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This study aimed to analyze the 100 most-cited papers in the field of oral medicine and pathology over time, identifying the areas of more intense research. Papers in journals of oral medicine and pathology were identified using the Web of Science database. The specified research period was between 1900 and 2019. Descriptive statistics was used to analyze the data. Pearson's correlation analysis was used to explore the relationships among Web of Science citations, Dimensions citations, and Altmetric Attention Score. The number of citations of an article in the top 100 most-cited papers published in 1953 or later ranged from 541 to 3623. The papers were published in 47 different journals. The New England Journal of Medicine, American Cancer Society, and Nature Genetics published the most papers. Authors from 18 different countries published papers on head and neck cancer, craniofacial congenital anomalies, and osteonecrosis. Most of the papers were laboratory and descriptive studies. A correlation analysis showed a strong correlation only between Web of Science and Dimensions citations. In sum, although non-specific journals for pathology and oral medicine published the majority of the 100 most-cited papers, this biometric citation study show that head and neck cancer was the issue with the most citations. Together, these results make an important scientific contribution by providing a historical perspective on the research carried out. [ABSTRACT FROM AUTHOR]

Background: radiotherapy is one of the major treatments for lung cancer and has been a hot research area for years. This bibliometric analysis aims to present the research trends on lung cancer radiotherapy. Method: On August 31, 2022, the authors identified 9868 articles on lung cancer radiotherapy by the Web of Science (Science Citation Indexing Expanded database) and extracted their general information and the total number of citations. A bibliometric analysis was carried out to present the research landscape, demonstrate the research trends, and determine the most cited papers (top-papers) as well as top-journals on lung cancer radiotherapy. After that, the authors analyzed the recent research hotspots based on the latest publications in top-journals. Results: These 9868 papers were cited a total of 268,068 times. "Durvalumab after chemoradiotherapy in stage III non-small-cell lung cancer" published in 2017 by Antonia et al. was the most cited article (2110 citations). Among the journals, New England Journal of Medicine was most influential. Moreover, J. Clin. Oncol. and Int. J. Radiat. Oncol. Biol. Phys. was both influential and productive. Corresponding authors represented the USA (2610 articles) and China mainland (2060 articles) took part in most publications and articles with corresponding authors from Netherlands were most cited (46.12 citations per paper). Chemoradiotherapy was the hottest research area, and stereotactic body radiotherapy has become a research hotspot since 2006. Radiotherapy plus immunotherapy has been highly focused since 2019. Conclusions: This bibliometric analysis comprehensively and quantitatively presents the research trends and hotspots based on 9868 relevant articles, and further suggests future research directions. The researchers can benefit in selecting journals and in finding potential collaborators. This study can help researchers gain a comprehensive picture of the research landscape, historical development, and recent hotspots in lung cancer radiotherapy and can provide inspiration for future research. [ABSTRACT FROM AUTHOR]

This paper presents a current literature review (from the years 2017–2022) on issues related to the application of power system stabilizers (PSSs) for damping electromechanical swings in power systems (PSs). After the initial selection of papers found in the databases used, over 600 publications were qualified for this review, of which 216 were subjected to detailed analysis. In the review, issues related to the following problems are described: applications of classic PSSs, applications of new stabilizer structures based on new algorithms (including artificial intelligence), development of new methods for tuning PSSs, and operation of PSSs in PSs with high power generation by renewable sources. Describing individual papers, the research methods used by the authors (simulations, measurement methods, and a combination of both) are specified, attention is paid to the waveforms presented in the papers, and reference is made to the types of PSs in which PSSs (large multimachine, reflecting real systems, smaller standard multimachine New-England type, and simplest single-machine) operate. The tables contain detailed comments on the selected papers. The final part of the review presents general comments on the analyzed papers and guidelines for future PS stability studies. [ABSTRACT FROM AUTHOR]

Accurate short-term load forecasting (STLF) plays an essential role in sustainable energy development. Specifically, energy companies can efficiently plan and manage their generation capacity, lessening resource wastage and promoting the overall efficiency of power resource utilization. However, existing models cannot accurately capture the nonlinear features of electricity data, leading to a decline in the forecasting performance. To relieve this issue, this paper designs an innovative load forecasting method, named Prophet–CEEMDAN–ARBiLSTM, which consists of Prophet, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the residual Bidirectional Long Short-Term Memory (BiLSTM) network. Specifically, this paper firstly employs the Prophet method to learn cyclic and trend features from input data, aiming to discern the influence of these features on the short-term electricity load. Then, the paper adopts CEEMDAN to decompose the residual series and yield components with distinct modalities. In the end, this paper designs the advanced residual BiLSTM (ARBiLSTM) block as the input of the above extracted features to obtain the forecasting results. By conducting multiple experiments on the New England public dataset, it demonstrates that the Prophet–CEEMDAN–ARBiLSTM method can achieve better performance compared with the existing Prophet-based ones. [ABSTRACT FROM AUTHOR]

Retractions of coronavirus disease 2019 (COVID‐19) papers in high impact journals, such as The Lancet and the New England Journal of Medicine, have been panned as major scientific fraud in public media. The initial reaction to this news was to seek out scapegoats and blame individual authors, peer‐reviewers, editors, and journals for wrong doing. This paper suggests that scapegoating a few individuals for faulty science is a myopic approach to the more profound problem with peer‐review. Peer‐review in its current limited form cannot be expected to adequately address the scope and complexity of large interdisciplinary science research collaboration, which is central in translational research. In addition, empirical studies on the effectiveness of traditional peer‐review reveal its very real potential for bias and groupthink; as such, expectations regarding the capacity and effectiveness of the current peer review process are unrealistic. This paper proposes a new vision of peer‐review in translational science that, on the one hand, would allow for early release of a manuscript to ensure expediency, whereas also creating a forum or a collective of various experts to actively comment, scrutinize, and even build on the research under review. The aim would be to not only generate open discussion and oversight respecting the quality and limitations of the research, but also to assess the extent and the means for that knowledge to translate into social benefit. [ABSTRACT FROM AUTHOR]

