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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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

Copyright of Ingenius, Revista Ciencia y Tecnología is the property of Universidad Politecnica Salesiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

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]

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]

Evaluating the rated MVA sizes of the Circuit Breaker (CB) requires calculating reactance parameters from fault analysis and synchronizing these parameters with the reference re-striking voltage. In this paper two ways are used in controlling X/R ratio of the system to reduce CB size in order to save cost on system configuration. PSS/E program was introduced to facilitate and illustrate the solution. X/R ratio and subtransient reactance were calculated to provide an approach to assess the current and duty interruption of generator buses with contributions from all other buses. The effect of reduction factors and NACD for the symmetrical component of the short circuit current is also applied in our proposed method. The obtained results proved that applying the reduction factor technique in New England system reduces the rated interruptions of C.Bs, and CB rate is also minimized in Bismayah132kV power plant from Iraqi grid utilizing effective switching which economizes choosing CB. It is concluded that the two techniques can be used successfully for designing CB ratings with a minimum cost. [ABSTRACT FROM AUTHOR]

Power systems are becoming more and more complicated. Traditional simulation tools such as transient stability programs and electromagnetic transients (EMT) programs are unable to meet the accuracy or efficiency requirements. This paper proposes a shifted frequency‐based EMT (SFEMT) simulation in the symmetrical component‐domain (abbreviated as SCSFEMT simulation) for the three‐phase transmission system. The sequence‐decoupled SCSFEMT modeling for the power system components is first presented based on the symmetrical component transformation. Then, the solution procedure of the presented SCSFEMT simulation algorithm is elaborated in detail based on the EMTP‐type solution and multi‐sequence Thevenin equivalence (MSTE). With the MSTE, the three sequence networks can be decoupled during the whole simulation. Thus, the dimension of the system is greatly reduced. Last, the accuracy and efficiency of the SCSFEMT simulation are validated on the 10‐machine 39‐bus New England system by comparing it with traditional EMT and SFEMT simulation. Test results show that the SCSFEMT simulation algorithm is much more efficient than traditional SFEMT simulation. [ABSTRACT FROM AUTHOR]

Lyme disease (LD) is the most common vector-borne disease in the United States (U.S.). This paper assesses how climate change may influence LD incidence in the eastern and upper Midwestern U.S. and the associated economic burden. We estimated future Ixodes scapularis habitat suitability and LD incidence with a by-degree approach using variables from an ensemble of multiple climate models. We then applied estimates for present-day and projected habitat suitability for I. scapularis, present-day presence of Borrelia burgdorferi, and projected climatological variables to model reported LD incidence at the county level among adults, children, and the total population. Finally, we applied an estimate of healthcare expenses to project economic impacts. We show an overall increase in LD cases with regional variation. We estimate an increase in incidence in New England and the upper Midwestern U.S. and a concurrent decrease in incidence in Virginia and North Carolina. At 3°C of national warming from the 1986–2015 baseline climate, we project approximately 55,000 LD cases, a 38-percent increase from present-day estimates. At 6°C of warming, our most extreme scenario, we project approximately 92,000 LD cases in the region, an increase of 145 percent relative to current levels. Annual LD-related healthcare expenses at 3°C of warming are estimated to be $236 million (2021 dollars), approximately 38 percent greater than present-day. These results may inform decision-makers tasked with addressing climate risks, the public, and healthcare professionals preparing for treatment and prevention of LD. [ABSTRACT FROM AUTHOR]

Three static techniques (i.e. Power flow, Continuation Power Flow (CPF) and the Q–V curve) are used to assess the voltage stability of the power grid with a Solar Photovoltaic Generator (SPVG) and FACTS devices under nominal and heavy loading conditions. A static model is proposed for the power system that includes conventional power generation units and SPVGs with FACTS devices. Two models of SPVG were used (i.e., PV-model and PQ-model) to elucidate the effect of the SPVGs on the stability of the voltage under various operating conditions. The best location for FACTS devices was obtained under nominal and heavy load conditions using static techniques. A comparison between series and shunt FACTS devices under nominal and heavy loading conditions was carried out using the three static techniques. The interaction between SPVGs and FACTS devices was detailed in this paper. The proposed approach was tested on the New England 39-bus standard test system, and the results confirmed the effectiveness of the proposed approach under various operating conditions. [ABSTRACT FROM AUTHOR]

