Sinusoidal Signal Distortion Monitoring via Circular Trajectory Approach
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Solution Overview
Problem
Current power system monitoring methods, relying on phasor measurement unit (PMU) data with low sampling rates, struggle to detect finer scale anomalies and transients, such as harmonics and fluctuations, due to limited computational capabilities and the need for numerous trigger conditions, which are not sufficient for complex and uncertain power systems.
Innovation Solution
The Circular Trajectory Approach (CTA) differentiates sinusoidal signals to calculate a distance index, allowing for real-time monitoring and visualization of distortions without time windows, reducing computational burden, and offering a more general solution for anomaly detection and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If PMU data with low sampling rate is used, then device complexity and computational burden are reduced, but measurement precision and ability to detect finer scale anomalies deteriorate
Solution Approach 1:
The patent transforms the signal from the time domain to the frequency domain by computing the fundamental frequency and its harmonics. This parameter transformation allows the system to detect distortions and anomalies through frequency spectrum analysis rather than requiring high sampling rates in the time domain, thus reducing computational burden while maintaining detection precision.
Solution Approach 2:
The patent replaces complex time-domain signal processing with frequency-domain analysis. Instead of processing high-rate sampled waveforms directly, the system uses Fourier series to convert the signal into frequency components, simplifying the processing mechanism while enhancing the ability to detect subtle anomalies and distortions.
2Reliability
If numerous trigger conditions are added to detect all anomalies, then detection coverage is improved, but device complexity and computational burden increase
Solution Approach 1:
The patent creates a universal anomaly detection mechanism based on frequency spectrum analysis that can detect multiple types of anomalies (amplitude variations, harmonics, transients, and other distortions) through a single unified approach. By analyzing the frequency components of the signal, the system can identify different anomaly types without requiring separate trigger conditions for each, thus reducing complexity while maintaining comprehensive detection coverage.
3Device complexity
If time windows are used for waveform analysis, then computational load is reduced, but detection timing and information accuracy deteriorate
Solution Approach 1:
The patent employs continuous frequency spectrum analysis without dividing the signal into discrete time windows. The system continuously computes the fundamental frequency and harmonic components as the signal passes through, enabling real-time anomaly detection without the delays inherent in time-windowed approaches. This continuous processing maintains information accuracy while managing computational load through efficient frequency-domain algorithms.
Data Source
AI summary
Systems and methods herein provide for sinusoidal signal distortion monitoring and visualization via a Circular Trajectory Approach (CTA). In one embodiment, a system includes a differentiator operable to differentiate an input signal from a sinusoidal signal at substantially a same fundamental frequency of the input signal. The input signal comprising a sinusoidal waveform having distortions. The system also includes a processor operable to calculate a distance index from the input signal to a derivative of the input signal to reveal distortions in the input signal, and a display operable to display the distortions in the input signal.