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Title: Integrated load and state estimation using domestic smart meter measurements
Author: Al-Wakeel, Ali
ISNI:       0000 0004 5922 8392
Awarding Body: Cardiff University
Current Institution: Cardiff University
Date of Award: 2016
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The UK Government is promoting the decarbonisation of the power sector. The electrification of transport and heating, installation of distributed generators, development of smart grids and creation of an electricity and gas smart metering system are in progress. Higher penetrations of distributed generation and low carbon loads may lead to operational difficulties in distribution networks. Therefore, increased real-time monitoring and control becomes a necessary requirement. Distribution network operators will have available to them smart meter measurements to facilitate safe and cost-effective operation of distribution networks. This thesis investigates the application of smart meter measurements to extend the observability of distribution networks. Three main aspects were covered in this work: 1. The development of a cluster analysis algorithm to extract consumption patterns from smart meter measurements. The The k-means based cluster analysis algorithm was demonstrated on measurements that were obtained from the Irish Smart Metering Trials. Daily and segmented load profiles of individual and aggregated domestic smart meters were clustered. 2. The development of a load estimation method to estimate missing and future measurements of smart meters. The load estimation algorithm uses the outputs of the clustering algorithm; and investigates the application of different distance functions to estimate any lost measurements. Different durations of lost measurements were simulated to assess the accuracy of the estimated load. 3. The development of an integrated load and state estimation algorithm to extend the observability of distribution networks. The developed load estimator provides pseudo measurements to an Iteratively Re-Weighted Least Squares (IRWLS) state estimator. The capability of the IRWLS state estimator was investigated using measurements from a low voltage microgrid. The IEEE 33 bus medium voltage distribution network was used to assess the performance of the integrated load and state estimation algorithm. The developed load and state estimator was capable of estimating the voltage magnitude and phase angle, at each busbar of the distribution network, with high accuracy. For one hour of missing measurements, the Mean Absolute Percentage Error (MAPE) of the estimated voltage magnitude was less than 0.03%. For 24 hours of missing measurements, the estimated voltage magnitudes had a MAPE that was less than 0.5%.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: TK Electrical engineering. Electronics Nuclear engineering