart grid has become the inevitable development trend of the modern power grid. The vigorous development of the smart grid led to the rise and development of the smart grid industry, seize the smart grid industry development opportunities, also has become one of the important choice of regional planning and construction. Scientifically reflecting the effect of regional development of smart grid industry of the conditions and the development of the industries to the region will guide regional smart grid industry planning, and encourage regional investment in the development of smart grid industry. This paper established smart grid industry maturity comprehensive evaluation index system from five aspects, the technical performance, industrial facilities, market environment, policy environment and social influence, to put forward to smart grid industry maturity evaluation algorithm, thus smart grid industry maturity assessment model is established, in order to provide reference for regional planning and smart grid industry.