Disturbance Scenarios Research Articles

Design of double-integral sliding-mode controller for fourth-order double input DC - DC boost converter with a voltage multiplier cell

ABSTRACT A voltage multiplier cell (VMC)-integrated dual-input boost converter (DIBC) with an inductor at the two inputs for dual boost, and working at a duty cycle greater than 0.5 is a requirement for achieving a high voltage gain from low-voltage energy sources. The main focus of this work is to design a suitable control to effectuate the voltage regulation during input voltage and load disturbances, as well as to reduce the start-up transient. The pulse-width-modulation-based double-integral sliding-mode controller was investigated for desirable voltage regulation. Two input inductor current references are incorporated in the controller design to facilitate the decoupling between the interacting loops, which render the design complex with two sliding surfaces for each input inductor current. Also, a comparative analysis with dual-loop proportional-integral control for DIBC with single VMC is presented to highlight the robustness of the proposed controller. A converter model of 200 W is developed, and the controller is carried out using a field-programmable gate array. Under various disturbance scenarios, MATLAB simulation and experimental results clearly showed the satisfactory transient and steady-state output voltage regulation with the proposed controller.

Geomagnetically Induced Current Modeling in New Zealand: Extreme Storm Analysis Using Multiple Disturbance Scenarios and Industry Provided Hazard Magnitudes

AbstractGeomagnetically induced currents (GICs) are induced in electrical power transmission networks during geomagnetic disturbances. Understanding the magnitude and duration of the GIC expected during worst‐case extreme storm scenarios is vital to estimate potential damages and disruptions to power networks. In this study we utilize the magnetic field waveforms measured during three large geomagnetic storms and scale them to expected worst case extreme storm magnitudes. Multiple methods are used to simulate the varying magnitude of the magnetic field across the different latitudes of New Zealand. Modeled GIC is produced for nine extreme storm scenarios, each covering 1–1.5 days in duration. Our industry partners, Transpower New Zealand Ltd provided GIC magnitude and duration levels which represent a risk to their transformers. Using these thresholds various extreme storm scenarios predict between 44 and 115 New Zealand transformers (13%–35%) are at risk of damaging levels of GIC. The transformers at risk are largely independent of the extreme storm time‐variations, but depend more on the latitude variation scenario. We show that these at‐risk transformers are not localized to any specific region of New Zealand but extend across all regions and include most of the major population centers. A peak mean absolute GIC over a 60‐min window of 920–2,210 A and an instantaneous 1‐min time resolution maximum GIC of 1,590–4,920 A occurs for a worst‐case extreme storm scenario. We believe this is one of the first studies to combine a reasonable worst‐case extreme geomagnetic storm with validated GIC modeling and industry‐provided GIC risk thresholds.

Functional diversity of experimental annual plant assemblages drives plant responses to biological soil crusts in gypsum systems

Abstract Biological soil crusts (BSC) are complex biotic aggregates comprised of lichens, cyanobacteria, algae and other micro‐organism that are known to differently affect plant development along life cycle by selecting plant functional traits based on species‐specific effects. In addition, functional differences between interacting species should modulate their response ability to other environmental factors. Thus, it should be expected that the effects of the BSC on plants will be significantly determined by the own functional diversity in the community. To understand the multiple effects of BSC and the extent to which the functional diversity of interacting plant species can modulate their effects on the development of coexisting species, we applied an experimental approach by manipulating the initial functional diversity of the entire annual plant community and BSC conditions in a common garden trial. We crossed three sorts of assemblages built on the basis of plant stature (combinations of only large, or only small, or diverse sized plant species in pots) with three lichen‐dominated BSC disturbance scenarios (intact, or tiny mechanically disaggregated, or absent portions of BSC). BSC strongly affected the establishment and development of gypsophilous annual plants in a complex, multifaceted manner, which shifted throughout the plant life cycle. We demonstrated that lichen‐dominated BSC could act as a major physical barrier to the establishment of annual plants at a heterogeneous fine spatial scale. Such a restrictive effect was particularly marked in the presence of intact BSC. However, after annual plants overcame the restrictions imposed by BSC, the same biotic layer facilitated plant growth and fitness, regardless of its physical integrity, resulting in larger plants producing more fruits. Importantly, our results suggest that the functional diversity structure of the community may also drive growth and fitness of coexisting species by activating alternative coexistence mechanisms such as niche partitioning or competition symmetry. This study highlights the importance of plant neighbourhood features for the performance of interacting species, and confirms a novel, experimental way to explore the effects of community diversity on plants for the interpretation of assembly mechanisms. Read the free Plain Language Summary for this article on the Journal blog.

