QFS-CNN for sub-synchronous oscillation source location based on dissipating energy flow

Abstract

Sub-synchronous oscillation (SSO) caused by large-scale renewable energy generations can threaten the safe and reliable operation of power systems. In this paper, a dissipating energy-based quaternion feature set convolutional neural network (QFS-CNN) method is proposed to locate the SSO sources quickly and accurately, providing the foundation for isolation of the SSO sources from power systems in time. Firstly, aiming at the difficulty in feature extraction of SSO sources due to the poor observability of power system, a temporal and spatial feature extraction method based on oscillation energy is proposed to characterize the SSO sources information in the form of images with limited measurement data. The temporal and spatial feature images are extracted based on the variation of oscillation energy in time and the direction of energy flow power in space, respectively. Then, the correlation between the oscillation energy and the SSO sources location can be revealed by the feature images with less calculation and storage space. Secondly, a QFS-CNN method based on temporal and spatial feature images for SSO sources location is proposed to address the insufficient labeled data of SSO. The QFS data augmentation technique increases the feature set via meaningful recombination of the existing labeled feature images. Then, the augmentative feature set is used to ensure the model training accuracy of QFS-CNN to improve the location accuracy of SSO sources. Finally, the proposed method is demonstrated and evaluated by a modified IEEE 39-bus wind power generation system. Simulation results show that the method has high accuracy and stronger anti-noise ability in the case of poor system observability and few sample data.