A Study on Non-Technical Loss Detection in Distribution Systems Using Semi-Definite Programming Method based State Estimation

碩士 === 國立中山大學 === 通訊工程研究所 === 103 === Before starting the development of smart grid, the users’ power consumption is collected by the traditional mechanical meter reading. People with bad intention remodel the mechanical meters to make the readings inaccurate. When the power company charges for the...

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Bibliographic Details
Main Authors: Wei-hung Lee, 李維紘
Other Authors: Chao-Kai Wen
Format: Others
Language:zh-TW
Published: 2015
Online Access:http://ndltd.ncl.edu.tw/handle/7nd372
Description
Summary:碩士 === 國立中山大學 === 通訊工程研究所 === 103 === Before starting the development of smart grid, the users’ power consumption is collected by the traditional mechanical meter reading. People with bad intention remodel the mechanical meters to make the readings inaccurate. When the power company charges for the electric bill, they get the wrong reading, and thus people can achieve the goal of stealing electric power. Stealing electric power also called non-technical loss. The stealing of electric power is a critical issue to be solved. In the future, a smart meter can immediately monitoring the whole system and get the instant operating data. By properly using the data from smart meters, the correctness of the operation data can be guaranteed. In this thesis, we consider state estimation to detect tampering data. Conventionally, weighted least-squared error based state estimation is used. In order to obtain better state estimation solution, we use semi-definite programming to solve this problem. The convex semi-definite relaxation (SDR) technique is further pursued to render the nonconvex R-SE problem efficiently solvable. Compared with the conventional Newton''s method, the semi-definite programming can achieve the global optimal solution rather than local optimal solutions. A transmission system and distribution system are performed to validate the effectiveness of this method. After the detection, the historical data is used to verify the possibility of anomalies. The integration of state estimation using on the received measured value and probability model using on the historical data can give a good trace of the users. The proposed process that involves stealing detection, confirm suspect, and data correction can solve the non-technical loss problem.