The social economy is growing rapidly, and the power grid load demand is increasing. To maintain the stability of the power grid, it is crucial to achieve accurate and rapid power system stability assessment. In the actual operation of the power network, data loss is an unavoidable situation. However, most of the data‐driven models currently used assume that the input data is complete, which has obvious limitations in real‐world applications. This paper suggests an IVS‐GAN model to assess power system stability using incomplete phasor measurement unit measurement data with random loss. The proposed method combines the super‐resolution perception technology based on generative adversarial network (GAN) with a time‐series signal classification model. The generator adopts a 1D U‐Net network and uses convolutional layers to complete and recover missing data. The discriminator adopts a new gated recurrent unit–attention architecture proposed here to better extract voltage temporal variation features on key buses. The result of this paper is that the stability evaluation method outperforms other algorithms in high voltage data loss rates on the New England 10‐machine 39‐bus system. [ABSTRACT FROM AUTHOR]

This paper reflects on the process of designing and implementing an update to the Introduction to Information Technology course at Bentley University, a business university in New England. Driven by a university-wide curriculum reform initiative and following best practices from digital literacy frameworks and IS model curricula, the new CS 100 course, entitled Solving Business Problems with Information Technology, shifted emphasis from a personal computing context to exploring small business applications of technology. Students networked with university alumni at “Topics in Tech” presentations, where they learned about career options as information technology professionals. The authors describe the pilot courses that led to proposing a new CS 100 course and reflect its challenges and successes. Challenges included meeting the university’s goal of transfer friendliness, scaling from a few pilots to multiple sections, consistency of course delivery, and evaluating assessment. Successes include placing course topics in a business context, introducing current technologies, and providing opportunities for students to learn about career opportunities and experiences from university alumni. The paper also describes how CS 100 aligns with the university’s student learning goals for the new curriculum. These include developing future-focused skills, encouraging curiosity and critical analysis, communicating and collaborating in different settings, and demonstrating understanding of issues related to ethical use of technology. [ABSTRACT FROM AUTHOR]

Objectives: A bibliometric and knowledge-map analysis is used to explore hotspots' evolution and development trends in the CAR-T cell field. By looking for research hotspots and new topics, we can provide new clues and ideas for researchers in this field. Methods: The articles and reviews regarding CAR-T cells were retrieved and obtained from the Web of Science Core Collection (WOSCC) on October 28th, 2021. CtieSpace [version 5.8.R3 (64-bit)] and VOSviewer (version 1.6.17) were used to conduct the bibliometric and knowledge-map analysis. Results: 660 authors from 488 institutions in 104 countries/regions published 6,867 papers in 1,212 academic journals. The United States was absolutely in the leading position in this research field. The institution that contributed the most publications was the University of Pennsylvania. Carl H June published the most articles, while Shannon L Maude had the most co-citations. However, there was little cooperation between countries. After 2012, cooperation among various institutions was also small. The journals that published the most CAR-T cell-related papers were Frontiers in immunology and Cancers. Nevertheless, Blood and The New England Journal of Medicine were the most commonly co-cited journals. The most influential research hotspots were the research of CAR-T cells in hematological malignancies, the related research of cytokine release syndrome (CRS), CD19, and the anti-tumor activity and efficacy of CAR-T cells. The latest hotspots and topics included the study of CAR-T cells in solid tumors, universal CAR-T cells, CAR-NK cells, CD22, and anakinra (the IL-1 receptor antagonist). The research of CAR-T cells in solid tumors was a rapidly developing hot field. Emerging topics in this field mainly included the study of CAR-T cells in glioblastoma (related targets: IL13Rα2, EGFRvIII, and HER2), neuroblastoma (related target: GD2), sarcoma (related target: HER2), and pancreatic cancer (related target: mesothelin), especially glioblastoma. Conclusion: As an anti-tumor therapy with great potential and clinical application prospects, CAR-T cell therapy is still in a stage of rapid development. The related field of CAR-T cells will remain a research hotspot in the future. [ABSTRACT FROM AUTHOR]

Background: The extent of past Indigenous cultural burning in the eastern US remains contested. Historical documents (e.g. early histories, journals, and reports) contain descriptions of burning. Scholars have summarised descriptions, but few have compiled them into databases. Aims: This paper presents efforts to compile descriptions of past Indigenous burning in the eastern US and early results from mapped descriptions. Methods: Utilising previously cited descriptions and those discovered from digitised historical texts, the current dataset mapped >250 descriptions of burning in the northeastern US. Most were historical summaries from 19th century authors, and fewer were firsthand observations. Descriptions are currently shared as a GIS data layer, a tabular file, and an interactive web map. Key results: Descriptions correspond with fire-adapted vegetation, and clusters of descriptions suggest burning over large extents (e.g. southern New England, western New York). Estimated dates of burning or initial Euro-American settlement show an east–west succession in Indigenous fire exclusion and replacement with early Euro-American burning. Conclusions: Historical descriptions suggest regional-extent influence of Indigenous burning upon past forested ecosystems, but the veracity of descriptions should be carefully evaluated. Implications: This study provides a dataset for further examination of Indigenous burning and comparison with other methodologies for historical cultural fire reconstruction. This study creates a dataset of historical descriptions of Indigenous wildland burning from digitised historical texts in the eastern US. The current version of the dataset contains >250 descriptions in the northeastern US, mainly from 19th century historians. Descriptions correspond with geographic patterns in past fire-adapted vegetation. [ABSTRACT FROM AUTHOR]

