318 research outputs found

    Demand-Side Threats to Power Grid Operations from IoT-Enabled Edge

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    The growing adoption of Internet-of-Things (IoT)-enabled energy smart appliances (ESAs) at the consumer end, such as smart heat pumps, electric vehicle chargers, etc., is seen as key to enabling demand-side response (DSR) services. However, these smart appliances are often poorly engineered from a security point of view and present a new threat to power grid operations. They may become convenient entry points for malicious parties to gain access to the system and disrupt important grid operations by abruptly changing the demand. Unlike utility-side and SCADA assets, ESAs are not monitored continuously due to their large numbers and the lack of extensive monitoring infrastructure at consumer sites. This article presents an in-depth analysis of the demand side threats to power grid operations including (i) an overview of the vulnerabilities in ESAs and the wider risk from the DSR ecosystem and (ii) key factors influencing the attack impact on power grid operations. Finally, it presents measures to improve the cyber-physical resilience of power grids, putting them in the context of ongoing efforts from the industry and regulatory bodies worldwide

    Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward

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    Advancements in digital automation for smart grids have led to the installation of measurement devices like phasor measurement units (PMUs), micro-PMUs (μ\mu-PMUs), and smart meters. However, a large amount of data collected by these devices brings several challenges as control room operators need to use this data with models to make confident decisions for reliable and resilient operation of the cyber-power systems. Machine-learning (ML) based tools can provide a reliable interpretation of the deluge of data obtained from the field. For the decision-makers to ensure reliable network operation under all operating conditions, these tools need to identify solutions that are feasible and satisfy the system constraints, while being efficient, trustworthy, and interpretable. This resulted in the increasing popularity of physics-informed machine learning (PIML) approaches, as these methods overcome challenges that model-based or data-driven ML methods face in silos. This work aims at the following: a) review existing strategies and techniques for incorporating underlying physical principles of the power grid into different types of ML approaches (supervised/semi-supervised learning, unsupervised learning, and reinforcement learning (RL)); b) explore the existing works on PIML methods for anomaly detection, classification, localization, and mitigation in power transmission and distribution systems, c) discuss improvements in existing methods through consideration of potential challenges while also addressing the limitations to make them suitable for real-world applications

    Review: Monitoring situational awareness of smart grid cyber-physical systems and critical asset identification

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    Cyber-Physical Systems (CPSs) are becoming more automated and aimed to be as efficient as possible by enabling integration between their operations and Information Technology (IT) resources. In combination with production automation, these systems need to identify their assets and the correlation between them; any potential threats or failures alert the relevant user/department and suggest the appropriate remediation plan. Moreover, identifying critical assets in these systems is essential. With numerous research and technologies available, assessing IT assets nowadays can be straightforward to implement. However, there is one significant issue of evaluating operational technology critical assets since they have different characteristics, and traditional solutions cannot work efficiently. This study presents the necessary background to attain the appropriate approach for monitoring critical assets in CPSs' Situational Awareness (SA). Additionally, the study presents a broad survey supported by an in-depth review of previous works in three important aspects. First, it reviews the applicability of possible techniques, tools and solutions that can be used to collect detailed information from such systems. Secondly, it covers studies that were implemented to evaluate the criticality of assets in CPSs, demonstrates requirements for critical asset identification, explores different risks and failure techniques utilised in these systems and delves into approaches to evaluate such methods in energy systems. Finally, this paper highlights and analyses SA gaps based on existing solutions, provides future directions and discusses open research issues

    Physics for Surgeons-Part 5: Optics for Surgeons

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    Optical techniques create a great impact in the biomedical field. Recent advances in the optical techniques (advances in photonics, biomaterials, genetic engineering, and nanotechnology) which are currently used in clinical practice to diagnose and treat the disease. In the present review, we highlight the fundamentals of light and its interaction with matter, applications of optics in the recent techniques so that surgeons can better understand the pattern of disease and find the best way to treat the disease

    Binder-Free Supercapacitors Based on Thin Films of MWCNT/GO Nanohybrids: Computational and Experimental Analysis

