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Abstract: This paper analyzes the influence of inverter-interfaced distributed generations’ (IIDGs) response during transmission network faults. The simplest and safest solution is to switch IIDGs off during network faults without impacting the network voltages. A more elaborate and efficient concept, required by national grid codes, is based on controlling the IIDGs’ currents, involving positive- and negative-sequence voltage measured at the connection point. In this way the magnitude and phase of the injected currents can be adjusted, although the generated power will depend on the actual line voltages at the connection point. Therefore, an improved concept is proposed to adjust IIDGs’ fault current injection through the required active and reactive power, employing the same voltage characteristics. The proposed, i.e., power-based concept, is more definite than the currentbased one, since the required power will always be generated. The discussed concepts for the fault current injection by IIDGs were tested in different 110-kV networks with loop and radial topologies, and for different short-circuit capabilities of the aggregated network supply. Based on extensive numerical calculations, the power-based concept during transmission networks faults generates more reactive power compared to the current-based concept. However, the voltage support by IIDGs during transmission networks faults, regardless of the concept being used, is influenced mainly by the short-circuit capability of the aggregated network supply. As regards distance protection operation, it is influenced additionally by the network topology, i.e., in radial network topology, the remote relay’s operation can be delayed due to a largely seen impedance.
Keywords: distributed generation, fault current injection, voltage support, distance protection, transmission network faults
Published in DKUM: 16.06.2025; Views: 0; Downloads: 2
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Abstract: Higher-order interactions shape collective dynamics, but how they affect transitions between different states in swarmalator systems is yet to be determined. To that effect, we here study an analytically tractable swarmalator model that incorporates both pairwise and higher-order interactions, resulting in four distinct collective states: async, phase wave, mixed, and sync states. We show that even a minute fraction of higher-order interactions induces abrupt transitions from the async state to the phase wave and the sync state. We also show that higher-order interactions facilitate an abrupt transition from the phase wave to the sync state bypassing the intermediate mixed state. Moreover, elevated levels of higher-order interactions can sustain the presence of phase wave and sync state, even when pairwise interactions lean towards repulsion. The insights gained from these findings unveil self-organizing processes that hold the potential to explain sudden transitions between various collective states in numerous real-world systems.
Keywords: collective dynamics, nonlinear oscillator, higher-order interactions, complex network, statistical physics
Published in DKUM: 07.05.2025; Views: 0; Downloads: 1
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Abstract: Navigation in a maize crop is a crucial task for the development of autonomous robots in agriculture, with numerous applications such as spraying, monitoring plant growth and health, and detecting weeds and pests. The Field Robot Event 2023 (FRE) continued to challenge universities and other research teams to push the development of algorithms for agricultural robots further. The Universidad Autónoma Chapingo has been developing a robot for various agricultural tasks, aiming to provide a low-cost alternative to work with Mexican farmers in the future. For this edition of the FRE, a navigation algorithm was created using an encoder, an IMU (Inertial Measurement Unit), an RPLIDAR (Rotating Platform Light Detection and Ranging), and cameras to collect data for decision-making. The algorithm was developed in ROS Melodic, dividing the task into steps that were tested to determine the robot's actual movements. The system navigates by using ROIs (regions of interest) and the mass center to guide the robot between maize rows. It calculates the mean of the final orientation values before reaching the end of a row, which is detected using an RPLIDAR. For turns and straight-line movements to reach the next row, the orientation is used as a guide. To detect plants for spraying, lasers located on each side of the vehicle are employed. Obstacle detection relies on a YOLOv5 (You Only Look Once) trained model and a laser, while reverse navigation uses a rear camera. During the competition, the robot faced challenges such as dealing with grass, the small size of the plants, and the need to use a different power source, which affected its performance.
Keywords: machine vision, convolutional neural network (CNN), regions of interest (ROI), autonomous navigation
Published in DKUM: 23.04.2025; Views: 0; Downloads: 0
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Abstract: Entity alignment refers to the process of discovering entities representing the same object in different knowledge graphs (KG). Recently, some studies have learned other information about entities, but they are aspect-level simple information associations, and thus only rough entity representations can be obtained, and the advantage of multi-faceted information is lost. In this paper, a novel node-level information strong fusion framework (SFEA) is proposed, based on four aspects: structure, attribute, relation and names. The attribute information and name information are learned first, then structure information is learned based on these two aspects of information through graph convolutional network (GCN), the alignment signals from attribute and name are already carried at the beginning of the learning structure. In the process of continuous propagation of multi-hop neighborhoods, the effect of strong fusion of structure, attribute and name information is achieved and the more meticulous entity representations are obtained. Additionally, through the continuous interaction between sub-alignment tasks, the effect of entity alignment is enhanced. An iterative framework is designed to improve performance while reducing the impact on pre-aligned seed pairs. Furthermore, extensive experiments demonstrate that the model improves the accuracy of entity alignment and significantly outperforms 13 previous state-of-the-art methods.
Keywords: knowledge graph, entity ealignment, graph convolutional network, knowledge fusion
Published in DKUM: 27.03.2025; Views: 0; Downloads: 5
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Abstract: Physics has a long tradition of laying rigorous quantitative foundations for social phenomena. Here, we up the ante for physics' forays into the territory of social sciences by (i) empirically documenting a tipping point in the relationship between democratic norms and corruption suppression, and then (ii) demonstrating how such a tipping point emerges from a micro-scale mechanistic model of spin dynamics in a complex network. Specifically, the tipping point in the relationship between democratic norms and corruption suppression is such that democratization has little effect on suppressing corruption below a critical threshold, but a large effect above the threshold. The micro-scale model of spin dynamics underpins this phenomenon by reinterpreting spins in terms of unbiased (i.e. altruistic) and biased (i.e. parochial) other-regarding behaviour, as well as the corresponding voting preferences. Under weak democratic norms, dense social connections of parochialists enable coercing enough opportunist voters to vote in favour of perpetuating parochial in-group bias. Society may, however, strengthen democratic norms in a rapid turn of events during which opportunists adopt altruism and vote to subdue bias. The emerging model outcome at the societal scale thus mirrors the data, implying that democracy either perpetuates or suppresses corruption depending on the prevailing democratic norms.
Keywords: tipping point, complex network, sociophysics
Published in DKUM: 13.03.2025; Views: 0; Downloads: 2
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