This paper examines the use of manifold learning in the context of electric power system transient stability analysis. Since wide-area monitoring systems (WAMSs) introduced a big data paradigm into the power system operation, manifold learning can be seen as a means of condensing these high-dimensional data into an appropriate low-dimensional representation (i.e., embedding) which preserves as much information as possible. In this paper, we consider several embedding methods (principal component analysis (PCA) and its variants, singular value decomposition, isomap and spectral embedding, locally linear embedding (LLE) and its variants, multidimensional scaling (MDS), and others) and apply them to the dataset derived from the IEEE New England 39-bus power system transient simulations. We found that PCA with a radial basis function kernel is well suited to this type of power system data (where features are instances of three-phase phasor values). We also found that the LLE (including its variants) did not produce a good embedding with this particular kind of data. Furthermore, we found that a support vector machine, trained on top of the embedding produced by several different methods was able to detect power system disturbances from WAMS data. [ABSTRACT FROM AUTHOR]

Climatic changes in the northeastern US are expected to cause coniferous stands to transition to deciduous stands over the next hundred years. Mercury (Hg) sequestration in forest soils may change as a result. In order to understand potential effects of 5 this transition, we studied aboveground vegetation and soils at paired coniferous and deciduous stands on eight mountains in Vermont and New Hampshire, US. Organic horizons at coniferous stands accumulated more Total Hg (THg) (42±6 gha-1) than deciduous stands (30±4 gha-1). Total Hg pools in the mineral horizons were similar for coniferous (46±8 gha-1) and deciduous stands (45±7 gha-1). Soil properties (C, 10 %clay, and pH) explained 56% of the variation in mineral soil Hg concentration when multiple regressed. Foliar and bole wood Hg concentrations were generally greater for coniferous species than deciduous species. We estimated Hg mean residence time (MRT) in the organic and mineral horizons at coniferous and deciduous stands using a simple two-box model. Organic horizon MRT were longer at coniferous stands 15 (183±44 yr) than deciduous stands (65±15 yr). Mineral soil horizon MRT values were also longer for coniferous stands (386±57 yr) than for deciduous stands (188±27 yr). We concluded that organic horizon Hg accumulation is influenced by vegetation type but mineral horizons are primarily affected by soil properties. Further investigations into the effect of vegetation type on volatilization, atmospheric deposition, and leaching 20 rates are needed to constrain regional Hg cycling rates. [ABSTRACT FROM AUTHOR]

Transient stability of a power system can be significantly affected by wind power generators due to their stochastic power output and complex dynamic characteristics. This paper proposes a robust optimization approach for coordinating generation dispatch and emergency load shedding against transient instability under uncertain wind power output. The problem is modelled as a two-stage robust optimization (TSRO) model considering transient stability constraints, where the first-stage is to optimize the generation dispatch (preventive control) before a contingency and the second-stage decision is the emergency load shedding (emergency control) after the contingency occurrence under the worst case of wind power variation. To solve this TSRO problem, this paper also proposes a solution algorithm which integrates transient stability assessment and transient stability constraint construction in a column and constraint generation framework. The proposed method is validated on the New-England 39-bus system and the Nordic32 system, which shows high computational efficiency and stability robustness against uncertain wind power. [ABSTRACT FROM AUTHOR]

Much has been written on the skills and capabilities of a new generation raised with social media, smartphones, and immediate access to data and information via the Internet. Today's college students grew up using the Internet, where they both generate and consume data. But do incoming college students have the skills necessary to thrive in a digital world that requires the ability to generate, analyze, and share insights from data? This paper presents a study, performed at two small New England institutions each with a business focus, which examines the digital skill sets of first-year college students in relation to the skills they have developed before entering college. The authors also consider whether there is a "digital divide" among first-year college students in relation to their previous technology skills. When applying the Databilities framework for evaluating data literacy competencies, results show that teaching data literacy skills to first-year college students will be critical to their academic success as future information technology workers. [ABSTRACT FROM AUTHOR]

Maintaining stability in modern power systems is challenging due to complex structures, rising power demand, and load disturbances. The integration of renewable energy sources further threatens stability by causing imbalances between generation and demand. Conventional load frequency stabilization methods fall short in such scenarios. This paper proposes an optimal fractional‐order proportional‐integral‐derivative‐acceleration (FOPIDA) controller, enhanced by a robust adaptive neuro‐fuzzy inference system (ANFIS), to improve load frequency control and reliability in power systems with wind and solar generators. First, the dynamical model of a multi‐area interconnected power system, including a thermal power plant, wind turbine, and solar photovoltaic generators, is developed. A decentralized ANFIS‐FOPIDA controller is then designed for load frequency control objectives. The gains of this controller are optimized using the whale optimization algorithm (WOA), focusing on frequency deviation and tie‐line power exchange. Simulations on a New England IEEE 10‐generator 39‐bus power system demonstrate the approach's effectiveness under various disturbances, including random load‐generation disturbances and nonlinear generation behaviors. Comparisons with other strategies, such as fractional order (FO) beetle swarm optimization algorithm (FOBSOA)‐FOPIDA, WOA‐PIDA, and WOA‐ANFIS‐PIDA, and recent control approaches highlight the superior performance of the WOA‐ANFIS‐FOPIDA method in enhancing power system stability. [ABSTRACT FROM AUTHOR]

The optimal PMU placement problem is placing the minimum number of PMUs in the network to ensure complete network observability. It is an NP-complete optimization problem. PMU placement based on cost and critical nodes is solved separately in the literature. This paper proposes a novel approach, a degree of centrality in the objective function, to combine the effect of both strategies to place PMUs in the power network optimally. The contingency analysis and the effect of zero-injection buses are solved to ensure the reliability of network monitoring and attain a minimum number of PMUs. Integer linear programming is used on the IEEE 7-bus, IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus, and IEEE 118-bus systems to solve this problem. The results are evaluated based on two performance measures: the bus observability index (BOI) and the sum of redundancy index (SORI). On comparison, it is found that the proposed methodology has significantly improved results, i.e., a reduced number of PMUs and increased network overall observability (SORI). This methodology is more practical for implementation as it focuses on critical nodes. Along with improvement in the results, the limitations of existing indices are also discussed for future work. [ABSTRACT FROM AUTHOR]

