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Context Aware Voltage Sag Waveform Decomposition Using Two-phase CEEMDAN-VMD Model For Power Quality Detection
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  • yuwei zhang ,
  • Minghao Wang ,
  • shuo yang ,
  • zhuofu deng ,
  • zhiliang zhu
yuwei zhang
Northeastern university

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Minghao Wang
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shuo yang
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zhuofu deng
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zhiliang zhu
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The power quality of distribution network side directly affects the charging efficiency and safety of electric vehicles, so excellent power quality detection approach will contribute to improve the charging quality of electric vehicles. However, present power quality decomposition technologies are mainly suitable for single disturbance signal detection, but not for composite power quality signals under different contextual conditions. To resolve these problems and improve the accuracy and reliability of waveform decomposition, a context-based two-phase decomposition method is proposed. At first, we develop a hybrid-model decomposition framework for analyzing composite disturbance signals to improve the decomposition performance of voltage sag waveform. Then, we design a context-awere mechanism to adapt to multiple types of composite disturbance signals in different regions, thereby make the model has better adaptability. Furthermore, We are the first time to propose a voltage sag waveform decomposition method for studying the impact of power quality in low-voltage distribution networks on electric vehicle charging. The experimental results demonstrate that compared with other methods, the proposed two-phase model is more applicable in decomposing composite power quality disturbance signal including voltage sag events, as well as attain significant decomposition results.