Abstract
Power system operators need real-time information to reliably maintain delivery of energy to the consumers in a modern electric power network characterized by dynamically varying power flow and infrastructure availability. State estimation involves minimizing a sparse nonlinear sum-of-squares objective function. The conventional method of solution is Gauss–Newton method which is subjected to divergence issues, particularly in the presence of bad data and topology error. In this paper, numerical optimization techniques like Powell’s dog leg (PDL), indefinite Gauss–Newton Powell’s dog leg (IGNPDL) and robust incrementalized least-squares estimation are presented. IGNPDL is an incrementalized version of the PDL method which is generally used suitably in sparse least-squares minimization. These methods are robust to nonlinearity of objective function. The robustness is extendable to numerical ill-conditioning also which arises due to topology errors and bad data. Data from phasor measurement units are also used in the SE to enhance the performance. The proposed algorithm is simulated and evaluated on IEEE 14-bus and IEEE 118-bus systems. The proposed methods converge in the presence of topology errors, and the error percentage in the estimated values is considerably reduced in the presence of bad data. The simulation results bring out the effectiveness of these methods.
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Bindu, S., Ushakumari, S. & Savier, J.S. Robust Incrementalized Least-Squares Estimation Method in State Estimation with Phasor Measurement Unit Measurements. J. Inst. Eng. India Ser. B 103, 1315–1325 (2022). https://doi.org/10.1007/s40031-021-00707-1
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DOI: https://doi.org/10.1007/s40031-021-00707-1