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

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  • Dan Su

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Electric Power Security and High Efficiency Key Laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Kaicheng Li

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Electric Power Security and High Efficiency Key Laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Nian Shi

    (Power China Hubei Electric Engineering Co. Ltd., Wuhan 430040, China)

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.

Suggested Citation

  • Dan Su & Kaicheng Li & Nian Shi, 2021. "Power Quality Disturbances Recognition Using Modified S-Transform Based on Optimally Concentrated Window with Integration of Renewable Energy," Sustainability, MDPI, vol. 13(17), pages 1-14, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9868-:d:627825
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    Citations

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    Cited by:

    1. Mingang Tan & Chaohai Zhang & Bin Chen, 2022. "Size Estimation of Bulk Capacitor Removal Using Limited Power Quality Monitors in the Distribution Network," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
    2. Surendra Singh & Avdhesh Sharma & Akhil Ranjan Garg & Om Prakash Mahela & Baseem Khan & Ilyes Boulkaibet & Bilel Neji & Ahmed Ali & Julien Brito Ballester, 2023. "Power Quality Detection and Categorization Algorithm Actuated by Multiple Signal Processing Techniques and Rule-Based Decision Tree," Sustainability, MDPI, vol. 15(5), pages 1-30, February.

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