Power system frequency calculation method of windowing spectral line interpolation

Abstract

The invention provides a power system frequency calculation method of windowing spectral line interpolation. The method includes the following steps that 1, voltage sampling data of ten continuous sampling cycles of a public connection point are obtained through a voltage measurement circuit; 2, time domain windowing function processing is conducted on the voltage sampling data; 3, local discrete Fourier transformation is conducted on the data obtained after windowing, and voltage amplitude values of specific harmonic wave times are obtained; 4, for the calculated specific voltage amplitude values, interpolation is conducted between the two adjacent maximal amplitude values; 5, calculation is conducted through a polynomial curve fitting method, a frequency coefficient is obtained, and thus fundamental wave frequency of a power grid is determined. By means of the method, a blackman window function is applied to N sampling points, local discrete Fourier transformation (DFT) is only conducted on designated harmonic frequency , it is proved through experiments that by means of the method, the spectrum leakage inhibition effect is good, the calculation speed is high, the calculation process is simple, precision is high, universality is high, and the method can be used for high-precision electric energy metering and electric energy quality analysis.

Description

technical field [0001] The invention relates to the field of power quality analysis and control, in particular to a power system frequency calculation method with window spectral line interpolation. Background technique [0002] Frequency is a necessary quality inspection index for electric energy production and consumption. It is also an important parameter to measure the operation status of the power system. It is one of the most important characteristic quantities of the power system. Therefore, frequency measurement is a very important link in the power system measurement. Failure to meet the requirements of frequency measurement accuracy will cause the measurement or calculation of frequency-related electrical parameters to seriously deviate from the actual value. [0003] The significance of grid frequency measurement technology research is firstly that grid frequency is an important indicator in the power quality system, and secondly, inaccurate grid frequency measure...

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Problems solved by technology

[0002] Frequency is a necessary quality inspection index for electric energy production and consumption. It is also an important parameter to measure the operation status of the power system. It is one of the most important characteristic quantities of the power system. Therefore, frequency measurement is a very important link in the power system measurement. Failure to meet the requirements of frequency measurement accuracy will cause the measurement or calculation of frequency-related electrical parameters to seriously deviate from the actual value

[0003] The significance of grid frequency measurement technology research is firstly that grid frequency is an important indicator in the power quality system, and secondly, inaccurate grid frequency measurement will lead to large errors in the measurement or calculation of frequency-related electrical parameters; in addition, in the power system, some high-tech The application of technology requires high-precision frequency measurement as a guarantee, and it is also the basis for subsequent harmonic analysis; frequency measurement is mainly based on hardware frequency measurement technology and software frequency measurement technology; commonly used software frequency measurement algorithms include period method (or zero crossing method) ), analytical method, error minimization principle algorithm, Fourier algorithm, quadrature demodulation method, etc.; periodic method is widely used in real-time measuring instruments; practice has proved that although periodic method has the advantages of simple principle and easy implementation, but Its anti-interference ability to noise, harmonics and interharmonics is weak;

[0004] Fast Fourier Transform (FFT) is easy to implement in embedded digital signal systems. It is a common method for frequency software measurement and the main method for harmonic analysis. However, it is difficult to achieve synchronous sampling and full-cycle truncation, and there is spectrum leakage. and the fence effect phenomenon, which affect the frequency measurement accuracy; based on the above reasons, in the existing invention [CN101852826A], the fourth-order interpolation based on the Blackman-Harris window is used to approximate the fundamental frequency three times; although the accuracy meets the requirements, the algorithm's The calculation complexity is greatly increased, which is not allowed for power system detection devices with high real-time requirements, and the accuracy cannot be adjusted according to the actual situation; at the same time, the higher the order of the interpolation function, the calculation accuracy may not necessarily be reduced. The higher the higher, the power system frequency obtained by interpolation using the higher-order Blackman-Harris window is compared with the real frequency, and sometimes the error is not small; while in the existing invention [CN104849545A], the current signal is first sampled to obtain n time and n The sampling matrix at time -1 finally determines the phase and its system frequency by finding the characteristic root of the voltage-current matrix; Larger, and this method needs to obtain the generalized inverse matrix of the current matrix, which obviously increases the amount of calculation and lengthens the running time

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