Intelligent fault detection strategy for knowledge entities in fault semantic networks of distribution network based on siamese networks.

The advent of smart grid technologies has brought about a paradigm shift in the management and operation of distribution networks, allowing for intricate system information to be encapsulated within semantic network models. These models, while robust, are not immune to faults within their knowledge entities, which can arise from a myriad of issues, potentially leading to verification failures and operational disruptions. Addressing this critical vulnerability, our research delves into the development of a novel fault detection methodology specifically tailored for the knowledge entity variables of semantic networks in distribution networks. In our approach, we first construct a state space equation that models the behavior of knowledge entity variables in the presence of faults. This foundational framework enables us to apply an unknown input observer strategy to effectively detect anomalies within the system. To bolster the fault identification process, we introduce the innovative use of a siamese network, a neural network architecture which is proficient in differentiating between similar datasets. Through simulation scenarios, we demonstrate the efficacy of our proposed fault detection method.

Control of metal-enhanced fluorescence with pH- and thermoresponsive hybrid microgels.

In this paper, we report on the Ag nanoparticle-containing hybrid poly(N-isopropylacrylamide-co-acrylic acid) (PNIPAM-co-PAA) microgels with pH- and thermoresponsive metal-enhanced fluorescence (MEF). The hybrid microgels were prepared by in situ reducing Ag salts to Ag nanoparticles in the PNIPAM-co-PAA microgels. According to the interaction distance-dependent nature of MEF effects, we can realize a controllable MEF effect by adjusting the average interaction distance between fluorophores and Ag nanoparticles due to the good stimuli-responsive swelling-shrinking behavior of the hybrid microgels. The results show that MEF effect can be well tuned in the pH region 2-12 as well as the temperature region of 20-50 °C. By this method, an enhanced fluorescence detection can possibly be manipulated by adjusting external stimuli such as pH and temperature.