No abstract

As microgrids develop rapidly, more inverters are adopted to achieve DC/AC or AC/DC/AC conversion of distributed generators (DGs). The virtual synchronous generator (VSG) control has started to replace the traditional droop control for inverters. In order to restore the frequency to its nominal value, most existing secondary frequency control (SFC) methods are based on frequency measurements. However, while reducing the rate of change of frequency (ROCOF), virtual inertia also slows down the convergence of frequency-based SFC. Therefore, this paper proposes a new distributed hierarchical control for fast frequency restoration. Based on the real-time VSG control at the bottom level, a novel frequency restoration control is designed. The power reference values generated by the proposed control can accelerate the frequency restoration with accurate power sharing. Meanwhile, by designing event-triggering conditions, parallel inverter controllers only need to communicate with neighbors at the event-triggered moments. Simulations have been performed in MATLAB/Simulink environment. Furthermore, the proposed control has also been tested on the experiment platform, which contains practical physical circuits and real-time controllers. Both simulation and experiment results verify the effectiveness of the proposed control strategy.INDEX TERMS Hierarchical control, islanded microgrid, parallel voltage source inverters (VSIs), real-time experiment, virtual synchronous generator (VSG).

Multimodal web rumors, which combine images and text, are confusing and can be inflammatory, and therefore can be harmful to national security and social stability. Currently, web rumor detection fully considers text content but ignores image content, including text embedded in images. This paper proposes a multimodal web rumor detection method based on a deep neural network considering images, image-embedded text, and text content. This method uses a VGG-19 network to extract image content features, DenseNet to extract embedded text content, and an LSTM (Long Short-term Memory) network to extract text content features. After concatenation with image features, the mean and variance vectors of the image and text shared representations are obtained through a completely connected layer, and random variables sampled from a Gaussian distribution are used to form a reparameterized multimodal feature as the input of the rumor detector. Experiments show that the accuracy of this method is 68.5% and 79.4% on Twitter and Weibo, respectively.

This paper investigates the consensus tracking problem for second-order multi-agent systems without/with input delays. Randomized quantization scheme is considered in the communication channels, and impulsive consensus tracking algorithms using position-only information are proposed for the consensus tracking of multi-agent systems. Based on the algebraic graph theory and stability theory of impulsive systems, sufficient and necessary conditions for consensus tracking are studied. It is found that consensus tracking for second-order multi-agent systems without/with input delays can be achieved by appropriately choosing the sampling period and control gains which are determined by second/third degree polynomials. Simulations are performed to validate the theoretical results.