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Data-driven disturbance source identification for power system oscillations using credibility search ensemble learning
by
Ul Banna, Hasan
, Solanki, Jignesh
, Solanki, Sarika Khushalani
in
Algebra
/ B0240Z Other topics in statistics
/ B8110B Power system management, operation and economics
/ B8150 Power system measurement and metering
/ C6170K Knowledge engineering techniques
/ Classification
/ classification models
/ credibility search ensemble learning
/ Data mining
/ data-driven disturbance source identification
/ Decision making
/ Dynamical systems
/ Energy
/ Ensemble learning
/ highly ranked models
/ learning (artificial intelligence)
/ low-frequency oscillations
/ measurement errors
/ Measuring instruments
/ Methods
/ offline credibility estimation
/ Oscillations
/ phasor measurement
/ Phasors
/ power supply quality
/ power system degrade power quality
/ power system faults
/ power system interconnection
/ power system measurement
/ power system oscillations
/ Research Article
/ source location
/ Support vector machines
/ Topology
/ western interconnection power system
2019
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Data-driven disturbance source identification for power system oscillations using credibility search ensemble learning
by
Ul Banna, Hasan
, Solanki, Jignesh
, Solanki, Sarika Khushalani
in
Algebra
/ B0240Z Other topics in statistics
/ B8110B Power system management, operation and economics
/ B8150 Power system measurement and metering
/ C6170K Knowledge engineering techniques
/ Classification
/ classification models
/ credibility search ensemble learning
/ Data mining
/ data-driven disturbance source identification
/ Decision making
/ Dynamical systems
/ Energy
/ Ensemble learning
/ highly ranked models
/ learning (artificial intelligence)
/ low-frequency oscillations
/ measurement errors
/ Measuring instruments
/ Methods
/ offline credibility estimation
/ Oscillations
/ phasor measurement
/ Phasors
/ power supply quality
/ power system degrade power quality
/ power system faults
/ power system interconnection
/ power system measurement
/ power system oscillations
/ Research Article
/ source location
/ Support vector machines
/ Topology
/ western interconnection power system
2019
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Data-driven disturbance source identification for power system oscillations using credibility search ensemble learning
by
Ul Banna, Hasan
, Solanki, Jignesh
, Solanki, Sarika Khushalani
in
Algebra
/ B0240Z Other topics in statistics
/ B8110B Power system management, operation and economics
/ B8150 Power system measurement and metering
/ C6170K Knowledge engineering techniques
/ Classification
/ classification models
/ credibility search ensemble learning
/ Data mining
/ data-driven disturbance source identification
/ Decision making
/ Dynamical systems
/ Energy
/ Ensemble learning
/ highly ranked models
/ learning (artificial intelligence)
/ low-frequency oscillations
/ measurement errors
/ Measuring instruments
/ Methods
/ offline credibility estimation
/ Oscillations
/ phasor measurement
/ Phasors
/ power supply quality
/ power system degrade power quality
/ power system faults
/ power system interconnection
/ power system measurement
/ power system oscillations
/ Research Article
/ source location
/ Support vector machines
/ Topology
/ western interconnection power system
2019
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Data-driven disturbance source identification for power system oscillations using credibility search ensemble learning
Journal Article
Data-driven disturbance source identification for power system oscillations using credibility search ensemble learning
2019
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Overview
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.
Publisher
The Institution of Engineering and Technology,John Wiley & Sons, Inc,Wiley
Subject
/ B0240Z Other topics in statistics
/ B8110B Power system management, operation and economics
/ B8150 Power system measurement and metering
/ C6170K Knowledge engineering techniques
/ credibility search ensemble learning
/ data-driven disturbance source identification
/ Energy
/ learning (artificial intelligence)
/ Methods
/ offline credibility estimation
/ Phasors
/ power system degrade power quality
/ power system interconnection
/ Topology
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