Transient stability (TS) and short-term voltage stability (STVS) assessment are of fundamental importance for the operation security of power systems. Both phenomena can be mutually influenced in weak power systems due to the proliferation of power electronic interface devices and the phase-out of conventional heavy machines (e.g., thermal power plants). There is little research on the assessment of both types of stability together, despite the fact that they develop over the same short-term period, and that they can have a major influence on the overall transient performance driven by large electrical disturbances (e.g., short circuits). This work addresses this open research challenge by proposing a methodology for the joint assessment of TS and STVS. The methodology aims at estimating the resulting short-term stability state (STSS) in stable, or unstable conditions, following critical events, such as the synchronism loss of synchronous generators (SG) or the stalling of induction motors (IM). The estimations capture the mechanisms responsible for the degradations of TS and STVS, respectively. The paper overviews the off-line design of the data-driven STSS classification methodology, which supports the design and training of a hybrid deep neural network RCNN (recurrent convolutional neural network). The RCNN can automatically capture spatial and temporal features from the power system through a time series of selected physical variables, which results in a high estimation degree for STSS in real-time applications. The methodology is tested on the New England 39-bus system, where the results demonstrate the superiority of the proposed methodology over other traditional and deep learning-based methodologies. For reference purposes, the numerical tests also illustrate the classification performance in special situations, when the training is performed by exclusively using measurements from generation and motor load buses, which constitute locations where the investigated stability can be observed. [ABSTRACT FROM AUTHOR]

In this paper, an authentic near-real-time methodology is proposed for locating the sources of FOs using the fundamental frequency of the periodic external driving force based on the direction of the potential energies exchanged between the synchronous generators. First, A directed graph of the network reduced to the generator buses is defined in which the weight and direction of its branches are assigned based on a well-defined energy-driven function. Next, based on the cosine part appearing in the edge weights, subsequent decisions are made, depending on the generators' angular velocity and active power phases. The proposed algorithm can make a correct and swift diagnosis even under the most challenging operational circumstances, such as resonances and limit cycles. The methodological procedure has been validated on the WECC 3-machine 9-bus and New England 10-machine 39-bus benchmark power systems modeled in the Real-Time Digital Simulator and then analyzed in the MATLAB environment. The simulation results revealed that the presented approach reliably releases all different FOs sources with various kinds of origin. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) has a significant role in reliable operation and efficient scheduling of power systems. However, it is still a major challenge to accurately predict power load due to social and natural factors, such as temperature, humidity, holidays and weekends, etc. Therefore, it is very important for the efficient feature selection and extraction of input data to improve the accuracy of STLF. In this paper, a novel hybrid model based on empirical mode decomposition (EMD), a one-dimensional convolutional neural network (1D-CNN), a temporal convolutional network (TCN), a self-attention mechanism (SAM), and a long short-term memory network (LSTM) is proposed to fully decompose the input data and mine the in-depth features to improve the accuracy of load forecasting. Firstly, the original load sequence was decomposed into a number of sub-series by the EMD, and the Pearson correlation coefficient method (PCC) was applied for analyzing the correlation between the sub-series with the original load data. Secondly, to achieve the relationships between load series and external factors during an hour scale and the correlations among these data points, a strategy based on the 1D-CNN and TCN is proposed to comprehensively refine the feature extraction. The SAM was introduced to further enhance the key feature information. Finally, the feature matrix was fed into the long short-term memory (LSTM) for STLF. According to experimental results employing the North American New England Control Area (ISO-NE-CA) dataset, the proposed model is more accurate than 1D-CNN, LSTM, TCN, 1D-CNN–LSTM, and TCN–LSTM models. The proposed model outperforms the 1D-CNN, LSTM, TCN, 1D-CNN–LSTM, and TCN–LSTM by 21.88%, 51.62%, 36.44%, 42.75%, 16.67% and 40.48%, respectively, in terms of the mean absolute percentage error. [ABSTRACT FROM AUTHOR]

