Control Strategies of DC Microgrids Cluster:A Comprehensive Review

Multiple microgrids (MGs) close to each other can be interconnected to construct a cluster to enhance reliability and flexibility. This paper presents a comprehensive and comparative review of recent studies on DC MG clusters’ control strategies. Different schemes regarding the two significant control aspects of networked DC MGs, namely DC-link voltage control and power flow control between MGs, are investigated. A discussion about the architecture configuration of DC MG clusters is also provided. All advantages and limitations of various control strategies of recent studies are discussed in this paper. Furthermore, this paper discusses three types of consensus protocol with different time boundaries, including linear, finite, and fixed. Based on the main findings from the reviewed studies, future research recommendations are proposed

Optimal Coordinated Control of DC Microgrid Based on Hybrid PSO–GWO Algorithm

Microgrids (MGs) are capable of playing an important role in the future of intelligent energy systems. This can be achieved by allowing the effective and seamless integration of distributed energy resources (DERs) loads, besides energy-storage systems (ESS) in the local area, so they are gaining attraction worldwide. In this regard, a DC MG is an economical, flexible, and dependable solution requiring a trustworthy control structure such as a hierarchical control strategy to be appropriately coordinated and used to electrify remote areas. Two control layers are involved in the hierarchy control strategy, including local- and global-control levels. However, this research focuses mainly on the issues of DC MG’s local control layer under various load interruptions and power-production fluctuations, including inaccurate power-sharing among sources and unregulated DC-bus voltage of the microgrid, along with a high ripple of battery current. Therefore, this work suggests developing local control levels for the DC MG based on the hybrid particle swarm optimization/grey wolf optimizer (HPSO–GWO) algorithm to address these problems. The key results of the simulation studies reveal that the proposed control scheme has achieved significant improvement in terms of voltage adjustment and power distribution between photovoltaic (PV) and battery technologies accompanied by a supercapacitor, in comparison to the existing control scheme. Moreover, the settling time and overshoot/undershoot are minimized despite the tremendous load and generation variations, which proves the proposed method’s efficiency

Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children

Our study aims to identify children at risk of developing high myopia for timely assessment and intervention, preventing myopia progression and complications in adulthood through the development of a deep learning system (DLS). Using a school-based cohort in Singapore comprising 998 children (aged 6-12 years old), we train and perform primary validation of the DLS using 7456 baseline fundus images of 1878 eyes; with external validation using an independent test dataset of 821 baseline fundus images of 189 eyes together with clinical data (age, gender, race, parental myopia, and baseline spherical equivalent (SE)). We derive three distinct algorithms - image, clinical, and mix (image + clinical) models to predict high myopia development (SE ≤ -6.00 diopter) during teenage years (5 years later, age 11-17). Model performance is evaluated using the area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93-0.95; Test dataset 0.91-0.93), clinical models (Primary dataset AUC 0.90-0.97; Test dataset 0.93-0.94) and mixed (image + clinical) models (Primary dataset AUC 0.97; Test dataset 0.97-0.98) achieve clinically acceptable performance. The addition of 1 year SE progression variable has minimal impact on the DLS performance (clinical model AUC 0.98 versus 0.97 in the primary dataset, 0.97 versus 0.94 in the test dataset; mixed model AUC 0.99 versus 0.97 in the primary dataset, 0.95 versus 0.98 in test dataset). Thus, our DLS allows prediction of the development of high myopia by teenage years amongst school-going children. This has potential utility as a clinical decision support tool to identify "at-risk" children for early intervention.info:eu-repo/semantics/publishedVersio

Insights to plant–microbe interactions provide opportunities to improve resistance breeding against root diseases in grain legumes

Root and foot diseases severely impede grain legume cultivation worldwide. Breeding lines with resistance against individual pathogens exist, but these resistances are often overcome by the interaction of multiple pathogens in field situations. Novel tools allow to decipher plant–microbiome interactions in unprecedented detail and provide insights into resistance mechanisms that consider both simultaneous attacks of various pathogens and the interplay with beneficial microbes. Although it has become clear that plant‐associated microbes play a key role in plant health, a systematic picture of how and to what extent plants can shape their own detrimental or beneficial microbiome remains to be drawn. There is increasing evidence for the existence of genetic variation in the regulation of plant–microbe interactions that can be exploited by plant breeders. We propose to consider the entire plant holobiont in resistance breeding strategies in order to unravel hidden parts of complex defence mechanisms. This review summarizes (a) the current knowledge of resistance against soil‐borne pathogens in grain legumes, (b) evidence for genetic variation for rhizosphere‐related traits, (c) the role of root exudation in microbe‐mediated disease resistance and elaborates (d) how these traits can be incorporated in resistance breeding programmes

The role of predictability in the stress response of a cichlid fish

In recent years there has been an increasing interest in the cognitive abilities of fish with implications for animal welfare and management of rearing operations. Although it is known that psychological factors can modulate the stress response in mammals, this aspect has seldom been investigated within stress in fish. In this study we investigate whether the perception (appraisal) that fish make of significant environmental events modifies their behavioural and physiological response. For this purpose we have used a predictable vs. unpredictable paradigm for positive (feeding) and negative (confinement) events using the cichlid fish Oreochromis mossambicus as a model species. Results show that there is a differential effect of predictability for the feeding and confinement events. In the confinement experiment, predictability involved more attention to the visual cue and lower cortisol. The feeding event triggered higher levels of anticipatory behaviour and a tendency for higher cortisol in the predictable group. Therefore, predictable negative events reduce the cortisol response. Predictable positive events may elicit an anticipatory response, and when there is a significant delay between the visual cue and the actual occurrence of the event, it may also contain elements that can be interpreted as a stress response. These findings demonstrate that fish can appraise relevant aspects of the environment, with welfare implications for housing, husbandry and experimental procedures

Cortical pattern separation and item-specific memory encoding

Pattern separation and pattern completion are fundamental brain processes thought to be critical for episodic memory encoding and retrieval, and for discrimination between similar memories. These processes are best understood in the hippocampus, but are proposed to occur throughout the brain, in particular in sensory regions. Cortical, as well as hippocampal, pattern separation may therefore support formation of event-unique memory traces. Using fMRI, we investigated cortical pattern separation and pattern completion and their relationship to encoding activity predicting subsequent item-specific compared to gist memory. During scanning, participants viewed images of novel objects, repeated objects, and objects which were both perceptually and conceptually similar to previously presented images, while performing a size judgement task. In a later surprise recognition test, they judged whether test items were ‘same’ ‘similar’ or ‘new’ relative to studied items. Activity consistent with pattern separation – responses to similar items as if novel – was observed in bilateral occipito-temporal cortex. Activity consistent with pattern completion – responses to similar items as if repeated – was observed in left prefrontal cortex and hippocampus. Curve fitting analysis further revealed that graded responses to change in image conceptual and perceptual similarity in bilateral prefrontal and right parietal regions met specific computational predictions for pattern separation for one or both of these similarity dimensions. Functional overlap between encoding activity predicting subsequent item-specific recognition and pattern separation activity was also observed in left occipital cortex and bilateral inferior frontal cortex. The findings suggest that extrahippocampal regions including sensory and prefrontal cortex contribute to pattern separation and pattern completion of visual input, consistent with the proposal that cortical pattern separation contributes to formation of item-specific memory traces, facilitating accurate recognition memory