Transient stability improvement of power systems in the event of short-circuit faults has always been an important issue in power systems analysis and studies. Resistive-type superconducting fault current limiters (RSFCL), owing to their capability in restricting fault currents, have been often taken into account as an efficient method to improve the transient stability of a power system. Regarding technical constraints as well as economic concerns, optimal allocation and sizing of RSFCLs in a power system play a crucial role in their efficient utilization. This paper aims to continue the authors' previous work and enhance the transient stability of power systems by proposing an optimization approach for optimal sizing and the allocation of various candidate numbers of RSFCLs, as the most employed type of SFCL and the most efficient one in transient stability improvement. To solve the optimization problem, a PSO-based algorithm is solved in MATLAB through an objective function and related constraints. The efficacy of the proposed algorithm is evaluated by numerical studies on the IEEE 39-Bus New England system in various scenarios through the assessment of critical fault clearing time (CCT) as well as the generators rotor angle deviations as two crucial criteria for the transient stability of power systems. Simulating the optimization results in DIgSILENT Power Factory indicates an evident enhancement of the power system transient stability via employing optimized RSFCLs resulted from the proposed optimization algorithm. Moreover, the level of transient stability enhancement highly depends on the number of optimized RSFCLs employed in the power system. The results of this paper present a helpful guideline for power system planners to select an appropriate stability scheme based on RSFCLs besides other related technical and economic issues. [ABSTRACT FROM AUTHOR]

In recent years, the power system transient stability assessment (TSA) based on a data-driven method has been widely studied. However, the topology and modes of operation of power systems may change frequently due to the complex time-varying characteristics of power systems. which makes it difficult for prediction models trained on stationary distributed data to meet the requirements of online applications. When a new working situation scenario causes the prediction model accuracy not to meet the requirements, the model needs to be updated in real-time. With limited storage space, model capacity, and infinite new scenarios to be updated for learning, the model updates must be sustainable and scalable. Therefore, to address this problem, this paper introduces the continual learning Sliced Cramér Preservation (SCP) algorithm to perform update operations on the model. A deep residual shrinkage network (DRSN) is selected as a classifier to construct the TSA model of SCP-DRSN at the same time. With the SCP, the model can be extended and updated just by using the new scenarios data. The updated prediction model not only complements the prediction capability for new scenarios but also retains the prediction ability under old scenarios, which can avoid frequent updates of the model. The test results on a modified New England 10-machine 39-bus system and an IEEE 118-bus system show that the proposed method in this paper can effectively update and extend the prediction model under the condition of using only new scenarios data. The coverage of the updated model for new scenarios is improving. [ABSTRACT FROM AUTHOR]

This paper offers non‐linear control for enhancing the transient stability in power systems containing a photovoltaic (PV) and doubly fed induction generator (DFIG) by transient energy function (TEF) method and finite‐time observers. At first, the TEF method is utilized to enhance the transient stability in a power system containing a PV, DFIG, and synchronous generator (SG) by constructing a hybrid Lyapunov function. Then, the derivative terms in control are estimated with finite‐time observers. The main contribution of the research is the application of the TEF technique in the transient stability evaluation of a power system consisting of PV, SG, and DFIG. Another innovation of the research is the simultaneous control of DFIG and PV, whose uncertain parameters are estimated with the use of finite‐time observer. The proposed non‐linear control scheme is robust against changes in the configuration of the power system. In order to show the capability of this control scheme, time domain simulations are performed on the IEEE 9‐bus, and the NEW ENGLAND Standard 39‐Bus power system. Using the obtained result, some comparisons are made with the back‐stepping control scheme to clarify the superiority of the proposed method. The comparisons show that the proposed non‐linear control scheme can properly provide extra damping to reduce the first‐swing fluctuations in less time. The manuscript contains the new original results of the authors' research. In this article, to maintain and improve the transient stability, a new controller with simultaneous management of photovoltaic (PV) and doubly fed induction generator (DFIG) has been developed. The contributions of the paper are as follows. (1) The transient energy function (TEF) is obtained for the power system containing synchronous generator (SG) and DFIG. (2) The control laws are acquired with four PV and DFIG control variables. (3) The derivative parameters of the PV and DFIG controls by a finite‐time observer are estimated. [ABSTRACT FROM AUTHOR]

The goal of contingency ranking is to provide a list of possible critical situations and rank them according to the severity of their impact on a power network. The methods of contingency ranking may be divided into two major categories: static and dynamic techniques. Static approaches are fast and can be implemented in real‐time, but they present different results depending on the parameter which has been selected for contingency ranking. Dynamic methodologies are more accurate than static ones, but they are time‐consuming. This paper presents a novel combined static and dynamic method for contingency ranking which takes advantage of both techniques and shows the performance evaluation for the hybrid method using accuracy and efficiency as criteria. The proposed method uses three well‐known static indices and the Lyapunov spectrum based dynamic approach to select the worst critical lines. IEEE 39‐bus (New England grid) and IEEE 118‐bus networks are utilized to verify the proposed method. The method provides speed and accuracy. One of the prominent features of the proposed approach is its ability to rank contingencies that lead to network load flow divergence. [ABSTRACT FROM AUTHOR]

We examine econometrically the real effects of paper money's introduction into colonial New England over the 1703-1749 period. Departing from earlier analyses that focus primarily on the depreciation of paper money in the region, we show that expansion of the money stock promoted growth in modern sector activity and not the other way around. We also find that bills emitted for seigniorage purposes had a positive effect on the modern sector, while bills issued through loan banks did not. [ABSTRACT FROM AUTHOR]

