Hybrid State Estimation Approach for the Optimal Placement of Phasor Measurement Units
S. Gayathri1, R. Meenakumari2
1S. Gayathri, PG Scholar, Department of Electrical and Electronics Engineering, Kongu Engineering College, Erode, Tamil Nadu, India
2Dr.R. Meenakumari, Professor and Head, Department of Electrical and Electronics Engineering, Kongu Engineering College, Erode, Tamil Nadu, India.
Manuscript received on April 06, 2013. | Revised Manuscript received on April 28, 2013. | Manuscript published on May 05, 2013. | PP: 199-203 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1497053213/2013©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Power systems are rapidly becoming populated by Phasor Meaurement Units (PMU). Compared to conventional one(SCADA), PMU has synchrophasor technology and it measures the dynamic behaviour of the system. Real time monitoring operations are done through PMU in the smart grid environment. Finding a suitable location for the placement of PMU is an optimization problem which could be solved by various Optimization technique. PMUs actually measure the system state instead of indirectly estimating it, the idea to improve the quality of state estimate is that inclusion of this type of data in a state estimator. For analysis, operation and planning of power system state estimation and load flow analysis is most important. A hybrid state estimation technique (fixing a PMU in the conventional load flow analysis) is applied for the test case system and the results are validated. The true value is obtained by load flow analysis and the estimated value is obtained by weighted least squares (WLS) state estimation technique. From the simulated results it is observed that the residue will be less if PMU data’s are included.
Keywords: Newton Raphson Method, PMU, State Estimation, Load Flow, Weighted Least Squares.