Transient stability is one of the major features of power systems operation which can be also considered an objective function in the flexible AC transmission systems (FACTS) allocation problem. This paper introduces a general multi‐level multi‐objective optimisation framework and its application on optimising the size and location of the improved hybrid flow controller (IHFC). IHFC is a new member of the FACTS family whose ability to control power flow and improve power system stability has been investigated. The optimisation problem considers economic and stability‐based objective functions simultaneously, and the allocation problem is formulated as a non‐linear programming (NLP) problem. The New England 39‐bus system has been used as a case study to demonstrate that the proposed framework can provide efficient and economically viable solutions to the allocation problem. [ABSTRACT FROM AUTHOR]

This paper aims to improve transient stability using the Ant Colony Optimization for Continuous Domains (ACOR). This improvement is obtained by solving the Transient Stability Constrained Optimal Power Flow (TSCOPF) problem and extracting the sensitivity coefficients. The presented costs minimization approach requires less execution time to manage energy resources efficiently and compared to other conventional methods, it also outperforms based on statistical indicators such as mean and standard deviation. Furthermore, the sensitivity analysis of the suggested method considers the active power generation of the generating units along with their terminal voltage, which notably decreases the operation cost and reduces risk to the power system. On the other hand, the application of ACOR algorithm can reduce the fuel cost of power system operation to 60,928.36 $/h with a decrease of 15.33 $/h compared to the conventional method with a standard deviation equal to 2.51 $/h and an execution time of 7.66 s. This strategy could be used in power system dispatching. This method is implemented on the New England 39 bus system, and the results demonstrate the method's efficiency compared to other conventional methods. [ABSTRACT FROM AUTHOR]

Due to the expansion of the decarbonization in modern power systems, particularly the high integration of renewable energy resources, more and more uncertainties and variabilities are brought to electricity systems. These uncertainties change the system operating conditions and face the power system with unknown issues. The secure operation of modern power systems requires developing security assessment techniques that effectively mitigate the variation of the operating conditions. This paper proposes a novel continuous learning scheme for online static security assessment (SSA) that enables the periodic renovation of the assessment model. The scheme consists of a Mondrian forest (MF)‐based training model and database generation approach conducting characteristics of operating conditions. The employed security index is a new weather‐depended performance index whose ambient temperature can affect line ratings. After the new training set is generated in the update procedure, the MF algorithm is updated using this data. Eventually, the SSA model is enriched with current circumstances in the power system. The proposed scheme is examined using real‐world data mapped to the New England 39‐bus and 118‐bus standard test systems. The effectiveness and superior performance of the proposed method are demonstrated using various criteria and measures. [ABSTRACT FROM AUTHOR]

Globally,wind power plants (WPPs) installations are dramatically increasing, leading to a large-scale integration into the power grids. Huge endeavors are devoted to achieving an efficient operation of WPPs. This paper offers a novel chaotic-billiards optimizer (C-BO) algorithm to properly design the fuzzy logic control (FLC) strategy for stability enhancement of grid-integrated WPPs. The C-BO algorithm has distinct features such as simple construction, low numbers of parameters required to design, high convergence speed, and low computational burden. The permanent-magnet synchronous generator (PMSG)-based WPP is interlinked into the electric power grid through power converters. A cascaded FLC approach, which is optimally fine-tuned by the C-BO, is offered as the control methodology for these converters. The C-BO algorithm fine-tunes the gain factors of multiple FLCs at a rapid convergence speed. A simulation-based optimization approach is applied, in which the integrated square error criterion is chosen as an objective function. For the sake of preciseness, the PMSG-based WPP is connected to the IEEE 39-bus New England test system. The effectiveness of the optimal FLC using the C-BO algorithm is compared with that achieved using the water cycle algorithm-based optimal FLC, the genetic algorithm-based optimal FLC, and the marine predator algorithm-based optimal proportional-integral controller, taking into account severe grid disturbances. Moreover, real wind speed measured data captured from Zaafarana Station in Egypt are extensively performed in the system studies. The validity of the offered control approach is widely verified by the simulation analyses using the MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of frequency stability prediction (FSP) following active power disturbances in power systems by proposing a vision transformer (ViT) method that predicts frequency stability in real time. The core idea of the FSP approach employing the ViT is to use the time-series data of power system operations as ViT inputs to perform FSP accurately and quickly so that operators can decide frequency control actions, minimizing the losses caused by incidents. Additionally, due to the high-dimensional and redundant input data of the power system and the O(N2) computational complexity of the transformer, feature selection based on copula entropy (CE) is used to construct image-like data with fixed dimensions from power system operation data and remove redundant information. Moreover, no previous FSP study has taken safety margins into consideration, which may threaten the secure operation of power systems. Therefore, a frequency security index (FSI) is used to form the sample labels, which are categorized as "insecurity", "relative security", and "absolute security". Finally, various case studies are carried out on a modified New England 39-bus system and a modified ACTIVSg500 system for projected 0% to 40% nonsynchronous system penetration levels. The simulation results demonstrate that the proposed method achieves state-of-the-art (SOTA) performance on normal, noisy, and incomplete datasets in comparison with eight machine-learning methods. [ABSTRACT FROM AUTHOR]

