Abstract
The Broadband Mode Decomposition (BMD) method was previously proposed to solve the Gibbs phenomenon that occurs during photovoltaic signal decomposition; its main idea is to build a dictionary which contains signal features, and to search in the dictionary to solve the problem. However, BMD has some shortcomings; especially if the relative bandwidth of the decomposed signal is not small enough, it may treat a square wave signal as several narrowband signals, resulting in a deviation in the decomposition effect. In order to solve the problem of relative bandwidth, the original signal is multiplied by a high-frequency, single-frequency signal, and the wideband signal is processed as an approximate wideband signal. This is the modulation broadband mode decomposition algorithm (MBMD) proposed in this article. In order to further identify and classify the disturbances in the photovoltaic direct current (DC) signal, the experiment uses composite multi-scale fuzzy entropy (CMFE) to calculate the components after MBMD decomposition, and then uses the calculated value in combination with the back propagation (BP) neural network algorithm. Simulation and experimental signals verify that the method can effectively extract the characteristics of the square wave component in the DC signal, and can successfully identify various disturbance signals in the photovoltaic DC signal.
Highlights
The photovoltaic direct current (DC) current signal and its disturbance were taken as the research objects, and a series of algorithm experiments on feature extraction and classification was conducted
The modulation broadband mode decomposition algorithm (MBMD) method is proposed to solve the problem of broadband signal feature extraction due to the Gibbs phenomenon in the photovoltaic DC signal disturbance analysis
In past time–frequency methods such as ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), and other defects caused by extreme point interpolation or the Gibbs phenomenon, it is proposed that the MBMD method can be avoided by searching in the dictionary
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The use of an improved Hilbert–Huang transform (HHT) and the energy entropy algorithm [20] to extract the features of alternating current (AC) signal square waves was proposed by K. He et al Second, the essence of the methods that are based on the non-Fourier transform is the use of the interpolation function to calculate the envelope of the extreme point, and the division of the original signal into a number of intrinsic. To denoise photovoltaic DC signals, a broadband mode decomposition (MBMD) method is proposed in this paper, which is based on the modulation differential operator. All the data in this paper come from the data collected by the photovoltaic experimental platform in the literature [25]
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