The integration of distributed energy resources(DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants,...The integration of distributed energy resources(DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method(IIDM) using an intrinsic mode function(IMF)feature-based grey wolf optimized artificial neural network(GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.展开更多
In ultrasonic non-destructive tests, the echo signal at the flaw is highly complex due to the interference of multiple echoes with random amplitudes and phases, and is disturbed by all kinds of noises, such as thermal...In ultrasonic non-destructive tests, the echo signal at the flaw is highly complex due to the interference of multiple echoes with random amplitudes and phases, and is disturbed by all kinds of noises, such as thermal noise, digitalization noise, and structure noise. In this paper, the ultrasonic signal was decomposed by empirical mode decomposition (EMD) to obtain the in-trinsic mode function (IMF) components according to ultrasonic defect echo signals occuring at the corresponding time, and the energy of the ultrasonic signal was concentrated. The IMF component selection criterion based on sub-band energy extraction was proposed to extract the ultrasonic signal component accurately and automatically from IMF components. When the selected IMF components were filtered by a band pass filter, the signal-to-noise ratio (SNR) was enhanced greatly.展开更多
基金supported by the National Research Foundation (NRF) of South Korea funded by the Ministry of Science, ICT & Future Planning (MSIP) of the Korean government (No.2018R1A2A1A05078680)。
文摘The integration of distributed energy resources(DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method(IIDM) using an intrinsic mode function(IMF)feature-based grey wolf optimized artificial neural network(GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.
基金Project (No. 2005AA602021) supported by the High-Tech Researchand Development Program (863) of China
文摘In ultrasonic non-destructive tests, the echo signal at the flaw is highly complex due to the interference of multiple echoes with random amplitudes and phases, and is disturbed by all kinds of noises, such as thermal noise, digitalization noise, and structure noise. In this paper, the ultrasonic signal was decomposed by empirical mode decomposition (EMD) to obtain the in-trinsic mode function (IMF) components according to ultrasonic defect echo signals occuring at the corresponding time, and the energy of the ultrasonic signal was concentrated. The IMF component selection criterion based on sub-band energy extraction was proposed to extract the ultrasonic signal component accurately and automatically from IMF components. When the selected IMF components were filtered by a band pass filter, the signal-to-noise ratio (SNR) was enhanced greatly.
