Abstract
To meet power quality requirements, it is necessary to classify and identify the power quality of the power grid connected with renewable energy generation. S-transform (ST) is an effective method to analyze power quality in time and frequency domains. ST is widely used to detect and classify various kinds of non-stationary power quality disturbances. However, the long taper and scaling criteria of the Gaussian window in standard ST (SST) will lead to poor time domain resolution at low frequency and poor frequency resolution at high frequency. To solve the discrete side effects, it is necessary to select the optimal window function to locate the time frequency accurately. This paper proposes a modified ST (MST) method. In this method, an improved window function of energy concentration in time-frequency distribution is introduced to optimize the shape of each window function. This method determines the parameters of Gaussian window to maximize the product of energy concentration in a time-frequency domain within a given time and frequency interval, so as to improve the energy concentration. The result shows that compared with the SST with Gaussian window, ST based on the optimally concentrated window proposed in this paper has better energy concentration in time-frequency distribution.
Highlights
In order to cope with the increasingly severe energy shortages and the challenge of energy conservation and emissions reduction, the penetration rate of renewable energy generation such as photovoltaic power generation and wind power generation is increasing in power systems [1]
Taking a photovoltaic inverter as an example, the harmonic current mainly consists of two parts [3]: (1) low-order harmonics caused by dead time, such as 3, 5, 7 odd harmonic currents; (2) high-order harmonics caused by PWM modulation process
Highfrequency harmonics can be filtered by L-type or LCL type output filters [4]; for low order harmonics, parallel tuned filters are used for harmonic suppression and reactive power compensation [5]
Summary
In order to cope with the increasingly severe energy shortages and the challenge of energy conservation and emissions reduction, the penetration rate of renewable energy generation such as photovoltaic power generation and wind power generation is increasing in power systems [1]. In order to improve the time-frequency resolution and the energy concentration of timefrequency distribution, many researchers try to modify the Gaussian window function structure by introducing adjustable parameters to control the window width [31] and to optimize these parameters. A method based on the MST and parallel stacked sparse auto-encoder to PQ disturbances recognition is proposed in reference [33], and a Kaiser window is used in MST for a better energy concentration in time-frequency matrix. A modified ST method for obtaining optimal energy concentration by time-frequency domain analysis is proposed based on standard S-transform.
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