DEVELOPMENT OF A STATE ESTIMATION BASED IMPROVED DETECTION AND LOCALIZATION OF NON-TECHNICAL LOSSES USING SMART METER MEASUREMENTS

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dc.contributor.authorZAKARIYYAH, Abdulkareem
dc.date.accessioned2019-09-12T10:40:58Z
dc.date.available2019-09-12T10:40:58Z
dc.date.issued2019-04
dc.descriptionA DISSERTATION SUBMITTED TO THE SCHOOL OF POSTGRADUATE STUDIES, AHMADU BELLO UNIVERSITY, ZARIA IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF A MASTER OF SCIENCE DEGREE (MSc.) IN POWER SYSTEM ENGINEERING DEPARTMENT OF ELECTRICAL ENGINEERING, FACULTY OF ENGINEERING, AHMADU BELLO UNIVERSITY, ZARIA NIGERIAen_US
dc.description.abstractThis research work presents the development of branch current based state estimation for Non-Technical Losses (NTLs) Detection and Localization. The use of weighted least square (WLS) state estimation for the evaluation of branch current of a network during theft is considered. In order to confirm the presence of theft in a network, current measurement value obtained from Distribution Transformer Controller (DTC) installed at substation was compared with that of all customers’ smart meters readings, a difference above an estimated threshold signifies the presence of theft. For the case of locating the point of theft, the concept of weighted least square state estimation was used for the evaluation of the actual branch current of each branch of the network despite theft, the estimated branch current is compared with the calculated branch current based on meter reading, and the difference is exploited in order to locate the point of location. The developed method was implemented on a 415V Low Voltage network used in this literature. The results obtained were validated by comparing it with the work of Marques et al., 2016. All modelling and analysis were carried out using OPENDSS and MATLAB R2015a. From the results obtained, when the total theft in the network is 30%, 40% or 50% the maximum variation of the estimated branch current are 0.62%, 0.83%, 1.02% respectively, these are taken to be the threshold for decision of theft in the network. It was also observed that the True Positive Rate (TPR) and The False Positive Rate (FPR) irrespective of the percentage of theft in the networkshow an improvement of 27.5% and 11.11% respectively. The method was further compared with other works where the used of machine learning was exploited. This method shows 7.5% improvement in terms of TPR than the use of Artificial Intelligent method.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/11867
dc.language.isoenen_US
dc.subjectDEVELOPMENT,en_US
dc.subjectSTATE ESTIMATION BASED IMPROVED DETECTIONen_US
dc.subjectLOCALIZATION,en_US
dc.subjectNON-TECHNICAL LOSSESen_US
dc.subjectSMART METER,en_US
dc.subjectMEASUREMENTS,en_US
dc.titleDEVELOPMENT OF A STATE ESTIMATION BASED IMPROVED DETECTION AND LOCALIZATION OF NON-TECHNICAL LOSSES USING SMART METER MEASUREMENTSen_US
dc.typeThesisen_US
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