Power quality disturbance detection method for power distribution network based on improved EWT and CMPE

Abstract

The invention discloses a power quality disturbance detection method for a power distribution network based on improved EWT and CMPE, and the method comprises the steps of decomposing a PQ disturbancesignal of an active power distribution network system by adopting improved empirical wavelet decomposition EWT, filtering noise of the PQ disturbance signal, and decomposing to obtain an EWT component containing characteristic information; using the EWT component containing the characteristic information an input signal of a composite multi-scale permutation entropy CMPE algorithm, carrying out permutation entropy calculation on each EWT component containing the characteristic information by utilizing the composite multi-scale permutation entropy CMPE algorithm, and calculating an entropy value matrix of each PQ disturbance signal under each mode function; using a PCA algorithm to perform dimensionality reduction on the calculated entropy value matrix, calculating principal component components, and obtaining characteristic ranges of various PQ disturbance signals; according to the obtained eigenvalue matrix after the dimensionality reduction processing, using the eigenvalue matrix asthe input quantity of a SVM algorithm; and identifying the PQ disturbance signal of the active power distribution network system containing the distributed energy. The power quality disturbance detection method for the power distribution network based on improved EWT and CMPE is simple in steps and accurate in classification, and can improve the reliability of the power distribution network.

Description

technical field [0001] The invention relates to the technical field of power quality disturbance signal detection, in particular to a method for detecting power quality disturbances in distribution networks based on improved EWT and CMPE. Background technique [0002] Renewable energy has a great impact on power quality due to its uncertainty and its penetration rate is increasing year by year. In addition to the traditional power disturbance in the high-penetration active distribution network, due to the constraints of natural conditions such as environmental climate, DG output is random. , volatility and intermittency may lead to oscillation or flicker. DG output direct current such as photovoltaic array fuel cell needs to be connected to the system through power electronic devices as an interface, which will cause a lot of harmonic pollution, and due to the low inertia of DG , the system is more susceptible to various disturbances and becomes unstable; therefore, it is mo...

AI Technical Summary

Problems solved by technology

[0002] Renewable energy has a great impact on power quality due to its uncertainty and its penetration rate is increasing year by year. In addition to the traditional power disturbance in the high-penetration active distribution network, due to the constraints of natural conditions such as environmental climate, DG output is random. , volatility and intermittency may lead to oscillation or flicker. DG output direct current such as photovoltaic array fuel cell needs to be connected to the system through power electronic devices as an interface, which will cause a lot of harmonic pollution, and due to the low inertia of DG , the system is more susceptible to various disturbances and becomes unstable; therefore, it is more important to detect and classify sub-compound disturbances in high-penetration active distribution networks

[0003] There are still some problems in the existing power quality disturbance detection technology of distributed power distribution network, for example: Hilbert-Huang transform is a combination of empirical mode decomposition and Hilbert spectrum analysis, which puts the signal in the time-frequency domain However, in addition to not having a reliable theoretical basis, it also has problems such as slow calculation speed and over-envelopment; local mean decomposition is suitable for feature extraction of non-stationary power signals in power systems, and decreases according to frequency during the iterative screening process The law decomposes the signal into a series of modal functions with physical meaning, but its disadvantages of "end point effect" and "mode aliasing" greatly affect the accuracy of the results; although the variational modal decomposition decomposes the signal from the recursive screening mode Converting to a non-recursive screening variational mode solves the above-mentioned local mean decomposition problem, but at the cost of higher computational complexity and still cannot be adaptively decomposed

Images