Statistical analysis of the measurement data manipulation impact on power system state estimation

Abstract

The integration of information and communication technology, and cyber components, electricity infrastructures have made power systems more open and accessible from outside networks. Thus, the system becomes more vulnerable to data manipulations. This paper aims at analyzing the impact of data manipulation, on power system state estimation under various system operating conditions and explores various options to recommend a robust solution approach under such uncertainties. In-state estimation, the measurements can be manipulated either by creating a contingency to the system or by interfering with injections or flows. Contribution of this work lies in analysing simulated impact, statistically through the computation of errors namely, mean error, standard error, standard deviation, and variance indices and also by a parametric test Further, the performance and robustness of the conventional and evolutionary methods, employed for state estimation, are analysed under different measurement uncertainties. Results of these estimators are compared with base values of state variables obtained through load flow solution and then the percentage error in the estimated values for each case study is calculated. The efficiency and robustness of the Weighted Least Square method, Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing techniques are evaluated on IEEE 6, 14, and 30 bus systems. Finally, a comparative analysis of the statistical results is conducted, proving that the GA-based state estimation is more efficient and robust than other conventional and evolutionary techniques.