Implementation of Autoregressive(AR)Method to Pre-Filter the Set of Measurements

This paper describes an approach to identify and change the measurement weights used in Weight Least Square (WLS) estimation method employed in State Estimation (SE). The individual measurement is assigned with their own weighting
factor based on technical experience by the engineers. However, ents could occur in a real time system. Thus, the higher weighting factor or wrongly assigned weighting factor to the measurement could lead to flag the measurement as bad. This paper describes a pre-screening process to identify the bad measurements and the measurement weights before WLS estimation method employed in
SE is performed. The autoregressive (AR) method proposed in this paper is used to predict the data and at the same time filtering the logical weighting factors that have been assigned to the identified bad measurements. The AR algorithms known as Burg and Modified Covariance (MC) are used to calculate the one-step-ahead of the predicted values of the state variables. The performance of the AR filter is tested using 5-bus, IEEE 14-bus, IEEE 24-bus, IEEE 57-bus, IEEE 118-bus, IEEE 300-bus system and local utility network consisting 103-bus. Simulation results are presented and compared with the measured values to validate the proposed method.