Transient stability of the electric power system still heavily rests on a timely and correct operation of the relay protection of individual power generators. Power swings and generator pole slips, following network short-circuit events, can initiate false relay activations, with negative repercussions for the overall system stability. This paper will examine the generator's underimpedance (21G) and out-of-step (78) protection functions and will propose a machine learning based classifier for supporting and reinforcing their decision-making logic. The classifier, based on a support vector machine, will aid in blocking the underimpedance protection during stable generator swings. It will also enable faster tripping of the out-of-step protection for unstable generator swings. Both protection functions will feature polygonal protection characteristics. Their implementation will be based on European practice and IEC standards. Classifier will be trained and tested on the data derived from simulations of the IEEE New England 10-generator benchmark power system. [ABSTRACT FROM AUTHOR]

In this paper we propose a novel distributed continuous-time algorithm aimed to solve optimization problems with cost function being a sum of local strictly convex multidimensional functions associated to individual agents. Additionally, the problems can have coupled equality and inequality constraints. We prove global asymptotic convergence of the algorithm for a connected graph topology. In order to investigate its practical implementation, we analyze convergence when Euler method is applied to represent discrete-time communication. Finally, we support our results with numerical experiments of the developed approach application for power balancing in New England power system. [ABSTRACT FROM AUTHOR]

The increasing interactivity between energy carrier networks such as electrical, natural gas (NG), and heating can be appropriately used for improving the operation optimality along with overcoming some challenges of those systems. With such a view, this paper studies the practical issue of power transmission lines congestion management in integrated multicarrier energy systems (MCESs) and makes a comparison with the only electrical operation mode. The MCES consisting of electrical and NG network infrastructures with exchanged energy between them is modelled by considering the several local energy hubs and their electrical and heat loads. Rescheduling of energy sources (power plants and gas wells) and demand side management in both electrical and NG networks have been utilized as the two important tools in the developed congestion alleviation framework. The proposed method confirms that the provided flexibility in the integrated operation of the MCESs can lead to a remarkable reduction in the congestion management cost index. Implementing the proposed congestion management framework on an assumed test MCES with a combined 39‐bus New England power system and 20‐node Belgium NG system demonstrates its effectiveness and applicability for the system operators. The issue of power transmission lines congestion management in integrated multi‐carrier energy systems (MCESs) has been studied. The proposed method confirms that the provided flexibility in the integrated operation of the MCESs can lead to reduction in the congestion management cost. [ABSTRACT FROM AUTHOR]

Addressing the issue of reduced system inertia and significantly increased risk of system frequency deviation due to high penetration of renewable energy sources, this paper proposes a power system disturbance frequency trajectory prediction method based on light gradient boosting machine (LightGBM) Spearman to provide data support for advanced system stability judgment and the initiation of stability control measures. Firstly, the optimal cluster is determined by combining the K-means clustering algorithm with the elbow method to eliminate redundant electrical quantities. Subsequently, the Spearman coefficient is used to analyze feature correlation and filter out electrical quantities that are strongly correlated with frequency stability. Finally, a frequency trajectory prediction model is built based on LightGBM to achieve accurate prediction of disturbed frequency trajectories. The method is validated using a case study on the New England 10-machine 39-bus system constructed on the CloudPSS 4.0 full electromagnetic cloud simulation platform, and the results show that the proposed method has high accuracy in frequency trajectory prediction. [ABSTRACT FROM AUTHOR]

Increasing the replacement of conventional synchronous machines by non-synchronous renewable machines reduces the conventional synchronous generator (SG) inertia in the modern network. Synthetic inertia (SI) control topologies to provide frequency support are becoming a new frequency control tactic in new networks. However, the participation of SI in the market of RES-rich networks to provide instant frequency support when required proposes an increase in the overall marginal operation cost of contemporary networks. Consequently, depreciation of operation costs by optimizing the required SI in the network is inevitable. Therefore, this paper proposes a flexible SI optimization method. The algorithm developed in the proposed method minimizes the operation cost of the network by giving flexible SI at a given SG inertia and different sizes of contingency events. The proposed method uses Box's evolutionary optimizer with a self-tuning capability of the SI control parameters. The proposed method is validated using the modified New England 39-bus network. The results show that provided SIs support the available SG inertia to reduce the RoCoF values and maintain them within acceptable limits to increase the network's resilience. [ABSTRACT FROM AUTHOR]

Severe disturbances in a power network can cause the system frequency to exceed the safe operating range. As the last defensive line for system emergency control, under frequency load shedding (UFLS) is an important method for preventing a wide range of frequency excursions. This paper proposes a hierarchical UFLS scheme of "centralized real-time decision-making and decentralized real-time control" for inter-connected systems. The centralized decision-layer of the scheme takes into account the importance of the load based on the equivalent transformation of kinetic energy (KE) and potential energy (PE) in the transient energy function (TEF), while the load PE is used to determine the load shedding amount (LSA) allocation in different loads after faults in real-time. At the same time, the influence of inertia loss is considered in the calculation of unbalanced power, and the decentralized control center is used to implement the one-stage UFLS process to compensate for the unbalanced power. Simulations are carried out on the modified New England 10-generator 39-bus system and 197-bus system in China to verify the performance of the proposed scheme. The results show that, compared with other LSA allocation indicators, the proposed allocation indicators can achieve better fnadir and td. At the same time, compared with other multi-stage UFLS schemes, the proposed scheme can obtain the maximum fnadir with a smaller LSA in scenarios with high renewable energy sources (RES) penetration. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including statistical or technical research, analysis on topics of current interest concerning the economy, and research on economic and policy issues of concern to New England.

