Fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods

Accurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution systems.However,traditional intelligent methods limit the use of the physical structures and data information of power networks.To this end,this study proposes a fault diagnostic model for distribution systems based on deep graph learning.This model considers the physical structure of the power network as a significant constraint during model training,which endows the model with stronger information perception to resist abnormal data input and unknown application conditions.In addition,a special spatiotemporal convolutional block is utilized to enhance the waveform feature extraction ability.This enables the proposed fault diagnostic model to be more effective in dealing with both fault waveform changes and the spatial effects of faults.In addition,a multi-task learning framework is constructed for fault location and fault type analysis,which improves the performance and generalization ability of the model.The IEEE 33-bus and IEEE 37-bus test systems are modeled to verify the effectiveness of the proposed fault diagnostic model.Finally,different fault conditions,topological changes,and interference factors are considered to evaluate the anti-interference and generalization performance of the proposed model.Experimental results demonstrate that the proposed model outperforms other state-of-the-art methods.

Overview of Mechanism and Mitigation Measures on Multi-frequency Oscillation Caused by Large-scale Integration of Wind Power

In recent years,the large-scale integration of re-newable energy sources represented by wind power and the widespread application of power electronic devices in power systems have led to the emergence of multi-frequency oscillation problems covering multiple frequency segments,which seriously threaten system stability and restrict the accommodation of renewable energy.The oscillation problems related to renewable energy integration have become one of the most popular topics in the field of wind power integration and power system stability research.It has received extensive attention from both academia and industries with many promising research results achieved to date.This paper first analyzes several typical multi-frequency oscillation events caused by large-scale wind power integration in domestic and foreign projects,then studies the multi-frequency oscillation problems,including wind turbine’s shafting torsional oscillation,sub/super-synchronous oscillation and high frequency resonance.The state of the art is systematically summarized from the aspects of oscillation mechanism,analysis methods and mitigation measures,and the future research directions are explored.