Robust State Estimator (Smart Electric Grid, LLC)
Robust State Estimator(Smart Electric Grid, LLC) 

Accurate Non-Divergent State Estimator

Accurate Non-Divergent State Estimator (AND-SE) which has been recently developed at SEG is capable of providing a feasible voltage solution under any circumstances for power utility companies. AND-SE is a state estimator which can avoid the crashes of system monitoring, and it has solved the divergence and robustness problem of state estimator which is open for more than 50 years.

 

AND-SE is an ideal offline backup state estimator for power companies who are willing to improve the levels of robustness and data accuracy of daily operations.

The PROMISES of AND-SE:

1) Save the cost of engineers to manually fix the divergence problem of your state estimators.

2) No worries about reporting the divergence of your state estimators to NERC (a fine may be imposed if the divergence is severe).

3) No worries about the divergence of your post power flow analysis.

4) Increase the reliability of your EMS by 45%.

5) Largely improve the data quality (especially the reactive power and voltage magnitudes) in your daily operations.

6) Help detect the bad data which cause the divergence of your state estimator, and provide accurate true values of the contaminated measurements.

AND-SE is more accurate than WLS based state estimator

AND-SE is an ideal offline backup state estimator for power companies who are willing to improve the levels of robustness and data accuracy of daily operations.

 

The Unique Features of AND-SE:

1) Insensitive to Topology errors, Parameter errors, and Measurement errors.

2) Capable of accurately recovering the True values of bad data.

3) Leverage Point Does Not exist for AND-SE.

4) 100% convergence once the system is observable.

5) The cutting-edge technology can be used in power transmission network SE, power distribution network SE, linear SE, as well as dynamic SE.

Brief Introduction to Non-Divergent State Estimator
Introduction_to_NDSE_2021_April.pdf
Adobe Acrobat document [331.0 KB]

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