Optimal Sparse PMU Placement for Distribution Network State Estimation
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Solution Overview
Problem
Distribution networks face challenges in state estimation due to the limited number of real-time measurements from phasor measurement units (PMUs), making it infeasible to place PMUs at every node, and requiring an optimal placement method for accurate real-time monitoring and control.
Innovation Solution
The method involves constructing a quotient gradient system (QGS) based on constraint sets to integrate measurements, identify and correct measurement residuals, and iteratively reconstruct the QGS to achieve steady-state state estimation using a limited number of PMUs, allowing for accurate estimation of state variables like voltage amplitudes and phase angles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If PMUs are placed at every node of the distribution network, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The distribution network is divided into multiple regions or zones, and PMUs are strategically placed at key buses within each segment. This segmentation allows the system to achieve comprehensive monitoring coverage through a reduced number of PMUs by focusing measurements on critical network sections rather than deploying PMUs at every node.
Solution Approach 2:
The patent introduces a state estimation system that acts as an intermediary between limited PMU measurements and the need for complete network state knowledge. This intermediary system uses mathematical models and algorithms to infer the state of unmeasured buses based on measurements from strategically placed PMUs, thereby achieving full system observability without requiring PMUs at every node.
2Measurement precision
If a large number of PMUs are deployed, then state estimation accuracy is improved, but loss of energy and cost increase
Solution Approach 1:
The system implements partial action by deploying PMUs only at critical buses rather than all buses, and uses state estimation algorithms to compensate for the missing measurements. This approach achieves sufficient state estimation accuracy for reliable operation while consuming significantly less energy and incurring lower costs compared to full PMU deployment.
Solution Approach 2:
The patent changes the operational parameters of the measurement system by transitioning from a dense PMU deployment pattern to a sparse deployment pattern combined with advanced state estimation. This parameter change in the measurement strategy maintains estimation accuracy while reducing the number of active PMUs and their associated energy consumption.
3Device complexity
If PMUs are sparsely placed in the distribution network, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system performs preliminary action by pre-calculating optimal PMU placement locations and pre-configuring state estimation algorithms tailored to the specific network topology. This preliminary preparation ensures that even with sparse PMU placement, the state estimation accuracy is maintained by having the estimation system ready to effectively process the limited measurements available.
Solution Approach 2:
The state estimation system continuously receives feedback from PMU measurements and iteratively refines its estimates of the network state. This feedback mechanism allows the system to maintain high measurement precision despite sparse PMU placement by using the available measurements to continuously update and improve the estimated state of all buses in the network.
Data Source
AI summary
A method for state estimation of a distribution network comprises: (a) obtaining measurements from phasor measurement units (PMUs) placed at buses in the distribution network; (b) constructing a quotient gradient system (QGS) based on a constraint set H that relates the measurements to state variables of the distribution network; (c) integrating the QGS to reach a steady state; (d) identifying one or more of the state variables whose measurement residuals violate a measurement residual constraint in the constraint set H; (e) integrating a reconstructed QGS, which is reconstructed based on the constraint set H by setting the identified one or more state variables to values of corresponding PMU measurements; (f) iterating steps of (d) and (e) until no measurement residuals violate the measurement residual constraint, to thereby obtain the state estimation; and (g) reporting the state estimation to a control system during real-time monitoring of the distribution network.