Interaction effects of crude oil and nutrient exposure on settlement of coral reef benthos

Anthropogenic stressors increasingly cause ecosystem-level changes to sensitive marine habitats such as coral reefs. Intensification of coastal development and shipping traffic can increase nutrient and oil pollution on coral reefs, yet these two stressors have not been studied in conjunction. Here, we simulate a disturbance scenario exposing carbonate settlement tiles to nutrient and oil pollution in a full-factorial design with four treatments: control, nutrients, oil, and combination to examine community structure and net primary productivity (NPP) of pioneer communities throughout 28 weeks. Compared to the control treatment oil pollution decreased overall settlement and NPP, while nutrients increased turf algae and NPP. However, the combination of these two stressors resulted in similar community composition and NPP as the control. These results indicate that pioneer communities may experience shifts due to nutrient enrichment, and/or oil pollution. However, the timing and duration of an event will influence recovery trajectories requiring further study.

Hierarchical Model Predictive Control for On-Line High-Speed Railway Delay Management and Train Control in a Dynamic Operations Environment

In practice, the operation of high-speed trains is often affected by adverse weather conditions or equipment failures, which result in delays and even cancellations of train services. In this article, a novel two-layer hierarchical model predictive control (MPC) model is proposed for on-line high-speed railway delay management and train control for minimizing train delays and cancellations. The upper layer manages the global objectives of the train operation, that is, minimizing the total train delays and providing guidance for the speed control in the lower layer. The objectives of the lower layer are to satisfy the running time requirements given by the upper layer and to save energy at the same time. The optimization problems in both levels of the hierarchical MPC framework are formulated as small-scale mixed integer linear programming problems, which can be solved efficiently by existing solvers. Particularly, the train control problem is solved in a distributed way for each train. Simulation analysis based on the real-life data of the Beijing–Shanghai high-speed railway shows that the proposed hierarchical MPC framework can meet the real-time requirements and reduce train delays effectively when compared with widely accepted strategies, for example, first-scheduled-first-serve and first-come-first-serve. Moreover, the proposed hierarchical MPC framework also provides good robustness performance for different disturbance scenarios.

Measuring urban ecological network resilience: A disturbance scenario simulation method

Human disturbance of urban ecosystems is intensifying, as is ecological fragmentation. As such, effective connections between green spaces must be established and restored to form structurally resilient urban ecological networks. In this study, we focused on indicators of city-scale ecological network resilience. We constructed a network model, analyzed the disturbance simulation results, and identified key nodes. Shenzhen, a megacity in China was selected for empirical research. First, we used Floyd algorithm to extract least-cost paths and then generate the corridor network, constructing an ecological network model with 386 nodes and 4910 edges. Second, focusing on the nodes of the constructed ecological network, we adopted a selected attack strategy to conduct dynamic simulations, using two parameters, network efficiency and maximum connectivity, to evaluate the resilience of the ecological network to changes under various disturbance scenarios. The results showed that the ecological network structure in the study area was relatively stable, and the first 30 % of nodes substantially impacted network resilience. The outcomes reflect the state of urban ecosystems that have been disturbed by socio-economic systems and are practically important for formulating adaptive spatial planning and management policies.

Trait-trait relationships and tradeoffs vary with genome size in prokaryotes.

