Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730777
Title: A data analytic approach to automatic fault diagnosis and prognosis for distribution automation
Author: Wang, Xiaoyu
Awarding Body: University of Strathclyde
Current Institution: University of Strathclyde
Date of Award: 2017
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Abstract:
Distribution Automation (DA) is deployed to reduce outages and to rapidly reconnect customers following network faults. Recent developments in DA equipment have enabled the logging of load and fault event data, referred to as pick-up activity. This pick-up activity provides a picture of the underlying circuit activity occurring between successive DA operations over a period of time and has the potential to be accessed remotely for off-line or on-line analysis. The application of data analytics and automated analysis of this data supports reactive fault management and post fault investigation into anomalous network behavior. It also supports predictive capabilities that identify when potential network faults are evolving and offers the opportunity to take action in advance in order to mitigate any outages. This thesis details the design of a novel decision support system to achieve automatic fault diagnosis and prognosis for DA schemes. It combines detailed data from a specific DA device with SCADA data, by utilising rule-based, data science techniques (e.g. data mining and clustering techniques) to deliver the diagnostic and prognostic functions. These are applied to 11kV distribution network data captured from Pole Mounted Auto-Reclosers (PMARs) as provided by a leading UK network operator. This novel automated analysis system diagnoses the condition of device faults, the nature of a circuit's previous fault activity, identifies underlying anomalous circuit activity, and highlights indications of problematic events gradually evolving into a full scale circuit fault using prognostic functionality. The novel contributions also include the characterisation and identification of semi-permanent faults and a re-usable methodology and approach for applying data analytics to any DA device data sets in order to provide diagnostic decisions and mitigate potential fault scenarios.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.730777  DOI: Not available
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