Electric energy meter metering method based on S transformation under non-ideal condition

Abstract

The invention discloses an electric energy meter metering method based on S transformation under a non-ideal condition. The method comprises the following steps: establishing a data model under a non-ideal environment; forming a two-dimensional complex matrix R of signal data according to an S transformation result by using a time-frequency transformation analysis means of S transformation, wherein each row of data represents amplitude and phase information of each frequency component after different sampling time points are subjected to S transformation; and extracting a fundamental frequency component under S transformation and a harmonic component under S transformation according to the two-dimensional complex matrix R. According to the invention, comprehensive analysis and evaluation can be effectively carried out on the metering data of the electric energy meter in non-ideal environments such as harmonic waves, sag and abrupt change, the phenomenon of frequency aliasing existing in technologies such as wavelet transformation is solved, fundamental waves and harmonic components are accurately separated and metered, and therefore high-precision metering of electric energy in the non-ideal environments such as the harmonic waves is achieved.

Description

technical field [0001] The invention relates to a measurement method for electric energy meters based on S-transformation under non-ideal conditions, and belongs to the technical field of measurement. Background technique [0002] The accuracy of measurement results of electric energy meters in modern smart grids is very important to realize the economical and efficient operation of smart grids. At the same time, as the basis for power generation companies, power transmission and distribution companies, and power users to calculate electricity costs among each other, the accuracy of smart energy meters is directly related to the economic interests of the three. The electricity measurement result data is related to the reliability of the energy meter operation, so it is necessary to comprehensively analyze and evaluate the energy meter measurement data. During the development of comprehensive evaluation research on electric energy meters, countries and international organiza...

AI Technical Summary

Problems solved by technology

However, when the wavelet transform method deals with distorted signal data, there will be frequency aliasing phenomenon, which will increase the measurement error, and the application of intelligent algorithms such as neural networks will have problems such as complex implementation.

The S transform separates the different frequency components in the signal data without multiple divisions like the wavelet transform, which reduces the complexity of the implementation and can eliminate the frequency aliasing phenomenon, but it is difficult to obtain a good time by the conventional S transform. frequency resolution

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