We report genomic traits that have been associated with the life history of prokaryotes and highlight conflicting findings concerning earlier observed trait correlations and tradeoffs. In order to address possible explanations for these contradictions we examined trait-trait variations of 11 genomic traits from ~18,000 sequenced genomes. The studied trait-trait variations suggested: (i) the predominance of two resistance and resilience-related orthogonal axes and (ii) at least in free living species with large effective population sizes whose evolution is little affected by genetic drift an overlap between a resilience axis and an oligotrophic-copiotrophic axis. These findings imply that resistance associated traits of prokaryotes are globally decoupled from resilience related traits and in the case of free-living communities also from traits associated with resource availability. However, further inspection of pairwise scatterplots showed that resistance and resilience traits tended to be positively related for genomes up to roughly five million base pairs and negatively for larger genomes. Genome size distributions differ across habitats and our findings therefore point to habitat dependent tradeoffs between resistance and resilience. This in turn may preclude a globally consistent assignment of prokaryote genomic traits to the competitor - stress-tolerator - ruderal (CSR) schema that sorts species depending on their location along disturbance and productivity gradients into three ecological strategies and may serve as an explanation for conflicting findings from earlier studies. All reviewed genomic traits featured significant phylogenetic signals and we propose that our trait table can be applied to extrapolate genomic traits from taxonomic marker genes. This will enable to empirically evaluate the assembly of these genomic traits in prokaryotic communities from different habitats and under different productivity and disturbance scenarios as predicted via the resistance-resilience framework formulated here.

Resilience Assessment of Traffic Networks in Coastal Cities under Climate Change: A Case Study of One City with Unique Land Use Characteristics

How to assess the risk of flood disasters and improve the resilience of coastal cities has become a scientific problem that must be solved urgently. This paper aims to construct a resilience assessment model for transport systems in the context of climate change based on an analysis of the spatial characteristics of regional transport networks and complex network theory, using the Pudong New Area in Shanghai, China as the empirical object. Other objectives of the developed model are to establish a system of homogeneity, efficiency, and stability indicators and to assess the impact of flood depth (up to 7 m) on the resilience of transport networks in terms of static network structure and dynamic network performance by designing flood inundation disturbance scenarios. Finally, the characteristics, change trends, and conceptual connotations of the resilience of transport networks in coastal cities are condensed. The results of this study provide a solid scientific basis for future flood disaster risk management in global coastal cities.

Control for plasma glucose regulation in type 1 diabetes mellitus patients with unknown input delays

This paper presents a control strategy for plasma glucose regulation in the presence of input with unknown time delay (constant or variable) and meal disturbances in the system. The control scheme proposed is an observer-based state feedback controller. The observer achieves to estimate the state vector even if the control input in the system presents an unknown time delay in the input. The main advantage of the controller is that the gains are selected so the control law will provide positive values that ensure an implementable case. The observer-based control scheme is validated considering different disturbance and unknown delays scenarios, in both cases, glucose regulation is achieved, rejecting meal disturbances in 4 hours and avoiding hypoglycemic episodes.

ASSESSMENTS OF SPATIO-TEMPORAL DYNAMICS OF FOREST COVER CHANGES, DEFORESTATION RATES, ITS CAUSES AND CONSEQUENCES: A CASE STUDY OF CHAUPAHARI JUNGLE, BIRBHUM DISTRICT (WEST BENGAL, INDIA)

Forest considered as a lungs of nature, because of its great significance on our ecosystem. But its economic importance it has become as open treasure to human society. And the result is deforestation. In the present age of mechanized civilization in the name of development deforestation has been taking place. Extensive deforestation has caused entropy in ecosystem (like loss of biodiversity, global warming, ozone depletion etc.) and above all on human society. Generally, greater importance is given to the conservation of those forest which covered large areas (reserve forest, national park), but localized forest (less extensive) which has a significant impact on the local climate and human, is the most vulnerable, being neglected by the authorities or people. In this regard this paper highlights spatio-temporal changes of Chaupahari jungle, Birbhum district (West Bengal, India) from 1990-2021 and predicted for the year of 2030 using the current disturbance scenario with help of RS-GIS technique. Over the 31 years periods shows that this deciduous forest primarily being degraded in the form of anthropogenic pressure. Forest are natures precious gift to mankind, which play a vital role to maintain a stable environment. So, deforestation rate in the study area has serious repercussion for natural resources, ecology and socio-economic condition of the locals. To secure future and improvement of this forest adequate attention are required which promote stress free environment.

