Electric energy quality disturbance recognition and classification method based on PSO
A technology of power quality disturbance and classification method, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of not finding parameters, searching for optimization, etc., achieving high accuracy, accurate and reliable recognition, and improving training speed and classification. The effect of accuracy
Inactive Publication Date: 2016-03-30
GUANGDONG UNIV OF TECH
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In addition, at the present stage, the SVM classifier used for the identification and classification of power quality disturbances is all given parameters in terms of parameter selection, and the best method has not been found to optimize the parameters
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Embodiment 1
[0028] Such as Figures 1 to 7 As shown, a PSO-based power quality disturbance identification and classification method includes the following steps:
[0029] a. Establish a signal model containing common dynamic disturbance signals. Common disturbance signals include five types of voltage swell signals, voltage sag signals, temporary voltage interruption signals, transient pulse signals and transient oscillation signals. Table 1 shows the five A model of a common dynamic disturbance signal and the corresponding parameter settings.
[0030] Table 1 Table 1 Five common dynamic disturbance signal models
[0031]
[0032]
[0033] The disturbance signal is extracted from the input voltage signal by complex wavelet transform, and the disturbance signal is decomposed by multi-scale complex wavelet by constructing the Db4 orthogonal compact support complex wavelet using the Mallat fast wavelet algorithm, and the structural information of the disturbance signal in each frequen...
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The invention provides an electric energy quality disturbance recognition and classification method based on a PSO, and the method achieves detection and positioning of a disturbance signal through employing complex wavelet transform, and effectively extracts a dynamic electric energy quality disturbance feature vector. After the optimization of an SVM parameter is completed through the PSO algorithm, the method carries out the automatic recognition and classification of the electric energy quality disturbance according to an extracted feature signal. The method can effectively improve the training speed and classification accuracy of the detection and classification of the electric energy quality disturbance. The complex wavelet transform can iron out the defect that the conventional real wavelet transform just can achieve the analysis of the amplitude-frequency of a signal, and can achieve the analysis of the amplitude-frequency and phase-frequency features of the signal, and also can provide a plurality of types of composite information. The method can accurately recognize the most common dynamic disturbance signals in a power system. Compared with a conventional method for recognizing an interference signal through a neural network, the method is accurate and reliable in recognition, and is higher in accuracy.
Description
technical field [0001] The invention relates to the field of power system power quality analysis technology research, and more specifically, to a PSO-based power quality disturbance identification and classification method. Background technique [0002] The problem of power quality (PowerQuality, PQ) has aroused widespread concern of workers in the electric power industry. With the development of the field of industrial control in the direction of nonlinearity, network integration, and large-scale, and the continuous increase of nonlinear load capacity such as large-scale rectification equipment and frequency conversion speed control equipment in the system, the power quality of multi-grid power supply has caused serious pollution and seriously affected the power supply. The quality of electric power supplied by enterprises, and the accurate identification and classification of power quality disturbances are the prerequisites for ensuring the safe and economical operation of...
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Login to View More IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/08G06F2218/12G06F18/2411
Inventor 何君如杨俊华杨济溦
Owner GUANGDONG UNIV OF TECH