Renewables, such as solar and wind generation, combined with storage are becoming a key part of modern grid. This paper develops probabilistic tools for analysis of grid reliability with such variable generation resources. The developed tools improve speed and accuracy of the reliability analysis compared to usual Monte Carlo methods. This is achieved by using an extension of well known convolution method applicable to interdependent variables. The interdependent distributions are obtained from historical data using Machine Learning of quantile models. The paper presents a novel approach to the analysis of reliability contribution of storage based on these models and related to Information Theory. The developed tools are demonstrated in several example scenarios for ISO-New England service area. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to obtain dynamic versions of static distribution factors, such as power-transfer, line-outage, and outage-transfer distribution factors. With the proposed dynamic distribution factors (DDFs), one can predict line flows over the post-contingency transient period with the same computational effort as obtaining static distribution factors. Our development centers on deriving closed-form expressions that approximate generator outputs through the post-contingency transient period with a reduced-order aggregate dynamical model to recover dynamic generator participation factors. The full suite of DDFs can then be derived by combining these dynamic generator participation factors with injection shift factors, i.e., static linear sensitivities of line active-power flows with respect to nodal active-power injections, computed at the pre-disturbance steady-state operating point. We illustrate the accuracy and computational benefits of the proposed DDFs via numerical case studies involving the New England test system. [ABSTRACT FROM AUTHOR]

Conventional power systems function under the assumption that loads are uncontrollable, and that the generation control is the primary means of preserving system voltage, frequency, and stability. Thanks to recent advancements of power electronics technology and communication schemes, demand‐side resources are now capable of providing fast frequency regulation. Controllable loads can provide upward/downward reserve during frequency excursions. In this paper, the authors consider collective contribution of large clusters of controllable loads which modulate their aggregate demand power to regulate the primary frequency. Koopman model predictive control is designed to handle local frequency variations caused by various disturbances at each load bus, considering uncertain load models. The efficacy of the proposed method has been validated using the New‐England power system considering two scenarios, namely, load variation, and generation outage. [ABSTRACT FROM AUTHOR]

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Power system security assessment is one of the most important aspects of wind integrated power system due to intermittent and stochastic nature of wind. This paper proposes a nonparametric method for security risk assessment. Parzen window density estimation is utilized to obtain probability density functions for wind power, generation and load profiles. Correlation between independent and dependent samples of load, wind power and generation is replicated by a spatial transformation of probability distributions of random variables using Cholesky decomposition. Probabilistic load flow is performed for determining the branch flows and bus voltages of the power system for N − 1 contingency analysis. Risk assessment is performed by quantifying the probability and severity indices for branch flows and bus voltages. The proposed method is tested on modified New England 39-bus system and extended for simplified power grid of Shandong province, China. Simulation results and comparisons demonstrate the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

This paper develops a systematic framework for analyzing how low frequency forced oscillations propagate in electric power systems. Using this framework, the paper shows how to mathematically justify the so-called Dissipating Energy Flow (DEF) forced oscillation source location technique. The DEF's specific deficiencies are pinpointed, and its underlying energy function is analyzed via incremental passivity theory. This analysis is then used to prove that there exists no passivity transformation (i.e. quadratic energy function) which can simultaneously render all components of a lossy classical power system passive. The paper goes on to develop a simulation-free algorithm for predicting the performance of the DEF method in a generalized power system, and it analyzes the passivity of three non-classical load and generation components. The proposed propagation framework and performance algorithm are both tested and illustrated on the IEEE 39-bus New England system and the WECC 179-bus system. [ABSTRACT FROM AUTHOR]

This paper proposes a new coordinated method for preventive generation rescheduling and corrective load shedding to maintain power system transient stability under uncertain wind power variation. A two-step bi-level optimization model is proposed where generation rescheduling and load shedding are coordinated by a risk coordination parameter, which adjusts the total coordination cost in the upper level. Then, based on Extended Equal Area Criterion (EEAC) and trajectory sensitivities, the non-linear risk and stability constraints are converted into the linear form for generation rescheduling cost and load shedding cost optimization in the lower level, respectively. Uncertain wind power output is modeled as a small number of robust test scenarios. Finally, the golden section search is applied to solve the bi-level problem. The proposed method is validated on the New-England 39-bus system by using commercial grade software. The computational efficiency, economic optimality, and stability robustness under random wind power of the method are demonstrated. [ABSTRACT FROM AUTHOR]

Based on the knowledge accumulated off-line and feature extractions, a novel data-based learning and control method is proposed for the long-term voltage stability problem in this paper. All the spatial-temporal data is considered and the features of different emergency events are extracted by principle component analysis which can reduce the dimension and reveal the significant internal structure of the data. An artificial neural network is used to build a classifier to reinforce the relationship directly between the system dynamics and optimal control actions. With the prepared control knowledge, it is faster to find an optimal control action online with a good system performance. Simulation results on the 6-bus system, New England 39-bus system and Iceland 189-bus system are given to show the potential of this method for on-line control. [ABSTRACT FROM AUTHOR]

The increasing power/load demand is becoming a new concern for the grid operators as it can potentially overload transmission lines. Among advantages of a multi-terminal dc (MTDC) grid, its high controlability makes it a promising asset for ac line overload alleviation. This paper proposes a model predictive control based strategy, which regularly updates the power setpoints of the MTDC converter stations and ac generators to alleviate ac line overloads. To compute and update the power setpoints, necessary measurements are received by the controller within regular time intervals. The proposed controller ensures that subsequent to any ac line overload, changing the power setpoints of the MTDC converter station does not cause any operation violation in the MTDC grid. Furthermore, it ensures that the ac voltage limits and voltage stability criteria are met to avoid voltage instability. Performance and effectiveness of the proposed controller are evaluated by simulation studies on two systems, i.e., the 39-bus New England system integrated with a 5-bus MTDC system and the IEEE 118-bus system connected to a 6-bus MTDC system. Simulation results confirm that the MTDC grid can play a major role in controlling active powers of most of the ac transmission lines. [ABSTRACT FROM AUTHOR]