A Novel Practical Stochastic Pricing Model for Multi-Interval Real-Time Markets Practical implementations of economic dispatch with associated pricing systems are crucial for operating electricity markets. Because of the high volatility caused by the increasing integration of renewable energy, consideration of the underlying stochastic problem is becoming more important than ever. It is a challenge to incorporate the uncertain nature of real-time operations into an already complex multi-interval dynamic problem with intertemporal constraints. Because solving a standard multi-stage stochastic programming problem is too burdensome in terms of calculation time for real-time markets, it has been standard practice in electricity markets to use a deterministic approximation with varying degrees of look-ahead. Cho and Papavasiliou, in their article "Pricing Under Uncertainty in Multi-Interval Real-Time Markets", introduced a practical alternative method for pricing under uncertainty in multi-interval real-time markets. Using slightly different stochastic formulations, these authors propose an approach that preserves the attractive features from both the deterministic formulation (simpler calculation) and the standard stochastic formulation (better performance). Recent research has demonstrated that real-time auctions can generate the need for side payments, even if the market clearing models are convex, because of the rolling nature of real-time market clearing. This observation has inspired proposals for modifying the real-time market-clearing model in order to account for binding past decisions. We extend this analysis in order to account for uncertainty by proposing a real-time market-clearing model with look-ahead and an endogenous representation of uncertainty. We define two different types of expected lost opportunity cost as performance metrics. Our market-clearing model provides the price signal minimizing one of these metrics using the Stochastic Gradient Descent algorithm. We present results from a case study of the ISO New England system under a scenario of significant renewable energy penetration while accounting for ramp rates, storage, and transmission constraints. History: This paper has been accepted for the Operations Research Special Issue on Computational Advances in Short-Term Power System Operations. Funding: This work has received funding from the European Research Council (ERC) under the European Union Horizon 2020 research and innovation program [Grant agreement 850540]; FEVER under Horizon 2020 [Grant 864537]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2314. [ABSTRACT FROM AUTHOR]

With increasing the energy demand, the optimal and safe operation of power systems is the main challenge for engineers. Thus, a technique for the optimal implementation of demand response programs (DRPs), installation of distributed generation (DG) with power transmission distribution factors, and DC dynamic load flow is presented in this paper. In fact, finding the optimal time execution of DRPs and the bus for installing wind units with its probabilistic effects is considered. In this model, the congestion is decreased and the available transfer capability (ATC) rates are significantly improved. According to various types of price-based DRPs, the customers motivate to change their utilization models by shifting the price of electricity at different times. Finally, the proposed model is evaluated on the well-known IEEE 39-bus New England power system. The numerical results show the efficiency of the proposed method because, after its application, the available transmissibility values in the critical buses have significantly increased. At the same time, system peak loads, total system costs, and losses are reduced, and the voltage profile also shows a significant improvement. Totally, numerical results demonstrate that using the recommended algorithm, system loss and cost decrease by 9 percent and $4472. [ABSTRACT FROM AUTHOR]