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Article

Power Quality Disturbances Recognition Using Modified S-Transform Based on Optimally Concentrated Window with Integration of Renewable Energy

by 1,2, 1,2,* and 3
1
State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2
Hubei Electric Power Security and High Efficiency Key Laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
3
Power China Hubei Electric Engineering Co. Ltd., Wuhan 430040, China
*
Author to whom correspondence should be addressed.
Academic Editor: Thanikanti Sudhakar Babu
Sustainability 2021, 13(17), 9868; https://0-doi-org.brum.beds.ac.uk/10.3390/su13179868
Received: 19 July 2021 / Revised: 29 August 2021 / Accepted: 30 August 2021 / Published: 2 September 2021
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. View Full-Text
Keywords: S-transform; energy concentration; power quality; renewable energy generation S-transform; energy concentration; power quality; renewable energy generation
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MDPI and ACS Style

Su, D.; Li, K.; Shi, N. Power Quality Disturbances Recognition Using Modified S-Transform Based on Optimally Concentrated Window with Integration of Renewable Energy. Sustainability 2021, 13, 9868. https://0-doi-org.brum.beds.ac.uk/10.3390/su13179868

AMA Style

Su D, Li K, Shi N. Power Quality Disturbances Recognition Using Modified S-Transform Based on Optimally Concentrated Window with Integration of Renewable Energy. Sustainability. 2021; 13(17):9868. https://0-doi-org.brum.beds.ac.uk/10.3390/su13179868

Chicago/Turabian Style

Su, Dan, Kaicheng Li, and Nian Shi. 2021. "Power Quality Disturbances Recognition Using Modified S-Transform Based on Optimally Concentrated Window with Integration of Renewable Energy" Sustainability 13, no. 17: 9868. https://0-doi-org.brum.beds.ac.uk/10.3390/su13179868

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