Short-term probabilistic load forecasting is essential to power systems management and optimization of power flows across transmission networks. Developing forecasting tools capable of providing accurate predictions must comply with their practical implementation in short-term operations, mainly in terms of fast computing and high efficiency. Many data-driven forecasting solutions are often unnecessarily verbose, thus making their practical value limited. This problem occurs more frequently when multiple loads have to be predicted simultaneously, as in power transmission system analysis and optimization. In this paper, we propose a new cooperative forecasting system that refines probabilistic forecasts of individual loads online. The refining procedure is based on a multivariate quantile regression, which is dynamically applied to the individual forecasts as new observations become available. The proposal is validated on the load data published by ISO New England for eight regions, covering six states of the United States. The quality of probabilistic forecasts is assessed in terms of reliability and sharpness, comparing the results to three benchmarks. The proposed method outperforms the best benchmark by up to 6% w.r.t. the reduction in pinball loss. [ABSTRACT FROM AUTHOR]

The growing share of renewable energy has an increasingly significant impact on the electricity market. Locational marginal price (LMP), as a sensitive price signal, has a potential but considerable correlation with renewable energy generation. In this context, this paper focuses on the joint fluctuation laws between LMP and renewables consumed to generate electricity. At first, LMP and renewables consumption data from Independent System Operator New England (ISO‐NE) are collected and preprocessed. Then, through highly comparative time‐series analysis (hctsa), it is found that discrete symbolization features have effective description ability. Therefore, a coarse‐grained method is conducted to convert the real matrix data into a symbol vector based on numerical distribution analysis. Next, joint fluctuation complex networks are constructed according to the symbol vector. From a global perspective, the joint fluctuation network follows an exponential distribution and exhibits hierarchical modularity. From a local perspective, the joint fluctuation network follows a power‐law distribution. The research results of this paper provide a new perspective to understand the evolution of the electricity market with large‐scale renewable energy and have reference value for identifying the valuable joint fluctuation patterns and improving the LMP forecast results. [ABSTRACT FROM AUTHOR]

The letter to the editor responds to a recent paper by Armstrong et al. that discussed pre-colonial land use by Indigenous peoples in northwestern British Columbia. The authors of the letter reject the characterization of their own scholarship as racist and argue that Armstrong et al. misrepresented their findings. They emphasize that Indigenous peoples in southern New England did have ecological impacts on the landscape, but these impacts were primarily local in scale. The authors also highlight the importance of understanding the scale and variability of Indigenous peoples' ecological impacts for conservation and land management strategies. [Extracted from the article]

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Security issues are always considered in systems with wireless networks. However, few of them investigated covert signals existing on different communication channels to confuse advisories. In this paper, we consider the cooperation between two energy-constrained agents, who could inject covert signals. First, the system performance is measured by Kullback–Leibler divergence (KLD) to avoid much deviation. Then, the cooperative game between two agents is considered, in which two agents share the common goal at confusing advisories. More formally, this cooperative game is formulated as a Markov decision process (MDP) and the most economic strategies are obtained through reinforcement learning (RL) under the imperfect information. Finally, the feasibility of theoretical results is demonstrated on the interconnected New England test system (NETS) as well as its reduced system. [ABSTRACT FROM AUTHOR]

This paper presents a QV-based approach called Critical Voltage-Reactive Power Ratio (CVQR) index to assess the voltage instability tendencies of power system buses with increase in renewable energy (RE) penetration within the power system. The buses are thus ranked according to the order in which they are impacted by increase in renewable energy penetration. Simulations were performed using DIgSILENT PowerFactory and result analyses were done with MATLAB. The developed CVQR index has been employed to assess the effect of increasing RE generation on grid voltage stability. This index provides information on the voltage instability tendencies of all non-slack buses of the RE-integrated grid and the buses are ranked from the weakest to the strongest. The rankings obtained from the developed CVQR has been compared with five commonly-used indices and the result of the comparison verifies the accuracy of the proposed index. IEEE 14-bus and IEEE 39-bus New England systems have been used to evaluate the proposed CVQR index and various scenarios of RE system combinations and placements have been considered. Voltage collapse condition is indicated whenever the CVQR index associated with any bus becomes positive (CVQR > 0). This index can as well be applied to other power system networks. The overall ranking of the buses based on the CVQR index can provide insights on the most appropriate location for large inductive loads or compensating devices, which can either absorb or inject reactive power into the power system, thereby influencing the system's voltage stability. [ABSTRACT FROM AUTHOR]

This paper proposes modified Karhunen–Loeve transform with total least square estimation of signal parameters using rotational in-variance technique (MKLT-TLS-ESPRIT) to approximate the low-frequency oscillatory modes. MKLT decreases the impact of highly correlated additive colored Gaussian noise (ACGN) from the signal by differentiating the correlation matrix w.r.t from the final time instance. A quantitative study of the suggested method with other estimation methods is used to evaluate the effectiveness of the proposed method. Monte Carlo simulations with 50,000 runs are conducted to test the robustness of the estimation scheme for MKLT-TLS-ESPRIT. The evaluation of the efficiency of the proposed method in real-time perspective, the two-area system, and New England sixty-eight bus test system has been considered. The analysis shows that the suggested methodology correctly measures the interarea modes and lowers their mean and standard deviation to a minimum value. [ABSTRACT FROM AUTHOR]

Power system congestion is a major problem that the system operator (SO) would face in the post-deregulated era. Therefore, investigation of techniques for congestion-free wheeling of power is of paramount interest. One of the most practiced and an obvious technique of congestion management is rescheduling the power outputs of generators in the system. However, all generators in the system need not take part in congestion management. Development of sound formulation and appropriate solution technique for this problem is aimed in this paper. Contributions made in the present paper are twofold. Firstly a technique for optimum selection of participating generators has been introduced using generator sensitivities to the power flow on congested lines. Secondly this paper proposes an algorithm based on particle swarm optimization (PSO) which minimizes the deviations of rescheduled values of generator power outputs from scheduled levels. The PSO algorithm, reported in this paper, handles the binding constraints by a technique different from the traditional penalty function method. The effectiveness of the proposed methodology has been analyzed on IEEE 30-bus and 118-bus systems and the 39-bus New England system. [ABSTRACT FROM AUTHOR]

