QGS State Estimation

Nonlinear PMU-only and Hybrid (SCADA/PMU/AMI) State Estimation

State Estimation provides the foundation for control center operations such as monitoring, analysis, optimization, and control functions. The conventional method, Weighted-Least Squares (WLS), has been used for over half a century with little change. However, with state estimation now needed in distribution networks or using PMU data where measurement availability is limited, there is a need for alternative state estimation methods.

BSI QGS State Estimation provides reliability through backup or the enhancement of control center state estimation. QGS stands for Quasi-Gradient Systems, which takes a dynamic approach to solving the non-linear state estimation problem, accommodating scenarios where measurement redundancy is limited. This application can operate as stand-alone or when integrated with the Energy Management System (EMS) or Advanced Distribution Management Systems (ADMS). State estimation in the distribution network is key for determining important real-time information such as renewable energy integration. Utilities with a network of advanced metering infrastructure (AMI) can use BSI distribution state estimation for full system observability.


The QGS State Estimation can enhance conventional state estimators or serve as a backup to state estimators when they diverge.

QGS State Estimation can be used for:

  • PMU-only non-linear State Estimation
  • Hybrid State Estimation (combine PMU, SCADA, AMI, and other inputs)
  • Enhancing SCADA State Estimation

AMI-based Distribution State Estimation

AMI-based Distribution State Estimation has been trending recently. BSI offers a low-cost AMI-only state estimation tool(patent pending) for distribution power networks that can serve as the basis for active network management (ANM). Inputs considered include:

Phase Identification via AMI

BSI’s Phase Identification engine will provide utilities with the phase at every node using AMI data. This will reduce the need for labor costs to physically identify the phase and increase the efficiency of the phase identification. Having the phase identification at every node will allow for further analysis and control improvements.

Key Functions

  • Great convergence property for power systems with limited measurements
  • Robustness against a set of measurements with a large quantity of current magnitudes for medium and low-voltage distribution systems
  • Flexible formulation with residual constraints for every measurement
  • Easily incorporate PMU measurements, which may not provide full system observability between two refreshed SCADA data.
  • Easy integration of existing SE solvers and greatly improved SE results