Empirical mode decomposition-based voltage anomaly characteristic identification method

Abstract

The invention belongs to the electric power system electric energy quality monitoring and analysis field and relates to an empirical mode decomposition-based voltage anomaly characteristic identification method. The method includes the following steps that: a signal processor sets a sampling period as T for the voltage signals of an electric power system, and performs real-time sampling and quantization on the signals to obtain original signal v (t); empirical mode decomposition is performed on the original signals v (t), so that IMF modal components of various orders can be obtained; Hilbert-Huang transform is performed on the first IMF modal component of the original signal v (t), so that an instantaneous frequency graph and an instantaneous amplitude graph can be obtained; and voltage anomaly signals are detected and identified according to mutational point, amplitude change and instantaneous frequency trend characteristic information in the Hilbert-Huang transform graphs. With the empirical mode decomposition-based voltage anomaly characteristic identification method of the invention adopted, nonlinear and non-stationary signals of the electric power system can be processed, different characteristics of signals can be clearly identified in the Hilbert-Huang transform graphs, and electric energy quality detection analysis problems can be automatically extracted and correctly classified from massive voltage disturbance signals.

Description

technical field [0001] The invention relates to the field of power system power quality monitoring and analysis, in particular to a voltage anomaly feature recognition method based on empirical mode decomposition. Background technique [0002] In the modern power system, the application of power electronic equipment is becoming more and more extensive, which brings various random, nonlinear, impulsive, fluctuating and transient power signals, and the power quality of the power system is becoming more and more serious. pollution. In view of this, the requirements for power supply reliability and power quality analysis at the load end (user end) are also increasing day by day. On the other hand, the existence of abnormal voltage signals will bring serious adverse effects to the power system. For example: the abnormal operation of the servo motor will reduce the service life of the power equipment, increase the power loss, and may lead to power failure in severe cases. [00...

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Problems solved by technology

[0004] Based on the Fourier transform and a series of improved analysis methods, there are certain spectrum leakage phenomena and fence effects, and only stable signals can be analyzed. For transient and sudden disturbance signals, windowing algorithms are often used to correct them. However, the window function The selection of the window function needs to be based on the characteristics of the signal, and the inappropriate selection of the window function may cause signal distortion;

[0005] The analysis method based on wavelet transform has been widely used in power quality disturbance processing, but it is easily affected by signal noise and is not suitable for disturbance signals dominated by time domain changes;

[0006] S-transform is a new time-frequency domain analysis method. In recent years, it has made great progress in the extraction of power signal feature information. However, its algorithm structure is complex and requires high system hardware when analyzing long signals.

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