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Title: | EKF based State Estimation of Power System including Solar PV based Generation |
Authors: | Sharma, Bandita |
Keywords: | Electrical 2013 Project Report 2013 Electrical Project Report Project Report 13MEE 13MEEE 13MEEE23 EPS EPS 2013 EE (EPS) Electrical Power Systems |
Issue Date: | 1-Jun-2015 |
Publisher: | Institute of Technology |
Series/Report no.: | 13MEEE23; |
Abstract: | State Estimation is the most vital function of the energy management system (EMS) and aims at analysing, monitoring and controlling the stability of the electric power system. Reliability of the modern power system greatly depends on the fact that how efficient and accurate is the state estimation. Due to the slow updation rate of SCADA systems, the traditional state estimators which are based on steady sate system model cannot capture the system dynamics very well. Thus, to overcome these limitations, wide area measurements and control systems (WAMAC) using PMUs are being implemented worldwide. WAMAC systems have the ability to capture dynamic system information which is beneficial for the state estimators of a power system in generating dynamic states i.e. synchronous generator rotor angle and synchronous generator speed giving an accurate picture of the overall condition of power network thereby leading to an enhanced situational awareness by the system operators. Moreover, though the power system planners have a variety of generation technologies to chose from, there is an increasing interest in the use of renewable. Due to excellent solar resource availability, it is intended to replace one of the synchronous generators by PV array in order to generate the adequate amount of power. Based on this point of view, changes taking place in the system dynamics due to penetration of solar into the grid is intended to be studied and estimated. The modeling of the system is carried out on standard WSCC 3-generator, 9-bus system in MATLAB. As a whole, the dynamic state estimation process is laid out based on Extended Kalman Filtering Technique. |
URI: | http://hdl.handle.net/123456789/6003 |
Appears in Collections: | Dissertation, EE (EPS) |
Files in This Item:
File | Description | Size | Format | |
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13MEEE23.pdf | 13MEEE23 | 1.38 MB | Adobe PDF | ![]() View/Open |
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