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    This work reports an innovative approach to the fabrication of free-standing thin films of multiwalled carbon nanotubes (MWCNTs)/graphene oxide (GO) nanohybrids by using dimethyl formamide (DMF) and n-hexane as a solvent–antisolvent system for the growth of thin films of MWCNTs/GO nanohybrids. The synthesis of the GO was carried out by using the modified Hummers method, while the synthesis of MWCNTs/GO nanohybrids was done by the intermixing of the carboxylic acid functionalized MWCNT and GO using the solution-mixing method. The growth of the thin film of MWCNTs/GO nanohybrids was done by obeying the surface-tension-driven phenomena which occur mainly due to the coalescence of bubbles due to the solvent–antisolvent interfacial tension. Furthermore, density functional theory (DFT)-based first-principles simulations were performed to understand the structural, electronic, and capacitive aspects of MWCNT/GO nanohybrids. The computational results demonstrated excellent quantum capacitance in the MWCNT/GO nanohybrid electrodes. Inspired by the computational results, the same process elaborated above has also been employed to develop binder-free supercapacitor devices utilizing the MWCNT/GO nanohybrid as an electrode material. The electrochemical performance of this electrode in 1 M aqueous H2SO4 demonstrates a good energy density of 21.63 WhKg−1 at a current density of 0.5 Ag−1, with a high specific capacitance of 369.01 F/g at the scan rate of 2 mVs−1 and excellent cyclic stability of 97% for 5000 charge–discharge cycles

    Estimating and Calibrating DER Model Parameters Using Levenberg–Marquardt Algorithm in Renewable Rich Power Grid

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    The proliferation of inverter-based distributed energy resources (IBDERs) has increased the number of control variables and dynamic interactions, leading to new grid control challenges. For stability analysis and designing appropriate protection controls, it is important that IBDER models are accurate. This paper focuses on the accurate estimation and parameter calibration of DER_A, a recently proposed aggregated IBDER model. In particular, we focus on the parameters of the reactive power–voltage regulation module. We formulate the problem of parameter tuning as a non-linear least square minimization problem and solve it using the Levenberg–Marquardt (LM) method. The LM method is primarily chosen due to its flexibility in adaptively selecting between the steepest descent and Gauss–Newton methods through a damping parameter. The LM approach is used to minimize the error between the actual measurements and the estimated response of the model. Further, the computational challenges posed by the numerical calculation of the Jacobian are tackled using a quasi-Newton root-finding approach. The proposed method is validated on a real feeder model in the northeastern part of the United States. The feeder is modeled in OpenDSS and the measurements thus obtained are fed to the DER_A model for calibration. The simulation results indicate that our approach is able to successfully calibrate the relevant model parameters quickly and with high accuracy, with a total sum of square error of 3.57×10−7

    Binder-Free Supercapacitors Based on Thin Films of MWCNT/GO Nanohybrids: Computational and Experimental Analysis

    No full text
    This work reports an innovative approach to the fabrication of free-standing thin films of multiwalled carbon nanotubes (MWCNTs)/graphene oxide (GO) nanohybrids by using dimethyl formamide (DMF) and n-hexane as a solvent–antisolvent system for the growth of thin films of MWCNTs/GO nanohybrids. The synthesis of the GO was carried out by using the modified Hummers method, while the synthesis of MWCNTs/GO nanohybrids was done by the intermixing of the carboxylic acid functionalized MWCNT and GO using the solution-mixing method. The growth of the thin film of MWCNTs/GO nanohybrids was done by obeying the surface-tension-driven phenomena which occur mainly due to the coalescence of bubbles due to the solvent–antisolvent interfacial tension. Furthermore, density functional theory (DFT)-based first-principles simulations were performed to understand the structural, electronic, and capacitive aspects of MWCNT/GO nanohybrids. The computational results demonstrated excellent quantum capacitance in the MWCNT/GO nanohybrid electrodes. Inspired by the computational results, the same process elaborated above has also been employed to develop binder-free supercapacitor devices utilizing the MWCNT/GO nanohybrid as an electrode material. The electrochemical performance of this electrode in 1 M aqueous H2SO4 demonstrates a good energy density of 21.63 WhKg−1 at a current density of 0.5 Ag−1, with a high specific capacitance of 369.01 F/g at the scan rate of 2 mVs−1 and excellent cyclic stability of 97% for 5000 charge–discharge cycles

