A Parametric Test based Analysis of State Estimation Techniques under Data Uncertainties

This work examines the statistical analysis of conventional and evolutionary strategies used to solve state estimation problems. All energy management systems use state estimation to determine the operational condition of the system. Moreover, with the rise of the electrical market and the notion of a smart grid, the assessment of system parameters has received considerable attention. Hence an assessment of the efficiency and robustness of various state estimation techniques used to compute the system parameters is very much required. This paper primarily focuses on the parametric tests used to access and compare the robustness of various state estimators. Case studies are conducted on IEEE 6 bus and 14 bus systems. In addition, this paper also provides a statistical evaluation of the performance of evolutionary algorithms with varying upper and lower optimal solution constraints. Furthermore, the algorithms' robustness under conditions of missing and infringed data is also determined. The findings derived from these estimators are compared with the base values, and the percentage error in estimated values is computed and analysed

A Parametric Test based Analysis of State Estimation Techniques under Data Uncertainties

837-849This work examines the statistical analysis of conventional and evolutionary strategies used to solve state estimation problems. All energy management systems use state estimation to determine the operational condition of the system. Moreover, with the rise of the electrical market and the notion of a smart grid, the assessment of system parameters has received considerable attention. Hence an assessment of the efficiency and robustness of various state estimation techniques used to compute the system parameters is very much required. This paper primarily focuses on the parametric tests used to access and compare the robustness of various state estimators. Case studies are conducted on IEEE 6 bus and 14 bus systems. In addition, this paper also provides a statistical evaluation of the performance of evolutionary algorithms with varying upper and lower optimal solution constraints. Furthermore, the algorithms' robustness under conditions of missing and infringed data is also determined. The findings derived from these estimators are compared with the base values, and the percentage error in estimated values is computed and analysed