Renewable energy's grid-integration will strongly influence 3-sectional overcurrent protection's correct operation in distribution network because of its in feed current effect or shunt effect. Firstly, the fault current characteristics of different types of renewable energy sources are simulated, and their impacts on overcurrent protection are analyzed. Secondly, the detailed influence factors, including the renewable energy capacity, the integration location, the fault location, the reliability coefficient and the transmission line length, are thoroughly discussed in the distribution system model. Especially, by changing the renewable energy source capacity, the relationship between the short-circuit capacity ratio at the integration point and the fault current flowing through the protection is investigated. Lastly, simulation results on PSCAD/EMTDC show that when the short-circuit capacity ratio at the integration point is superior to 11.72%, the instantaneous overcurrent protection (1st section) will mal-operate, and when the ratio surpasses 21.8%, the time-delay instantaneous overcurrent protection (2nd section) will mis-operate.
Hybrid simulation combines the physical testing and computer modeling to analyze the dynamic responses of structures to external impacts (Hakuno et al., 1969). This relatively novel technique has been widely used in earthquake engineering. In the present study, it is extended to analyze the responses of structures to coastal loadings. This paper concerns mainly on the hydrodynamic loading induced by storm surge and tsunami events.
In this paper, we proposed a quality of transmission (QoT) prediction technique for the quality of service (QoS) link setup based on machine learning classifiers, with synthetic data generated using the transmission equations instead of the Gaussian noise (GN) model. The proposed technique uses some link and signal characteristics as input features. The bit error rate (BER) of the signals was compared with the forward error correction threshold BER, and the comparison results were employed as labels. The transmission equations approach is a better alternative to the GN model (or other similar margin-based models) in the absence of real data (i.e., at the deployment stage of a network) or the case that real data are scarce (i.e., for enriching the dataset/reducing probing lightpaths); furthermore, the three classifiers trained using the data of the transmission equations are more reliable and practical than those trained using the data of the GN model. Meanwhile, we noted that the priority of the three classifiers should be support vector machine (SVM) > K nearest neighbor (KNN) > logistic regression (LR) as shown in the results obtained by the transmission equations, instead of SVM > LR > KNN as in the results of the GN model.Keywords optical networks, quality of transmission (QoT), quality of service (QoS), link establishment, physical performances, bit error rate (BER), machine learning
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.