Description:
Background
Accuracy of the network parameters has a strong influence on the results of power system state estimation. It has been shown earlier that normalized Lagrange multipliers can be used as a systematic way of identifying errors in network parameters. However, this approach carries a rather heavy computational burden limiting its practical utilization to small-size systems.
 
Technology Overview
This invention describes an efficient procedure that can be applied either to the single-scan or multiple-scan schemes with equal ease. The idea is to derive and compute only the necessary subset of the gain matrix and covariance matrix, thus avoiding the computation and storage of large dense matrices. The developed algorithm is simulated and tested on typical power systems. These tests confirm that the improvements in computational speed and memory requirements by the algorithm are significant. This technology enables detection and correction of network model parameter errors for very large scale utility power systems without the need to have any prior knowledge about suspect parameters or to have any arbitrary user interference with the detection or identification process. 
 
Benefits
- No need to specify a “suspect parameter set” in advance. All parameters can be processed simultaneously
- Detects and identifies measurement and parameter errors simultaneously. 
- Execution as an off-line application 
 
Applications
- Used as stand-alone network application in control centers by power grid operators to maintain a bias-free network model
- Used by utilities to periodically clean their databases from parameter errors
- Used as a tool to obtain the parameters of re-wired or modified transmission lines without going through the detailed derivations using first principles
 
Opportunity
- License
- Partnering
- Research collaboration
Patent Information:
Category(s):
-Networks
For Information, Contact:
Mark Saulich
Associate Director of Commercialization
Northeastern University
m.saulich@northeastern.edu
Inventors:
Ali Abur
Yuzhang Lin
Keywords: