Power System State Estimation Using Hybrid SCADA and PMU Segmentation
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Solution Overview
Problem
Existing state estimation methods for power systems using hybrid SCADA and PMU measurements face challenges in achieving accurate and timely results due to the differences in measurement frequency and accuracy between SCADA and PMU systems.
Innovation Solution
The method involves grouping power system buses into PMU and SCADA observed areas based on measurement types, using a two-level computation procedure to determine states, and formulating linear and nonlinear models for each area, with pseudo measurements added to compensate for inaccuracies and improve estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hybrid SCADA and PMU measurements are used for state estimation, then measurement accuracy and frequency are improved, but computational complexity and solution time increase
Solution Approach 1:
The power system is divided into multiple areas based on measurement types (PMU areas and SCADA areas). Each area is estimated separately using appropriate models, reducing the overall computational complexity while maintaining accuracy. The segmentation allows parallel processing and reduces the size of matrices that need to be inverted.
Solution Approach 2:
The problem is transformed from a single-level nonlinear estimation problem into a two-level hierarchical structure. The first level performs linear estimation for PMU areas, and the second level performs nonlinear estimation for SCADA areas using results from the first level. This dimensional change in the solution approach reduces computational burden.
2Measurement precision
If hybrid SCADA and PMU measurements are used for state estimation, then measurement accuracy and frequency are improved, but solution time increases
Solution Approach 1:
By segmenting the system into PMU and SCADA areas, the estimation can be performed in parallel for different areas, reducing overall solution time. The PMU areas with higher measurement frequency can be processed independently and faster than traditional SCADA-based estimation.
Solution Approach 2:
The first-level linear estimation for PMU areas is performed before the second-level nonlinear estimation for SCADA areas. The results from the first level are used as initial values or constraints for the second level, accelerating convergence and reducing solution time.
3Reliability
If a single-level nonlinear model is used for hybrid measurements, then comprehensive estimation is achieved, but computational burden increases
Solution Approach 1:
The single-level nonlinear model is segmented into two levels: a first-level linear model for PMU areas and a second-level nonlinear model for SCADA areas. This segmentation reduces the computational burden by handling the more computationally intensive nonlinear estimation only for the SCADA areas, while using efficient linear estimation for PMU areas.
Solution Approach 2:
Instead of applying nonlinear estimation to the entire system, the invention applies nonlinear estimation only partially to SCADA areas where it is most beneficial, while using linear estimation for PMU areas where it provides sufficient accuracy with less computational effort.
Data Source
AI summary
A method determines voltages of buses of a power system. Values the voltages include a magnitude and a phase angle. The buses of the power system are grouped in a first area and a second area based on a type of measurement associated with each bus. The first area and the second area have at least one common bus, and wherein at least one bus in the first area is associated with a first type of measurement, and at least one bus in the second area is associated with a second type of measurement. Next, the method determines sequentially voltages of the buses of the first and the second areas.