Waveform Period Estimation via Autocorrelation for Power Systems
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Solution Overview
Problem
Existing methods for determining the frequency of waveforms in electric power systems face challenges due to fluctuations and non-periodic conditions, leading to inaccuracies in frequency control and protection, especially in systems with harmonics, time-varying frequencies, and phase jumps.
Innovation Solution
The systems and methods utilize autocorrelation and correlation of waveforms with their derivatives to determine the period, employing equations such as A(t,T) and B(t,T) to find the time shift that maximizes or zeros the integral product, providing a quality indicator for periodicity, and using interpolation for accurate period estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional frequency measurement methods are used, then the system is simple to implement, but measurement precision deteriorates under distorted and time-varying waveform conditions
Solution Approach 1:
The patent implements a dynamic frequency measurement system that adapts to time-varying waveforms by continuously updating the period estimation using autocorrelation. The system adjusts to changing waveform characteristics rather than assuming a fixed frequency, enabling accurate measurement under dynamic conditions while maintaining computational feasibility through iterative refinement of the period estimate.
Solution Approach 2:
The patent changes the measurement parameter from direct frequency detection to period estimation through autocorrelation. By measuring the time shift that maximizes the correlation function and then deriving frequency from this period measurement, the system achieves higher precision under distorted conditions. This parameter transformation allows the system to handle harmonics and phase jumps more effectively.
2Measurement precision
If autocorrelation method is used to determine waveform period, then measurement precision improves under distorted waveforms, but computational complexity increases
Solution Approach 1:
The patent applies partial action by using a finite data window of recent samples for autocorrelation calculation rather than processing the entire signal history. This selective use of a subset of data points provides sufficient accuracy for frequency measurement while significantly reducing computational complexity compared to analyzing the complete signal record.
Solution Approach 2:
The patent uses the waveform signal as its own reference copy in the autocorrelation process. By correlating the current waveform with a time-shifted version of itself, the system eliminates the need for external reference signals or complex comparison mechanisms, thereby reducing overall system complexity while maintaining high measurement precision.
3Adaptability or versatility
If frequency measurement adapts to time-varying waveforms, then adaptability improves, but reliability deteriorates due to increased sensitivity to noise and distortions
Solution Approach 1:
The patent implements feedback by using the estimated period from autocorrelation to guide subsequent measurements and adjustments. The system continuously refines its frequency estimation based on the correlation results, creating a self-correcting mechanism that improves reliability. The feedback loop allows the system to adapt to time-varying waveforms while filtering out noise through iterative refinement.
Solution Approach 2:
The patent performs preliminary autocorrelation analysis on a data window before finalizing the frequency measurement. This preliminary action allows the system to identify the dominant period characteristic before committing to a frequency value, thereby improving reliability by ensuring that the measurement is based on a stable and representative sample of the waveform behavior.
Data Source
AI summary
Disclosed herein are systems and methods for estimating a period and frequency of a waveform. In one embodiment, a system may comprise an input configured to receive a representation of the input waveform. A period determination subsystem may perform an iterative process to determine the variable period of the input waveform. The iterative process may comprise selection of an estimated window length, determination of an autocorrelation value based on the estimated window length, determination of an adjustment value to the window length to identify a maximum of the autocorrelation value; and determination of the variable period based on the window length associated with the maximum of the autocorrelation value. The period determination subsystem may perform the iterative process to track changes in the variable period of the input waveform. A control action subsystem may implement a control action based on the variable period of the input waveform.