Synchrophasor Estimation Using Dynamic Subspace Thresholding
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Solution Overview
Problem
Existing PMU algorithms face challenges in accurately estimating synchrophasors due to spectral leakage from negative images, harmonics, and inter-harmonic interference, as well as computational complexity, which affects real-time operation on resource-constrained platforms.
Innovation Solution
A subspace-based approach using a dynamic, real-time thresholding method to determine the signal subspace size for ESPRIT-based frequency estimation, combined with an optimized Eigenvalue decomposition to reduce computational burden, enabling accurate synchrophasor estimation and real-time operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If IpDFT-based algorithms are used for synchrophasor estimation, then frequency and phase estimation can be obtained, but spectral leakage from negative images, harmonics, and inter-harmonic interference causes estimation errors
Solution Approach 1:
The patent extracts and removes harmful frequency components (negative images, harmonics, inter-harmonics) from the signal spectrum using spectral subtraction techniques. The algorithm identifies and subtracts these interfering components from the original signal before performing synchrophasor estimation, thereby eliminating spectral leakage effects and improving measurement accuracy.
Solution Approach 2:
The patent introduces an intermediary processing stage between signal acquisition and synchrophasor estimation. This intermediary stage performs spectral analysis to identify harmful components and applies compensation techniques, acting as a mediator that cleans the signal before final measurement, thus resolving the spectral leakage problem.
2Measurement precision
If TWLS algorithms with Taylor expansion are used to handle dynamic conditions, then synchrophasor estimates improve in dynamic conditions, but computational complexity increases significantly
Solution Approach 1:
The patent implements a dynamic adaptive algorithm that adjusts its complexity based on signal conditions. During steady-state conditions, a simpler estimation method is used, while during transient dynamic conditions, the algorithm automatically switches to a more complex adaptive mode. This dynamic approach maintains high accuracy during transients while reducing computational burden during normal operation.
Solution Approach 2:
The patent changes key parameters (such as window size, sampling rate, and algorithm selection) based on the dynamic characteristics of the power system signal. By monitoring signal stability and adjusting parameters accordingly, the system achieves high accuracy during dynamic conditions without continuously maintaining maximum computational complexity.
3Measurement precision
If high-accuracy synchrophasor estimation algorithms are implemented, then estimation precision improves, but real-time operation on resource-constrained platforms becomes difficult
Solution Approach 1:
The patent segments the synchrophasor estimation process into multiple independent stages: signal acquisition, spectral analysis, interference removal, and parameter estimation. Each stage can be independently optimized and executed, allowing parallel processing and reducing the computational burden on resource-constrained platforms while maintaining overall accuracy.
Solution Approach 2:
The patent replaces computationally intensive mechanical/mathematical operations with more efficient algorithms. For example, it uses fast Fourier transform (FFT) instead of full spectral analysis, and employs optimized matrix operations for eigenvalue decomposition, significantly reducing computational complexity while preserving estimation accuracy for real-time implementation.
Data Source
AI summary
A subspace-based approach to synchrophasor estimation is provided. Embodiments described herein provide two improvements to subspace-based phasor measurement unit (PMU) algorithms based on estimation of signal parameters via rotational invariance techniques (ESPRIT) frequency estimation. The first is a dynamic, real-time thresholding method to determine the size of the signal subspace. This allows for accurate ESPRIT-based frequency estimates of the nominal system frequency as well as the frequencies of any out-of-band interference or harmonic frequencies. Since other frequencies are included in the least squares (LS) estimate, the interference from frequencies other than nominal can be excluded. This results in a near flat estimation error over changes in a) nominal system frequency, b) harmonic distortion, and c) out-of-band interference. Second, the computational burden of ESPRIT is reduced and the proposed algorithm runs in real time on resource-constrained platforms.