A method for estimating the instantaneous frequency of complex main signals

Abstract

A Complex Adaptive Phase Discriminator (PD), as presented in some concepts of the present disclosure, is an adaptive filter that accurately estimates the instantaneous frequency of a dynamic complex signal. The PD can demonstrate accurate instantaneous frequency estimation and rapid convergence in dynamic complex signal environments, even when the frequency of its input may change rapidly. A direct PD architecture can estimate the instantaneous frequency of a complex primary signal, iteratively adapting a phase of a complex exponential by minimizing the mean squared error of a complex error signal. Instantaneous frequency can be directly estimated from the phase of the complex exponential. In contrast, an indirect PD architecture can estimate the instantaneous frequency of a complex primary signal, iteratively adapting a complex coefficient by minimizing the mean squared error of a complex error signal. Instantaneous frequency can be indirectly estimated by extracting the phase of the complex coefficient.

Description

technical field [0001] The present disclosure generally relates to estimating the instantaneous frequency of complex dynamic signals, and more particularly to systems, methods, and devices for extracting the exact instantaneous frequency of complex exponential components. Background technique [0002] Robust and accurate estimation of transient smoothing is fundamental to achieving a good measure of success in several types of electric machine analysis. Established techniques often explicitly or implicitly assume static or quasi-static motor operation. In practice, however, the motor and load environment is dynamic and depends on countless variables such as internal and external temperature, changing load dynamics, fundamental frequency and voltage, etc. The cumulative effect of such changes can have significant adverse effects on many types of electric machine analyses. [0003] Reference Models for Adaptive Systems (MRAS) is a method for iterating the electrical model of...

AI Technical Summary

Problems solved by technology

Traditional frequency estimation methods involving Fourier transforms provide total frequency estimates that lack the required temporal resolution and require impractical long-term observations to provide minimally sufficient frequency resolution

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