3-Phase Voltage Signal Phase Angle Detection
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Solution Overview
Problem
Existing methods for determining the phase angle of 3-phase voltage signals in power grids are sensitive to noise and unbalance, leading to inaccurate synchronization and dynamic performance issues, especially when using phase locked loops or fixed weight vector methods.
Innovation Solution
A method that separates the 3-phase voltage signal into positive and negative sequences using Clarke transformation, determines optimum weights based on noise covariance, and uses a weighted least squares method to iteratively update frequency and phase angle estimates, considering both amplitude and phase unbalance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a phase locked loop (PLL) method is used to determine phase angle, then synchronization can be achieved, but performance degrades in the presence of voltage unbalance due to introduction of double frequency component
Solution Approach 1:
The patent applies symmetrical component transformation to separate the 3-phase voltage signal into positive sequence and negative sequence components. This segmentation isolates the useful positive sequence signal from the harmful negative sequence that causes double frequency components in PLL, allowing accurate phase angle detection despite voltage unbalance.
Solution Approach 2:
The patent extracts only the positive sequence component from the 3-phase voltage signal using symmetrical component transformation. By taking out and processing only the relevant positive sequence signal, the method eliminates the interference from negative sequence components that would otherwise degrade PLL performance.
2Device complexity
If a fixed weight vector method is used to estimate frequency and phase angle, then computation is simplified, but the method is not adaptive and performs inaccurately in some situations
Solution Approach 1:
The patent transforms the fixed weight vector into an adaptive weight vector that dynamically adjusts based on the statistical correlation of noise. The weight matrix is updated iteratively using the inverse of the noise covariance matrix, allowing the estimation method to adapt to changing signal conditions and maintain high accuracy.
Solution Approach 2:
The patent changes the weight parameters from fixed heuristic values to adaptive values derived from noise statistical characteristics. By computing weights based on the inverse noise covariance matrix, the method optimizes estimation accuracy for different signal-to-noise conditions.
3Ease of operation
If conventional phase angle detection methods are used, then implementation is straightforward, but the methods are sensitive to noise and voltage signal disturbances
Solution Approach 1:
The patent implements an iterative feedback mechanism where the noise covariance matrix is updated based on previously estimated frequency and phase angle values. This feedback loop allows the method to progressively refine its noise model and improve estimation accuracy, making it robust against noise and disturbances.
Solution Approach 2:
The patent introduces symmetrical component transformation as an intermediary step between signal acquisition and phase angle detection. This transformation mediates by separating positive and negative sequences, allowing subsequent processing to focus only on the useful positive sequence signal and reducing sensitivity to disturbances.
Data Source
AI summary
At least one parameter of a signal is determined, wherein the signal is a sinusoidal signal including noise, wherein the parameter includes at least one of a frequency of the signal, and an angle of a phase of the signal. The frequency of the signal is determined iteratively based on a linear relationship among the frequency of the signal, samples of the noise, and samples of the signal using a statistical correlation among the samples of the noise. During a current iteration the statistical correlation is updated based on the frequency of the signal determined during a previous iteration, and the samples of the signal are updated with values of the signal during a current period of time.