Advanced modeling and computational methods for distribution system state estimation

Distribution system monitoring;Power system state estimation;Synchrophasors;Linear power flow modeling;Semidefinite programming;Distribution networks;Voltage regulators

Klauber, Cecilia ; Zhu ; Hao

关键词: Distribution system monitoring;    Power system state estimation;    Synchrophasors;    Linear power flow modeling;    Semidefinite programming;    Distribution networks;    Voltage regulators;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/95400/KLAUBER-THESIS-2016.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Growing penetration of distributed energy resources and smart grid technologies interfacing with the power distribution network motivate the continued advancement of accurate and robust system monitoring tools. Traditional state estimation approaches rely on iterative methods to solve the weighted least squares problem because of the nonlinear relationship between the power measurements and voltage phasor state. It is known that these methods may be prone to convergence and numeric instability issues, such as in the presence of a measurement set of diverse quality. In this thesis, distribution system state estimation techniques are developed to address this monitoring need and take advantage of recent interest in alternative power flow models for distribution systems and convex and quadratic optimization methods. Therefore, semidefinite and quadratic programming methods, enabled by alternative power flow models, are leveraged to provide accurate solutions that are robust to various measurement types yet computationally efficient.The first proposed method employs a reformulation of the power flow measurement equations that captures the quadratic relationship between power and voltage. The state estimation problem is cast as a semidefinite program and gains the desirable convergence and solution accuracy characteristics therein. This method attains near-optimal performance without suffering from the numerical issues caused by variety of measurement quality, specifically the inclusion of virtual measurements at zero-injection nodes. The second method utilizes linearized power flow equations to cast the problem as a quadratic program with linear constraints. With minimal added computational complexity, the estimate is improved by including approximations of the nonlinear terms ignored during the linearized model development. This method also efficiently provides a reliable state estimate while avoiding the ill-conditioning issues that plague the traditional iterative methods. Numerical tests have been successfully performed on the IEEE 13-bus and 123-bus case studies.