Data-driven disturbance source identification for power system oscillations using credibility search ensemble learning

Abstract

Low-frequency oscillations in power system degrade power quality and may trigger blackouts. This study identifies the source location of these oscillations using measurements from phasor measurement unit (PMU), offline credibility estimation and classification models. The performance of these classification models is ranked for each reported feature to use highly ranked models during the online stage. This proposed framework named as credibility search ensemble learning was tested and validated with promising results using western interconnection power system in North America (WECC-179). The reliability and robustness of the proposed framework were checked against measurement errors in PMUs as well as for practical topology change scenarios. Experimental results and performance comparison with average weight-based approach proved that the proposed approach is capable enough to predict the source location of oscillations with good accuracy. An interfacing tool, for MATLAB-WEKA, was developed and employed in this work for validation and testing of the proposed approach.

Keywords

• power system faults
• power system measurement
• power supply quality
• power system interconnection
• learning (artificial intelligence)
• phasor measurement
• data-driven disturbance source identification
• power system oscillations
• credibility search ensemble learning
• low-frequency oscillations
• power system degrade power quality
• source location
• offline credibility estimation
• classification models
• highly ranked models
• western interconnection power system
• measurement errors