Managing for red‐cockaded woodpeckers is more complicated under climate change

AbstractOpen pine (Pinus spp.) savannas are home to the federally endangered red‐cockaded woodpecker (Leuconotopicus borealis). Frequent fires are essential for maintaining the open canopy and wiregrass (Aristida stricta) groundcover preferred by these woodpeckers, which face ongoing threats from climate change, hurricanes, and land use change. Our objective was to project future changes in habitat for red‐cockaded woodpeckers at the Fort Bragg military installation in North Carolina, USA, under different scenarios of climate and disturbances from 2000 to 2050. We used a spatially explicit, forest simulation model (LANDIS‐II) to simulate climate change, hurricanes, and forest management (prescribed fire, thinning, harvesting). We examined the relative risks of climate change and hurricanes to red‐cockaded woodpecker habitat and quantified the capacity of management practices to promote their habitat, given hurricanes and projected changes in climate. Climate change had little direct effect on red‐cockaded woodpecker habitat, but it reduced the capacity of managers to use prescribed fire, as currently practiced. This effect became more pronounced in the scenario with low prescribed fire frequency, which caused a 10% decline in habitat under climate change. Hurricanes had a more substantial impact on red‐cockaded woodpecker habitat than climate change or management, causing an average decline in extent by 41% compared to scenarios without hurricanes. Our work suggests that hurricanes pose a greater threat to red‐cockaded woodpecker habitat than climate change, but despite these threats, overall habitat will likely still increase until 2050, which bodes well for this endangered species. Deployment of prescribed burning became more challenging under climate warming, and if this trend continues, it may threaten red‐cockaded woodpecker conservation efforts over the next century.

Research Progress and Framework Construction of Urban Resilience Computational Simulation

Simulating the dynamic process of urban resilience and analyzing the mechanism of resilience-influencing factors are of great significance to improve the intelligent decision-making ability of resilient urban planning. The purpose of this article is to implement a comprehensive literature review on the quantitative computation and simulation studies of urban resilience, investigating the characteristics of current research, including the most commonly applied methods, the most frequently space–time scales, the most popular research topics, and the most commonly involved risk types. Then, the study provides recommendations for future research: (1) research on multiple risk disturbance scenarios, (2) the computation of urban resilience from the public perspective, and (3) a computation-simulation framework with the goal of revealing the mechanism. Finally, this study constructs a resilience-computation simulation framework for resilient urban planning, which lays a foundation for the further development of urban-resilience dynamic-simulation computing and planning-scenario applications in the future.

DRDNN: A robust model for time-variant nonlinear optimization under multiple equality and inequality constraints

External disturbances ubiquitously exist in time-variant problems, including the time-variant nonlinear optimization problems. To develop feasible neural network for time-variant nonlinear optimization restricted to multiple equality and inequality constraints is a bottleneck problem due to its high complexity. In this paper, a novel disturbance rejection dynamic neural network (DRDNN) is proposed to handle nonlinear optimization with multiple equality and inequality constraints concerning external disturbances. According to Lagrange multiplier rule and Karush–Kuhn–Tucker condition, the original optimization problem restricted to multiple equality and inequality constraints is firstly transformed into a time-variant dynamic equation set. Then, detailed design process of the DRDNN model is developed accordingly. The proposed DRDNN inherently possesses the effectiveness and robustness by leveraging the time-derivative as well as the time-integration information. Theorems and proofs about stability, convergence property, and robustness against different forms of disturbances are provided. Finally, different examples, comparisons as well as tests substantiate the accuracy, superiority and robustness of DRDNN for nonlinear optimization limited by multiple equality and inequality constraints in disturbed scenarios.

Neural Adaptive Robust Motion-Tracking Control for Robotic Manipulator Systems

This paper deals with the motion trajectory tracking control problem based on output feedback and artificial neural networks for anthropomorphic manipulator robots under disturbed operating scenarios. This class of manipulator robots constitutes nonlinear dynamic systems subjected to disturbance torques induced mainly by work payload. Parametric uncertainty and possible dynamic modeling errors stand for other kind of disturbances that can deteriorate the efficiency and robustness of the tracking of controlled nonlinear robotic system trajectories. In fact, the presence of unknown dynamic disturbances is unavoidable in industrial robotic engineering systems. Therefore, for high-precision applications, such as laser cutting, marking, or welding, effective control schemes should be designed to guarantee adequate motion profile tracking planned on this class of disturbed nonlinear robotic system. In this context, a new adaptive robust motion trajectory tracking control scheme based on output feedback and artificial neural networks of anthropomorphic manipulator robots is presented. Three-layer B-spline artificial neural networks and time-series modeling are properly exploited in the design of novel adaptive robust motion tracking controllers for robotic applications of laser manufacturing. In this way, dependency on detailed nonlinear mathematical modeling of robotic systems is considerably reduced, and real-time estimation of uncertain dynamic disturbances is not required. Furthermore, several cases studies to demonstrate the motion planning tracking control robustness for a class of MIMO nonlinear robotic systems are described. blue Insights for the extension of the introduced output-feedback adaptive neural control design approach for other architecture of nonlinear robotic systems are depicted.

