Novel dynamic control of power systems with penetration of renewable energy sources in multi-generator framework

In this paper, the power system comprises of conventional synchronous generators (SGs) and renewable energy sources is represented as a multi-generator interconnected system. The renewable energy sources, here called renewable generators (RGs,) are connected to grid-tie inverters (GTI.) A novel controller is proposed for the GTI that makes the device behave as a conventional synchronous generator through dynamically varying gain and phase angle. The proposed GTI controller makes possible utilization of damping controllers, such as automatic voltage regulator (AVR) with power system stabilizer (PSS) and other advanced controllers, and thus improves transient stability. In addition, control mechanisms that assure constant input renewable power to the GTI during transients are introduced, which further improve the transient stability of the network. The end result is a feedback controller that makes possible the application of available multi-generator stabilizing techniques to the power systems with penetration of renewable energy sources. The effectiveness of the approach in damping oscillations that occur after disturbances has been verified by simulation on the IEEE 14-bus power system with the proposed GTI controller. © 2013 IEEE

Stability of multi-generator power system with penetration of renewable energy sources

In this paper, the power system with penetration of renewable energy sources is represented as a multi-machine interconnected system. The power system comprises of conventional synchronous generators and renewable energy sources via rectifier-inverters. A novel controller has been proposed for the inverter that connects the renewable source to the grid while each conventional synchronous generator is equipped with an automatic voltage regulator (AVR) that can be accompanied by a power system stabilizer (PSS). The proposed inverter controller utilizes a dynamically varying gain such that the dynamics of the renewable power source is similar to that of the conventional synchronous generators. Subsequently, stability of the power system is achieved by employing a conventional damping controller. Simulation results on the IEEE 14-bus power system with the proposed renewable energy source controller are provided to show the effectiveness of the approach in damping oscillations that occur after disturbances are removed. The end result is a feedback controller that makes possible for power systems with penetration of renewable energy sources the application of conventional multi-machine stabilizing techniques such as PSS. © 2012 IEEE

Discrete time modeling and control of DC/DC switching converter for solar energy systems

Distributed generation networks including micro grids benefit from solar cells that are controlled by dc-dc converters. In this paper a nonlinear discrete-time model for a buck converter tied to a solar system is derived with unknown internal dynamics. Then, adaptive neural network (NN) controller is employed to enhance stability of dc-dc converter connected to grid-tie inverter (GTI) in the presence of power system disturbances. The NN weights are tuned online by using a novel update law. By using Lyapunov techniques, all signals can be shown to be uniformly ultimately bounded (UUB). In addition, the interaction of the converter with the GTI is investigated to assure stability of the entire interconnected system while the GTI is controlled via a novel stabilizer similar to power system stabilizer (PSS). The proposed nonlinear discrete-time converter controller along with the GTI, equipped with PSS, is simulated in Matlab Simulink environment. The results have highlighted the effectiveness of the proposed modeling and controller design. © 2013 IEEE

Network graph representation of COVID-19 scientific publications to aid knowledge discovery

Introduction Numerous scientific journal articles related to COVID-19 have been rapidly published, making navigation and understanding of relationships difficult.Methods A graph network was constructed from the publicly available COVID-19 Open Research Dataset (CORD-19) of COVID-19-related publications using an engine leveraging medical knowledge bases to identify discrete medical concepts and an open-source tool (Gephi) to visualise the network.Results The network shows connections between diseases, medications and procedures identified from the title and abstract of 195 958 COVID-19-related publications (CORD-19 Dataset). Connections between terms with few publications, those unconnected to the main network and those irrelevant were not displayed. Nodes were coloured by knowledge base and the size of the node related to the number of publications containing the term. The data set and visualisations were made publicly accessible via a webtool.Conclusion Knowledge management approaches (text mining and graph networks) can effectively allow rapid navigation and exploration of entity inter-relationships to improve understanding of diseases such as COVID-19