Research Of Uncertainty Theory In Electric Power Measurement And Partial Discharge Signal Processing

Measurement is one of the important means of human understanding and transforming the world, the measurement results of the quality will directly affect the national and enterprise economic benefits. The uncertainty of measurement is an important index for evaluating the quality of measurement results.In this paper, the theory of measurement uncertainty are described, according to the basic measuring electric energy metering can be launched a meter measurement error uncertainty. Using the GUM method, the most commonly used to assess MCM method, Bayesian evaluation method based on MATLAB, the MCM method based on MATLAB is faster, speed rating GUM method has wider application range; and the Bayesian method is GUM method of measurement uncertainty evaluation of smaller, more reasonable.Aiming at the dynamic measurement system of strong, This paper processes the measurement data based on the dynamic measurement theory and evaluates uncertainty of measurement to illustrate the processed data with greater use value. Uncertainty is smaller, the higher the quality of measurement results, the greater the value of the use. Type A evaluation and Bayesian evaluation are done for the raw data and the processed data after dynamic measurement theory. Through comparative study, the conclusion is obtained. Bayesian evaluation is better than type A evaluation. Compared with raw data, the uncertainty evaluation of The processed data after dynamic measurement theory is small, and the reliability is high,which shows the processed data closer to the true value, but also illustrates the dynamic measurement theory feasibility and correctness in the data processing.As a typical application of data processing, this paper applies the theory of dynamic measurement to the processing of partial discharge signals. A partial discharge signal extraction method based on dynamic measurement uncertainty is proposed in this paper. The deterministic component is separated by polynomial fitting, and the random component of the remaining residual after the separation is estimated using autoregressive(AR) model; The true value estimation and dynamic measurement uncertainty of noisy signal are obtained by the deterministic component and random component;According to the 3σ rule, the threshold is selected, and based on dynamic measurement uncertainty, the poor quality of the data is removed to improve the filtering effect. Because of the discontinuity, wavelet transform is used to improve the accuracy of the extraction. Finally, the effectiveness of the method is verified by MATLAB simulation and experimental noisy PD signal extraction.Finally, from the practical point of view, combined with a variety of measurement model, the measurement of different kinds of uncertainty evaluation method using MATLAB GUI interface design method, in order to improve the measurement uncertainty of the speed and accuracy of calculation, for the future engineering applications bring convenience.