A novel approach for single and combined power quality disturbances detection using fundamental and harmonic phasors estimation model

The growing use of non-linear loads is one of the main reasons for harmonic distortions in distribution systems. Power quality disturbances have caused improper performance of electrical equipment and have created many technical and economic problems for industries and customers. One approach to overcome these problems and also the correct detection of PQ disturbances is power quality monitoring (PQM). Measurement and estimation are two essential tools to understand and investigate PQ problems in distribution networks. Here, a new estimation model is used to simultaneously extract the fundamental and harmonic phasors information of the system. In other words, the proposed estimation model has been used to access system-level data. Also, the recursive variational mode extraction (RVME) approach efficiency has been investigated for detecting power quality disturbances (PQDs). Finally, to validate the effectiveness of the proposed PQDs detection method, six different scenarios of disturbances are investigated. The IEEE 13-bus and IEEE 34-bus standard distribution test networks are used to evaluate the efficiency of the proposed method. Also, the real-time PQ disturbances are used for validating the proposed PQDs detection method. Results obtained demonstrate the efficiency and high accuracy of the proposed approach in correctly diagnosing real-time PQ disturbances.

Gray whale habitat use and reproductive success during seismic surveys near their feeding grounds: comparing state-dependent life history models and field data

We used a stochastic dynamic programming (SDP) model to quantify the consequences of disturbance on pregnant western gray whales during one foraging season. The SDP model has a firm basis in bioenergetics, but detailed knowledge of minimum reproductive length of females (Lmin) and the relationship between length and reproductive success (Rfit) was lacking. We varied model assumptions to determine their effects on predictions of habitat use, proportion of animals disturbed, reproductive success, and the effects of disturbance. Smaller Lmin values led to higher predicted nearshore habitat use. Changes in Lmin and Rfit had little effect on predictions of the effect of disturbance. Reproductive success increased with increased Lmin and with higher probability of reproductive success by length. Multiple seismic surveys were conducted in 2015 off the northeast coast of Sakhalin Island, with concomitant benthic prey surveys, photo-identification studies, and whale distribution sampling, thus providing a unique opportunity to compare output from SDP models with empirical observations. SDP model predictions of reproductive success and habitat use were similar with and without acoustic disturbance, and SDP predictions of reproductive success and large-scale habitat use were generally similar to values and trends in the data. However, empirical estimates of the proportion of pregnant females nearshore were much higher than SDP model predictions (a large effect, measured by Cohen’s d) during the first week, and the SDP model overestimated whale density in the south and underestimated density around the mouth of Piltun Bay. Such differences in nearshore habitat use would not affect SDP predictions of reproductive success or survival under the current seismic air gun disturbance scenario.

Predicting shifts in demography of Orbicella franksi following simulated disturbance and restoration

Disturbances of coral reefs are increasing in frequency, intensity, and duration. These changes will likely result in demographic shifts in many populations of reef-building corals with unknown consequences for ongoing coral restoration efforts. To address this knowledge gap, here we use empirically derived stage-based matrix population models to predict how a relatively stable population and areal coverage of Orbicella franksi may change under simulated disturbance and restoration scenarios. Overall, simulated restoration outplanting greatly increased the number of O. franksi colonies and overall estimated areal coverage when compared to baseline population estimates. Under a mild disturbance scenario, the number of O. franksi colonies were projected to decrease by up to 90% by 2050, but simulated restoration was predicted to offset the loss in number of colonies. Under a severe disturbance scenario, the number of O. franksi colonies also decreased, but simulated restoration efforts were not able to offset colony losses. Under both disturbance scenarios there was a large projected loss of O. franksi areal coverage even when restoration was implemented. However, restoration prevented a rapid decrease in number of colonies in the severe disturbance scenario. These findings highlight the potentially catastrophic effects of disturbances on previously stable coral populations, and the role restoration can play in mitigating these threats. Increasing studies focused on widespread ecological and demographic monitoring of disturbed and restored corals will be critical in the development of more effective restoration strategies for conserving these threatened species in an uncertain future.

