Akademik Veri Yönetim Sistemi
Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye
Tezin Onay Tarihi: 2015
Tezin Dili: İngilizce
Öğrenci: Volkan Özdemir
Danışman: MURAT GÖL
Özet:There are parameter errors in power system models due to the change of weather conditions, such as temperature and humidity changes, miscommunication between the control center and the transducers of circuit breakers and tap changers, etc. Because of the incorrect parameters, the state estimator may provide biased state estimates which may lead to many serious economic and operational results. In order to prevent that, one must identify and correct those parameter errors. This work proposes a local parameter estimator based on the robust Least Absolute Value (LAV) estimator. Considering the increasing number of Phasor Measurement Units (PMUs), their fast refreshing rate and high accuracy, the proposed method will employ PMU measurements in local parameter estimation which will provide a more reliable system model. In general, a PMU measures the current phasor owing through a branch, and the voltage phasor of the sending bus of the considered branch. However, it is known that those two measurements are not enough to estimate the parameters of that branch. Therefore, multiple measurements taken in di erent time instants will be used in the parameter estimation process for measurement redundancy, assuming that the state estimates are also available. It is known that the LAV estimator is a computationally expensive despite being robust in the presence of enough measurement redundancy. Note that, the parameter estimation problem is a non-linear problem, which increases the computational burden; since the vector to be estimated consists of not only the parameters of the considered branch, but also the bus voltages of the sending and the receiving ends of the considered branch. This de ciency will be eliminated by performing local parameter estimation, which is a very small sized problem compared to the state estimation problem's size.