Power harmonic signal analysis method and analysis equipment based on Bayes method

Abstract

The invention provides a power harmonic signal analysis method and power harmonic signal analysis equipment based on a Bayes method. Different from the existing harmonic solution method in the frequency domain, a harmonic signal analysis mathematic model is established by using the time domain sparse characteristic of a power system complex harmonic signal and by adopting a Bayes model, so that the amplitude and the frequency of the signal can be acquired through recursive solution on the problem according to the expected maximum algorithm. The power harmonic signal analysis method comprises the following steps: setting a Bayes estimation initial value and a convergence parameter; acquiring a harmonic frequency analysis interval, discretizing the harmonic frequency analysis interval into analysis subintervals with the set number and forming a harmonic measuring matrix; acquiring a sampling signal of a to-be-analyzed signal and establishing a probability density function of the harmonicsignal according to a Bayes formula; performing iterative calculation on the probability density function by the expected maximal algorithm and estimating to acquire the frequency and the amplitude of a harmonic component in the to-be-analyzed signal.

Description

technical field [0001] The invention relates to electric power technology, in particular to a power harmonic signal analysis method and analysis equipment based on a Bayesian method. Background technique [0002] In recent years, with the increase of disturbing loads and the sensitivity of equipment to harmonics, the impact of harmonics on power systems has become more and more serious, and harmonic pollution has become an urgent concern. It is generally believed that the frequency of an integer harmonic is an integer multiple of the fundamental frequency. In addition, there are abundant non-integer harmonics in the power grid, that is, inter-harmonics, whose frequency is not an integer multiple of the fundamental frequency, and whose spectrum may be in discrete or continuous form. Signals containing harmonics / simple harmonics can be collectively referred to as complex harmonics. [0003] Many harmonic analysis methods have been provided in the prior art, but most of these...

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Problems solved by technology

Admittedly, these methods are simple and computationally expensive, and can achieve high-precision analysis of harmonics under synchronous sampling conditions. However, these FFT-based methods are sensitive to frequency offsets, and the sampling conditions are relatively harsh, and there are spectrum leakage and Fence effects and other issues, while FFT-based parameter estimation methods are all limited by the Fourier resolution of the uncertainty principle

In particular, two sine wave signals with a frequency difference of Δω require a data length longer than 2π / Δω, and these defects are more obvious when analyzing interharmonics

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