Power system interharmonic wave detection method based on MUSIC spectrum estimation and HBF neural network

Abstract

The invention discloses a power system interharmonic wave detection method based on MUSIC spectrum estimation and a HBF neural network, comprising the steps of getting sampling data of detected signals by an analog-to-digital converter, getting various harmonic waves and interharmonic wave frequency contained in the signals through MUSIC spectrum estimation, and getting amplitudes and phases of the harmonic waves and the interharmonic waves through the adaptive adjustment of weights of the HBF neural network. The invention first provides the power system interharmonic wave measurement method based on the MUSIC spectrum estimation and the HBF neural network. The invention has the advantages of high frequency resolution without causing frequency spectrum leakage, high measurement precision of frequency, amplitudes and phases of the harmonic waves and the interharmonic waves, and high learning speed of the weights of the neural network, and is adaptive to on-line monitoring and off-line testing of interharmonic waves in the power system.

Description

technical field [0001] The invention relates to the field of detection of inter-harmonics in power systems, in particular to a method for detecting inter-harmonics in power systems based on MUSIC spectrum estimation and HBF neural network. Background technique [0002] Interharmonics in power systems are voltage or current components with non-integer multiples of the fundamental frequency, and interharmonics exist widely in power systems. Power electronic devices and periodically fluctuating nonlinear loads, such as inverters, synchronous cascade speed control devices, electric arc furnaces, electric welding machines, induction motors, etc., will generate interharmonics. Interharmonics are very harmful to power systems and equipment, causing problems such as flickering lights and displays, overloading of passive filters, saturation of current transformers, abnormal operation of low-frequency relays, and communication interference. Therefore, it is very important to accurate...

AI Technical Summary

Problems solved by technology

When the support vector machine method is used to measure interharmonics, the operation time is long and the measurement accuracy is low

[0004] The existing literature shows that Cai Zhongfa and others published "Harmonic Analysis Model and Algorithm Based on Adaptive Neural Network" in "Journal of Electrotechnical Society" Vol. 23 No. 7 in 2008. function (HBF) neural network model, and apply the HBF neural network model to harmonic analysis. The HBF neural network model estimates the parameters of each harmonic through the adaptive measurement principle, with high analysis accuracy and fast convergence speed, but when the harmonic When the frequency is unknown, the HBF neural network model cannot be used, so the HBF neural network cannot directly measure the interharmonics of the power system

[0005] The disadvantage of the existing technology is that the frequency resolution of the interharmonic detection is low, the data length of more cycles is required, and the analysis accuracy is low in practical applications.

The frequency of the interharmonic is not an integer multiple of the fundamental wave, so it is difficult to achieve ideal synchronous sampling, and the spectral leakage effect will occur during Fourier transform

[0006] The disadvantage of the existing technology is that the Adaline neuron and the HBF neural network cannot directly measure the interharmonics of the power system because they cannot detect the frequency of the interharmonics of the power system

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