State estimation based fault location using pmu measurements

---

Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2016

Öğrenci: AHMET ÖNER

Danışman: MURAT GÖL

Özet:

Accurate location of a permanent fault on a transmission line is extremely important to restore the service in the shortest time possible, which directly affects the operational cost and system reliability. There are measurement based fault location methods in the literature, which require a high number of installed devices at substations. In this thesis study, an accurate and computationally fast strategy for fault location based on well-known Weighted Least Squares state estimator is presented. The proposed method employs Phasor Measurement Unit (PMU) measurements recorded during the fault, due to the fast refresh rate of PMUs. PMUs provide synchronized voltage and current measurements. The synchronization of the PMU measurements is achieved using Global Positioning System (GPS). Those measurements are used to identify the faulted line via the estimated current flows in the system and then locate the fault on the flagged line disregarding the value of the fault impedance. The proposed method makes use of multiple PMU measurements received in consecutive time instants and hence reduces the required number of measurements for the solution of the fault location problem. Moreover, use of those multiple measurements improves the robustness of the proposed method against the bad data and incorrect parameters. The fault location problem is also solved with a modified formulation in order to show the robustness of the proposed method against the incorrect line parameters. The modified formulation includes estimation of the line parameters with multiple PMU measurements. The proposed method has an iterative solution, because of the non-linearity between the measurements and the states to be determined. This thesis also shows the importance of the initial values and indicates comments on selection of those values. The proposed method is validated using synthetic data in different computational environments.