Method for classifying electric energy quality mixing disturbances based on multi-feature quantity of time-frequency domain
A power quality disturbance, multi-feature technology, applied in instruments, character and pattern recognition, computer parts and other directions, can solve problems such as mutual influence and complex signal characteristics
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[0090] figure 1 It is the overall algorithm flow chart of the present invention.
[0091] A. Generation of original data of power quality mixed disturbance
[0092] Since the actual sampling signal cannot fully reflect the diversity of disturbance signals, MATLAB software is used to randomly generate normal signals, sags, swells, short-time interruptions, pulse transients, oscillation transients, harmonics and flicker. Single disturbances and 40 mixed disturbances.
[0093] Each type randomly generates 50 samples, the signal fundamental frequency is 50Hz, and the signal sampling frequency is 3.2kHz. All signals are superimposed with Gaussian white noise with a signal-to-noise ratio of 40dB.
[0094] B. Feature quantity construction and extraction
[0095] Time-Frequency Domain Analysis of Power Quality Mixed Disturbance Signals: Using EEMD and MIST ( figure 2 ) after processing the signal, 9 time-frequency domain feature quantities suitable for mixed disturbance classifi...
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