scholarly journals Analysis of Power Quality Signals Using an Adaptive Time-Frequency Distribution

Energies ◽  
2016 ◽  
Vol 9 (11) ◽  
pp. 933 ◽  
Author(s):  
Nabeel Khan ◽  
Faisal Baig ◽  
Syed Nawaz ◽  
Naveed Ur Rehman ◽  
Shree Sharma
Keyword(s):  
Power Quality ◽  
Frequency Distribution ◽  
Time Frequency ◽  
Quality Signals ◽  
Adaptive Time ◽  
Time Frequency Distribution

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

2021 ◽  
Vol 13 (17) ◽  
pp. 9868
Author(s):  
Dan Su ◽  
Kaicheng Li ◽  
Nian Shi
Keyword(s):  
Renewable Energy ◽  
Power Quality ◽  
Frequency Distribution ◽  
Window Function ◽  
Energy Concentration ◽  
Time Frequency ◽  
Gaussian Window ◽  
S Transform ◽  
Power Quality Disturbances ◽  
Time Frequency Distribution

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.

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