    Coronal Heating as Determined by the Solar Flare Frequency Distribution Obtained by Aggregating Case Studies

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    Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counter-intuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfv\'en waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, α=2\alpha=2 as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed >>600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: pre-flare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that α=1.63±0.03\alpha = 1.63 \pm 0.03. This is below the critical threshold, suggesting that Alfv\'en waves are an important driver of coronal heating.Comment: 1,002 authors, 14 pages, 4 figures, 3 tables, published by The Astrophysical Journal on 2023-05-09, volume 948, page 7

    Effect of salinity and nitrogen fertilization levels on growth parameters of Sarcocornia fruticosa, Salicornia brachiata, and Arthrocnemum macrostachyum

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    Salinity negatively influences crop growth, but several salt-tolerant plant species (halophytes) are viable crops. Sarcocornia fruticosa (ecotypes EL and VM) is currently cultivated, but there is demand for new crop candidates and higher biomass production. Salicornia brachiata Roxb. and Arthrocneum macrostachyum L. are considered novel crops, and to realize their potential, their response to salinity and nitrogen nutrition was compared to S. fruticosa ecotypes. Experiments revealed that higher N supplemented with lower NaCl significantly increased fresh and dry shoot biomass. Lower biomass was obtained at lower nitrogen supplemented with elevated NaCl, whereas total soluble solids content positively correlated with NaCl fertigation in both Sarcocornia ecotypes. Protein content increased with a lower nitrogen supply. Anthocyanins and oxygen radical absorbance capacity were highest in S. fruticosa EL and A. macrostachyum at higher NaCl supply. The results show that halophytes have a variety of strategies to cope with high NaCl, even between ecotypes of the same species. Notably, repetitive harvesting of S. brachiata delayed flowering enabling year-round biomass production. Additionally, S. brachiata accumulated higher biomass than Sarcocornia VM when grown in a greenhouse at higher radiation than in a growth room and strongly supports its inclusion as a cash-crop halophyte.info:eu-repo/semantics/publishedVersio

    Edge Engineered Graphene Nanoribbons as Nanoscale Interconnect: DFT Analysis

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    Density functional theory (DFT) with non-equilibrium Green\u27s function (NEGF) formalism and HSPICE simulator have been used to analyse the effect of elemental oxygen atom passivation on the structural, electronic and transport properties of Graphene Nanoribbons (GNRs). The analysis of delay, power dissipation, crosstalk effect, stability and frequency analysis has also been performed to understand its interconnect application. The present study includes all the possible morphologies of zigzag and armchair edge states of GNRs with oxygen and hydrogen passivation. The structural stability of GNRs, analysed in terms of binding energy (E-binding) observed that the Zigzag graphene nanoribbon (ZGNR) with both edge oxygen passivated, is the most stable configuration, where stability of the configuration increases with Oxygen concentration. Further, frotextm the bandstructure and density-of-states calculations, it has been observed that considered 6 atom wide and hydrogen passivated Armchair GNR (AGNR) is semiconducting in nature, whereas, with same width hydrogen passivated ZGNR is metallic. On the other hand, passivation of AGNR with increasing concentration of oxygen form them to metallic in nature. Based on the enhanced metallicity and increment in fermi velocity due to passivation of GNRs with oxygen, these structures may be a potential candidate for interconnects. Their computed electron transport properties, dynamical parameters, delay, power delay product and crosstalk induced delay confirms that the zigzag GNR with both the edges passivated with oxygen (O-ZGNR-O) can be considered as best contender for interconnect application due to its remarkable electrical and thermal transport in comparison to other GNR\u27s. The O-ZGNR-O shows lowest value of kinetic inductance and quantum capacitance of. 01032H/m and 2.21 nF/m respectively with higher stability and higher immunity to crosstalk effect in comparison to other proposed GNRs, which is required for nanoscale interconnects. The relative stability of GNRs have been analyzed in terms of nyquist plot with different interconnect lengths. The results suggest that O-ZGNR-O have lowest delay and power dissipation with higher stability, hence defends its application for interconnect application
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