The automatic generation control (AGC) system is temporally situated between economic dispatch and synchronous generator dynamics, and its primary role is to regulate frequency within and tie-line flows between control areas. Given appropriate choice of participation factors (feed-forward controller gains that govern the disaggregation of the area-level power requirement to individual generators), the AGC can be engineered to nudge system dynamics toward a steady-state operating point corresponding to economic optimality. This paper establishes necessary and sufficient conditions under which a widely accepted choice of participation factors guarantees the alignment of steady-state synchronous generator outputs with a global minimum of a prototypical economic dispatch problem. In so doing, it resolves several ambiguities and formalizes technical assumptions governing the role of the standard AGC architecture in the context of economic dispatch and steady-state operation. Numerical case studies tailored to a modified version of the New England 39-bus 10-machine test system validate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the problem of improving the stability of the power system with DFIG and in the presence of SSSC using nonlinear method is discussed. The nonlinear controller is designed by the multi-input backstepping method with a sliding mode observer. This controller is applied simultaneously to the excitation system of synchronous generators and the rotor side converter in DFIG and sssc in a way that improves the stability of the power system compared to the linear and nonlinear methods described in this paper. The control coefficient matrices are adjusted using intelligent algorithms in such a way that the stability of the system is more optimized. Practical constraints on the system are considered in selecting the control inputs. The designed controller is robust to changes in the operating point and the location of the disturbance. The pei'formance of the designed controller in a 39-bus, 10 macliines NEW ENGLAND network including DFIG generators and in the presence of sssc is simulated and investigated using MATLAB software. [ABSTRACT FROM AUTHOR]

Natural calamities that include heavy storms, earthquakes, snowfall, and drought/heat waves are now predominant over the world. These natural calamities cause high impact low frequency (HILF) events, such as multi-location faults and multi-area stressed conditions, that may cause large blackouts and reduce system resilience. This paper proposes a system integrity protection scheme (SIPS) to accurately identify the multi-location faults and multi-area stressed system conditions. The proposed SIPS accurately detects the multi-location faults for synchronized operation of distance relay and simultaneously provides backup distance relay blocking in higher operating zones, during stressed system conditions. For the entire operation, data from WAMS are used to first detect the abnormal operating areas followed by identification of vulnerable buses in the system. Further, by comparing the phase angles of transmission lines connected to vulnerable buses, faulted lines are identified. The effectiveness of the method is tested for multiple scenarios carried out on the WSCC 9-bus system and New England 39-bus system in MATLAB/Simulink platform. For practical implementation, the performance is validated on a 4-bus test network on Typhoon hardware in loop (HIL) system. Adequate and synchronized relay operation results in the augmentation of resiliency in the power system network. Calculated resistance indices reflect increase in transmission capacity up to 140%, load loss reduction percent up to 0%, and improvement in the active power deficiency up to 57% during different operating conditions for considered test systems. [ABSTRACT FROM AUTHOR]

With the intensive commissioning of high voltage direct current, transient voltage problems have become increasingly prominent, which seriously threatens the safe and stable operation of the power system. On the basis of cascaded CatBoost (CasCatBoost) and sparrow search algorithm (SSA), a novel temporal‐adaptive data‐driven method for short‐term voltage stability (STVS) assessment is proposed in this paper. First, normalized mutual information feature selection is employed for important feature selection to reduce the computational burden. Taking the important features as inputs, the CasCatBoost model is constructed to mine STVS informatization. On the basis of CatBoost and cascaded structure, CasCatBoost can enhance the assessment accuracy without sacrificing the assessment earliness. Furthermore, the SSA is embedded into the offline phases of CasCatBoost to determine the optimal confidence threshold. Extensive numerical tests are conducted on the improved New England 39‐bus system, and the simulation results demonstrate that the proposed method outperforms other representative machine learning algorithms and exhibits excellent reliability, rapidity, and applicability. [ABSTRACT FROM AUTHOR]

Relying on a cultural studies approach to literary texts, this paper analyzes Jhumpa Lahiri's recent embrace of the autobiographical form as a radical change, associated with a modernist strategy of innovation. The central theme of the author's fictional work is the process of self-understanding experienced mainly by second-generation members of the Bengali community in New England. This paper starts from the premise that, as well as her characters, the author follows a transcultural path to redefinition via her relocation to a different space (Italy). This argument investigates the significance of Henri Matisse's visual universe in relation to Lahiri's transcultural metamorphosis. [ABSTRACT FROM AUTHOR]

This paper proposes a fractional-order sliding mode controller (FOSMC) for fixed-time stability of multimachine power systems. Fractional calculus has been applied in sliding mode control (SMC) to design FOSMC which can better suppress the chattering phenomenon than the traditional integer-order SMC. Hence, a FOSMC is proposed to regulate the excitation of synchronous generators to improve the transient stability of the power system. In addition, the FOSMC can maintain the multimachine power systems stabilization in an upper bound and measurable convergence time independent of the initial operating conditions of the power system. The Lyapunov function is implemented to demonstrate the stability of multimachine power systems under the FOSMC, and the upper bound convergence time of the power system is generally estimated. The New England 10-machine 39-bus power system is taken into simulation to test the effectiveness and superior performances of the FOSMC than the existing control methods under different operating conditions. Meanwhile, the FOSMC with different orders are presented to verify that the FOSMC can better restrain the chattering phenomenon than traditional integer-order SMC. Simulation results show that the FOSMC can effectively reduce the chattering phenomenon and ensure the power system stabilization in an upper bound time, thus enhancing the dynamic performances of the power system. [ABSTRACT FROM AUTHOR]