Iterative Phasor Estimation for PMU Calibration Accuracy
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Solution Overview
Problem
Current phasor measurement unit (PMU) calibration methods fail to meet the high accuracy requirements for both static and dynamic conditions, especially under off-nominal frequency conditions, leading to inaccurate fault detection and state estimation in power systems.
Innovation Solution
A high-accuracy synchrophasor estimation algorithm that uses a dynamic phasor fitting model with an iterative solution method and a least-squares-based rate-of-change-of-frequency calculation, combined with signal filtering and post-processing techniques to suppress interference and improve measurement accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional phasor estimation algorithms are used for PMU calibration, then the measurement accuracy meets the basic requirements, but the accuracy is insufficient under off-nominal frequency conditions and dynamic conditions
Solution Approach 1:
The patent employs a dynamic phasor fitting model that adapts to changing signal conditions through iterative optimization. The algorithm dynamically adjusts fitting parameters based on real-time signal characteristics, enabling accurate measurements under off-nominal frequency and dynamic operating conditions where static models fail.
Solution Approach 2:
The iterative solution algorithm incorporates feedback mechanisms that continuously refine phasor and frequency estimates based on measurement residuals. This feedback loop enables the system to correct errors and converge on accurate values even under challenging off-nominal frequency conditions.
2Measurement precision
If iterative iterative solution algorithms are used to improve measurement accuracy, then phasor and frequency estimation precision increases, but calculation complexity and processing time increase
Solution Approach 1:
The patent transforms the complex nonlinear fitting problem into a simplified parameter estimation problem by changing variables. The iterative algorithm operates on transformed parameters that linearize the relationship between measurements and model parameters, reducing computational complexity while maintaining high accuracy.
Solution Approach 2:
The estimation process is segmented into multiple iterative steps, each handling a specific aspect of parameter estimation. This segmentation allows the complex problem to be solved through a series of simpler sub-problems, making the overall algorithm more manageable and computationally efficient.
3Measurement precision
If signal filtering is applied to suppress interference, then measurement accuracy improves, but signal processing time and computational requirements increase
Solution Approach 1:
The patent extracts and removes interfering components from the signal through targeted filtering techniques. By identifying and extracting specific interference patterns, the algorithm cleans the signal efficiently without requiring extensive processing, thus reducing overall computation time while maintaining accuracy.
Solution Approach 2:
Signal filtering and interference suppression are performed as preliminary actions before the main phasor estimation process. This preliminary processing removes most interference early in the pipeline, reducing the computational burden on subsequent estimation algorithms and accelerating overall processing.
Data Source
AI summary
A high-accuracy synchrophasor estimation algorithm for PMU calibration is disclosed. This method can construct a dynamic phasor fitting model. Then, an iterative solution algorithm based on nonlinear fitting can be used to estimate the phasor and frequency, which can include one parameter during the iterations. Moreover, a method is disclosed for calculating the ROCOF based on the least-squares method to improve the ROCOF dynamic accuracy.