Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU

Faults in distribution networks occur unpredictably, causing a threat to public safety and resulting in power outages. Automated, efficient, and precise detection of faulty sections could be a major element in immediately restoring networks and avoiding further financial losses. Distributed generati...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-01, Vol.22 (3), p.945
Main Authors: Mirshekali, Hamid, Dashti, Rahman, Keshavarz, Ahmad, Shaker, Hamid Reza
Format: Article
Language:English
Subjects:
Algorithms
Distributed generation
Electric Impedance
Electric potential
Fault location
fault section location
Linear algebra
Machine Learning
Mathematical functions
Measuring instruments
Methods
micro-phasor measurement units
neighborhood component analysis
Phasors
Power failure
Public safety
Records
Signal processing
Simulation methods
Substations
Support Vector Machine
Support vector machines
Voltage
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Summary:Faults in distribution networks occur unpredictably, causing a threat to public safety and resulting in power outages. Automated, efficient, and precise detection of faulty sections could be a major element in immediately restoring networks and avoiding further financial losses. Distributed generations (DGs) are used in smart distribution networks and have varied current levels and internal impedances. However, fault characteristics are completely unknown because of their stochastic nature. Therefore, in these circumstances, locating the fault might be difficult. However, as technology advances, micro-phasor measurement units (micro-PMU) are becoming more extensively employed in smart distribution networks, and might be a useful tool for reducing protection uncertainties. In this paper, a new machine learning-based fault location method is proposed for use regardless of fault characteristics and DG performance using recorded data of micro-PMUs during a fault. This method only uses the recorded voltage at the sub-station and DGs. The frequency component of the voltage signals is selected as a feature vector. The neighborhood component feature selection (NCFS) algorithm is utilized to extract more informative features and lower the feature vector dimension. A support vector machine (SVM) classifier is then applied to the decreased dimension training data. The simulations of various fault types are performed on the 11-node IEEE standard feeder equipped with three DGs. Results reveal that the accuracy of the proposed fault section identification algorithm is notable.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22030945