Adaptive harmonic analysis system and analysis method

Abstract

The invention discloses an adaptive harmonic analysis system and analysis method. A programmable timer is used as a trigger source for signal sampling, a sampled signal is generated according to the system setting, a sampling circuit is triggered to carry out sampling and AD conversion on an input signal, and after sampling is continuously carried out for 2N times, sampled data is subjected to FFTanalysis. A maximum value in result data is searched and analyzed, a position of a fundamental wave spectral line is determined, and a frequency of a fundamental wave signal is estimated. A proper signal sampling frequency is selected according to the position of the fundamental wave spectral line, and a sampling signal frequency output by a trigger timer is set. When a sampling rate is within aproper range, amplitudes of all spectral lines in an FFT analysis result except for a direct current component, a fundamental wave component and a harmonic component are added to obtain spectrum totalenergy leakage. According to the total energy leakage, the sampling rate is continuously subjected to fine regulation so as to implement spectrum analysis with the minimum energy leakage.

Description

technical field [0001] The invention belongs to the technical field of detection and signal analysis, and in particular relates to an adaptive harmonic analysis system and an analysis method thereof. Background technique [0002] Harmonic analysis of signal is an important part of signal processing, and it is widely used in many fields such as communication and electric power. At present, the basic method of online harmonic analysis of signals is the Fast Fourier Transform (FFT) algorithm. There is a basic limitation in the application of this algorithm, the number of input input data must be 2 N , so under a certain sampling period, the sampled data is corresponding to 2 N The time window for subsampling may not be an integer multiple of the period of the input signal, especially if the period of the input signal changes, and even a well-tuned sampling system will not match the input signal. Using such sampling data to perform FFT will inevitably produce certain spectrum...

AI Technical Summary

Problems solved by technology

This method can fundamentally solve the problem of spectrum leakage, but the PLL structure is more complicated, which increases the system cost

When the input signal frequency changes, the tracking speed of the PLL is low, and the dynamic error of the spectrum analysis is large. At the same time, due to the limitation of the PLL frequency tracking range, when the input signal frequency changes in a large range, the matching of the sampling window and the measured signal cannot be guaranteed.

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