A Robust State Estimator Based on Maximum Constraints Satisfaction of Uncertain Measurements

`Abstract: A new robust estimator based on the concept of uncertainty in the measurements is developed in this paper. The uncertainty in the measurements is modeled via deterministic upper and lower bounds on measurement errors, which take into account known meter accuracies. Inequality constraints are constructed to model the uncertainty in the measurements. A solution point satisfying most inequality constraints is the objective of the proposed estimator. Hence, this estimator is known as Maximum Constraints Satisfaction (MCS). The Robustness and performance of the proposed estimator is discussed via simulated problems of simple regression examples and D.C. three-bus system. Various scenarios of leverage measurements and bad data have been considered for further assessment of the performance of the MCS estimator. In particular, it is shown that the (MCS) estimator performs significantly well in situation where collinearity exists in the measurements. Results show that the proposed estimator is an accurate and reliable estimator.