Optimal operation method for load restoration of distribution networks under disturbance scenario

Extreme events can cause damages to transmission system equipment, resulting in a major power outage. In disturbance scenario, local distributed energy resources (DERs) and switches can be fully utilized to restore power loads, which is critical for enhancing the resiliency. A service restoration model for distribution network with soft open point (SOP) is proposed, in which the ZIP loads are considered. Traditional tie switch (TS) operations and the power output capabilities of local DERs are coordinated to restore loads in outage area. On this basis, SOP flexibility is explored to further improve the restoration performance. The restoration model includes nonlinear constraints and integer variables, which is usually a mixed-integer nonlinear programming (MINLP) problem, and is difficult to obtain a feasible solution within an acceptable time. In particular, the ZIP load model is not directly amenable with conic solvers. To improve solution efficiency, convex relaxation, linear approximation, and big-M methods are introduced to convert MINLP problem into a mixed-integer second-order cone programming (MISOCP) problem. Simulation tests are studied to verify the effectiveness of the proposed model.

At the Edge of Voltage Collapse in Slovenian Power System After Outage of NPP Krško and 400 kV Node Tumbri

A major disturbance that resulted in the tripping of the 400 kV line Tumbri (HR) – Hévíz (H) and the entire 400 kV node Tumbri in the Croatian system occurred on 27 August 2003 and almost led to a voltage collapse in that area. The disturbance was initiated by an accidental outage of a generator at NPP Krško (SLO) due to a human error during regular maintenance work. Its further development was the consequence of a hidden fault of the numeric protection at the 400 kV switchyard Tumbri. In the specific system operating conditions (network configuration and generator scheduling) at that moment, the systems of Slovenia and Austria in the central part of the UCTE system were brought near voltage collapse. At the same time the operational security level of the power systems of Croatia and BiH, then at the perimeter of the UCTE system, was also decreased. The sequence of events is described and discussed in the paper. Recordings of frequency and active power in representative transmission lines, taken at NDC Zagreb, are also given. A simulation analysis was performed first for the real disturbance scenario and then for the hypothetical scenario in which there was no failure of the protection at Tumbri substation and consequently the 400 kV node Tumbri would have remained in operation. The simulations showed that the loss of a NPP Krško generator would have only a local impact and would be barely noticeable at the UCTE level had the 400 kV node Tumbri remained in operation. Simulation results are in accordance with information obtained from the Slovenian power system where severely depressed voltages were recorded, particularly in the area around NPP Krško. It was also observed that the Slovenian system was acting as a reactive power sink while active power was flowing in the opposite direction, i.e. towards Croatia. Assessment of Croatian system security during the disturbance with respect to voltage stability, frequency and angle stability and congestion criteria was done on the basis of measurements and simulation results. The conclusion was that Croatian system security with respect to all the above mentioned criteria was preserved but it must be stressed that the initial system operating conditions were rather favorable from the stability point of view.

Deep reinforcement learning for rendezvous guidance with enhanced angles-only observability

This research studies the application of reinforcement learning in spacecraft angles-only rendezvous guidance in the presence of multiple constraints and uncertainties. To apply a standard reinforcement learning framework, a stochastic optimal control problem is constructed as a time-discrete Markov decision process, and the observability of angles-only navigation is included in the construction of a reward function. A proximal policy optimization algorithm is used to train a deep neural network (DNN) to map observations of spacecraft state to an optimal control strategy. The trained guidance control network provides a robust nominal orbit and the corresponding closed-loop guidance law, which has the potential to improve the performance of closed-loop navigation, guidance and control. Numerical simulation results are given for an example of far-rendezvous approach guidance. The influence of different weights of the observability term in the reward function on the optimal control strategy is studied. The robustness of the obtained closed-loop control law is evaluated and verified through Monte Carlo simulations in disturbed scenarios.