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PhD Final Exam – Tao Lyu

The Partitioning Technique of Power System to Improve the Observability of Sub-System for Multi-area State Estimation

An accurate state estimation plays an essential role in power system operation and planning in energy management systems. However, existing multi-area state estimation researches have not focused on the importance of system clustering. The clustering mechanism divides or partitions a system according to user-defined criteria. Few published research works have mentioned the importance of considering the electrical properties of a power system while devising their partitioning methods. To the best of our knowledge, these publications have not considered the application of such a concept to multi-area state estimation. This research attempts to model a partitioning technique of the power system whose purpose is to ensure the sub-systems observability prior to the multi-area state estimation. Hence, the accuracy of the multi-area state estimation could be improved. A modified genetic algorithm based phasor measurement unit (PMU) placement is introduced in this thesis, which includes the electrical distance based additional PMU installation technique.The modified partitioning method is introduced in this thesis based on a genetic algorithm partitioning algorithm. In the modified partitioning method, a proposed genetic index is proposed aiming to include the consideration of system observability, which employs the proposed PMU placement technique to represent the sub-system observability. In addition to the partitioning method, few publications have consider the state estimation by only employing PMU measurements. Alternatively, few publications have considered employing the noise statistic estimation technique to the state estimation to improve the convergence of the estimation process. A cubature Kalman filter based algorithm (CKF) is used in the the-sis to solve the state estimation, in which only PMU measuring data is employed. The online noise statistic estimation technique is incorporated into the CKF to improve the convergence. A modified two-level MASE is introduced to implement the modified CKF. The modified partitioning method is applied to the modified multi-area state estimation algorithm. By employing all the techniques introduced in this thesis, the improvement of accuracy and convergence can be achieved.

Major Advisor: Mario Magaña
Committee: Eduardo Cotilla-Sanchez
Committee: Ted Brekken
Committee: Jinsub Kim
GCR: William Warnes

Friday, March 20 at 1:00pm to 3:00pm

Kelley Engineering Center, 1005
110 SW Park Terrace, Corvallis, OR 97331

Event Type

Lecture or Presentation

Event Topic

Research

Organization
Electrical Engineering and Computer Science
Contact Name

Dakota Nelson

Contact Email

eecs.gradinfo@oregonstate.edu

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