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Enhancing state estimation in distribution and transmission systems using advanced metering infrastructure

University of British Columbia

State estimation is the heart of many tools used in operations of distribution and transmission power systems. The quality of distribution systems state estimation (DSSE) typically suffers from a lack of adequate/accurate measurements and has not been fully implemented by many utilities. Recently, as part of many smart grid related initiatives to modernize power systems, electric utilities started to invest in advanced metering infrastructure (AMI) throughout their distribution systems. The main challenge in this area is that AMI measurements are generally not synchronized, and the difference between the measuring times of smart meters can be significant. In generation and transmission systems, the transmission system state estimation (TSSE) is already prevalent in many utilities. However, TSSE typically suffers from four major problems: partial unobservability, numerical ill-conditioning, bad data, and low accuracy. This thesis is based on three contributions. Firstly, an innovative method is developed to incorporate the non-synchronized measurements coming from AMI based on the credibility of each available measurement and appropriately adjusting the statistical property of the measurement signals. To illustrate the effectiveness of the proposed method, it is compared with traditional approach used in DSSE and the results show the improvements in the accuracy of DSSE. Next, based on the interconnection of the transmission system and distribution systems at PQ buses (feeder heads), a novel approach in TSSE method is presented which uses the DSSE results to provide additional measurements at the PQ buses of the transmission system. Comparisons between the traditional TSSE and the proposed TSSE show that significant improvements are achieved. The third contribution is the methodology for identification of electricity theft points in distribution systems without violating privacy of consumers. The proposed approach models theft as bad data and consists of two stages. Firstly, the multiple bad data identification problem is solved using a heuristic optimization method to locate the points of theft which have redundant measurements. In the second stage, regarding identification of theft points which do not have redundant measurements, a method is proposed based on the discrepancies between the measured and estimated voltage magnitudes. Simulations results demonstrate the effectiveness of the proposed approach.

Text

2015-08-20

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/54551

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/