A robust state estimator against constant measurement delay based on the sensitivity penalisation of model‐parameter errors for systems with no exogenous inputs

State estimation (SE) is a primary data processing algorithm which is utilised by the control centres of advanced power systems. The most generally utilised state estimator is based on the weighted least squares (WLS) approach which is ineffective in addressing gross errors of input data of state estimator. This paper presents an innovative robust estimator for SE environments to overcome the non-robustness of the WLS estimator. The suggested approach not only includes the similar functioning of the customary loss function of WLS but also reflects loss function built on the modified WLS (MWLS) estimator. The performance of the proposed estimator was assessed based on its ability to decrease the impacts of gross errors on the estimation results. The properties of the suggested state estimator were investigated and robustness of the estimator was studied considering the influence function. The effectiveness of the proposed estimator was demonstrated with the help of examples which also indicated non-robustness of MWLS estimator in SE algorithm.

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Detection and Diagnosis of Model Parameter and Noise Variance Changes Using Sliding Windows Approach

Recent Patents on Signal Processinge ◽

10.2174/2210686311101010080 ◽

2011 ◽

Vol 1 (1) ◽

pp. 80-87

Author(s):

Theodor D. Popescu

Keyword(s):

Noise Variance ◽

Sliding Windows ◽

Model Parameter ◽

Detection And Diagnosis

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State Estimation Using Randomly Delayed Measurements

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.2897399 ◽

1993 ◽

Vol 115 (1) ◽

pp. 19-26 ◽

Cited By ~ 71

Author(s):

A. Ray ◽

L. W. Liou ◽

J. H. Shen

Keyword(s):

State Estimation ◽

Minimum Variance ◽

The State ◽

Sensor Data ◽

State Estimator ◽

Uniform Asymptotic Stability ◽

Simulation Experiments ◽

State Estimate ◽

Sampling Instant ◽

Recursive Relations

This paper presents a modification of the conventional minimum variance state estimator to accommodate the effects of randomly varying delays in arrival of sensor data at the controller terminal. In this approach, the currently available sensor data is used at each sampling instant to obtain the state estimate which, in turn, can be used to generate the control signal. Recursive relations for the filter dynamics have been derived, and the conditions for uniform asymptotic stability of the filter have been conjectured. Results of simulation experiments using a flight dynamic model of advanced aircraft are presented for performance evaluation of the state estimation filter.

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