Abstract
With the significant improvement of microgrid technology, microgrid has gained large-scale application. However, the existence of intermittent distributed generations, nonlinear loads and various electrical and electronic devices causes power quality problem in microgrid, especially in islanding mode. An accurate and fast disturbance detection method which is the premise of power quality control is necessary. Aiming at the end effect and the mode mixing of original Hilbert-Huang transform (HHT), an improved HHT with adaptive waveform matching extension is proposed in this paper. The innovative waveform matching extension method considers not only the depth of waveform, but also the rise time and fall time. Both simulations and field experiments have verified the correctness and validity of the improved HHT for power quality disturbance detection in microgrid.
Highlights
Microgrid technology has provided a new technical approach for the large-scale integration of renewable energy and distributed generations, as well as technical support for the grid-connected operation of distributed generations to meet the requirements of smart grid
In order to improve the accuracy of Hilbert-Huang transform (HHT) for power quality disturbance detection, this paper introduces an adaptive waveform matching extension method
This paper systematically analyzes the causes of the end effect of HHT and the mode mixing problem
Summary
Microgrid technology has provided a new technical approach for the large-scale integration of renewable energy and distributed generations, as well as technical support for the grid-connected operation of distributed generations to meet the requirements of smart grid. The disturbance detection method for microgrid application needs to analyze harmonic and inter-harmonic signals and nonlinear and non-stationary signals. Hilbert-Huang transform (HHT) is an adaptive time-frequency analysis method which can deal with nonlinear and non-stationary signal analysis as well. This timefrequency analysis method can adaptively decompose signals according to their characteristics, characteristics of power quality disturbance are automatically extracted from the signals themselves. Compared with wavelet transform, HHT has the advantages of wavelet transform, and does not need to select basic functions [5] For these reasons, HHT is a suitable method to carry out disturbance detection in microgrid.
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