Load-flow dependent twofold technique of tolerance selection for robust estimation of power system states

Accurate estimation of the voltage magnitudes, angles, and other states of the modern power system is essential for efficient monitoring. The precision of the meter readings affects the estimating procedure. However, the majority of traditional meter readings are incorrect, and some of them, known as bad-data, are far from the true levels. The estimation performance significantly declines when such poor measurements are present, either singularly or in multiple numbers. Some estimators have post-processing capabilities but are unable to deal with flawed data directly. One of the well-known robust estimators that can weed out faulty data during the esti-mating process is Least Measurement Rejected (LMR). For each measurement in the LMR formulation, a tolerance value must be carefully chosen. The performance of the estimation can be greatly enhanced by the careful se-lection of these numbers. The use of load-flow to choose the LMR tolerance values is recommended in this paper to prepare a twofold estimation process of robust nature. Several tests and analysis were done to determine how well the suggested load flow dependent LMR (LFLMR) estimator performed. It is contrasted with well-known estimators from the literature, including Weighed Least Square (WLS), its robust counterpart (RWLS), Weighted Least Absolute Value (WLAV), Iteratively Reweighted Least Square (IRLS), and seven distinct versions of LMR. Additionally, several load profile snapshots are simulated to test the LFLMR's robustness. In order to illustrate the effectiveness of the suggested estimator and to identify and estimate the wrongly measured values of the measurement series, IEEE standard 14, 30, and 118-bus power systems with various outlier scenarios are simulated.

Automated summary

Accurate estimation of voltage magnitudes, angles, and other states in the modern power system is crucial for efficient monitoring. This paper introduces a robust estimator called LMR, which can eliminate faulty data and improve estimation performance. By suggesting the use of load-flow calculations to select the estimator's parameters, the estimation accuracy is further enhanced. Simulation tests demonstrate the robustness of the suggested estimator.