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An extension of the dynamic capabilities model is evaluated in a rapidly changing and complex context: the global automotive sector. This industry is undergoing significant change to improve sustainable performance (e.g., alternative power train technologies with less pollution and better energy performance) responding to more stringent regulations and changing locus of innovation (i.e., suppliers investing more in R&D). Using a comparative and validated sample of 104 R&D projects in the U.S. and China, five hypotheses are tested and supported: 1) dynamic capabilities are significantly related to R&D project success, mediated by strategic intent (H1); 2) dynamic capabilities and resources operate independently and significantly in a model to predict sustainable R&D project success (H2); 3) for the U.S. data (a mature economy), there is a significant relationship between technology novelty and dynamic capabilities (H3), but not for the Chinese data, as predicted (H4); and 4) the Chinese economy does not experience the slowdown encountered in the U.S. economy (H5), but Chinese R&D is dominated by incremental improvements of existing products. These results sustain and clarify the differences in the dynamic capabilities model in mature and emerging economies for the global automotive industry. [ABSTRACT FROM AUTHOR]

Blockchain can be used to build a peer-to-peer, secure, and smart transaction system. As a horizontal technology that has changed several fields of industry, blockchain has tremendous potential to transform healthcare systems as well. In this article, a systematic review is conducted to critically evaluate 64 articles on blockchain-based healthcare systems, published between 2016 and January 2020 in 21 conferences, 33 journals, and ten online sources. The aim of this article is to answer three main questions. First, what are the applications of blockchain in the healthcare systems, and what are the structures and challenges of applying blockchain to a specific healthcare domain? Second, what are the technical, temporal, and spatial aspects of the currently developed blockchain applications for different healthcare domain? Third, what are the future research directions in designing and implementing blockchain-based healthcare systems? Statistical facts about the technical aspects of these 64 articles show that most of the proposed blockchain-based healthcare systems use private blockchain and Ethereum platforms; furthermore, the majority of the authors are affiliated with research institutions in the USA and China. We also discuss potential future research directions, e.g., integrating the blockchain in artificial intelligence based solutions, cloud-computing-based solutions, and parallel blockchain architecture. [ABSTRACT FROM AUTHOR]

Objective: ECG recordings often suffer from a set of artifacts with varying types, severities, and durations, and this makes an accurate diagnosis by machines or medical doctors difficult and unreliable. Numerous studies have proposed ECG denoising; however, they naturally fail to restore the actual ECG signal corrupted with such artifacts due to their simple and naive noise model. In this pilot study, we propose a novel approach for blind ECG restoration using cycle-consistent generative adversarial networks (Cycle-GANs) where the quality of the signal can be improved to a clinical level ECG regardless of the type and severity of the artifacts corrupting the signal. Methods: To further boost the restoration performance, we propose 1D operational Cycle-GANs with the generative neuron model. Results: The proposed approach has been evaluated extensively using one of the largest benchmark ECG datasets from the China Physiological Signal Challenge (CPSC-2020) with more than one million beats. Besides the quantitative and qualitative evaluations, a group of cardiologists performed medical evaluations to validate the quality and usability of the restored ECG, especially for an accurate arrhythmia diagnosis. Significance: As a pioneer study in ECG restoration, the corrupted ECG signals can be restored to clinical level quality. Conclusion: By means of the proposed ECG restoration, the ECG diagnosis accuracy and performance can significantly improve. [ABSTRACT FROM AUTHOR]

The question of how institutional entrepreneurs working on megaprojects successfully enact change in regard to logic practices remains an unsolved problem in megaproject management literature. Building on an in-depth case study of a megaproject in China, we examine how institutional entrepreneurs manage in order to transform a conventional construction logic into a new logic of “mechanization, autoassembling, and factory construction” in steel bridge industry. We present a model of institutional work conducted by entrepreneurs for institutional entrepreneurship in the megaproject platform. Based on this case, we highlight the significance of institutional compatibility and institutional governmentality in implementing the new logic practices. This article contributes to the literature by examining the different forms of institutional work conducted by entrepreneurs with the aim of driving the institutional changes, and exploring the relationship between megaproject management and institutional entrepreneurship. [ABSTRACT FROM AUTHOR]

This article develops and tests a primary conceptual model of the direct effects of business environment on supply chain integration (SCI) and an alternative moderation model to better understand the role of the environment in developing integration and financial performance improvement. Structural equation modeling and moderated regression are used to analyze the survey data collected from manufacturing firms in China. The results reveal significant positive relationships between environmental dynamism and all three dimensions of SCI, and between environmental hostility and internal integration. The results also indicate that internal integration fully mediates the relationships between environmental hostility and both customer and supplier integration. Supplier integration is significantly and positively associated with financial performance, whereas internal and customer integration are not significantly associated with performance. Importantly, through the test of an alternative model, environmental hostility and environmental dynamism are shown to affect SCI directly, rather than moderating the SCI-performance relationship. This article provides useful guidelines for managers to develop integrated supply chains to improve the financial performance under dynamic and hostile environments. [ABSTRACT FROM AUTHOR]

The rising number of cyberattacks and cybercriminals around the world has put major threats on the cybersecurity and even the national security of many countries. In response, governments started to introduce policies and regulations to improve cybersecurity. It is increasingly realized that effective cybersecurity policy making largely depends upon optimized policy attention and the effective use of public resources. This article draws on the “attention-driven policy choice model” and the punctuated equilibrium theory to analyze the relationship between policy attention allocation, policy agenda setting, and the choice of policy tools in different periods of cybersecurity development. Using Chinese cybersecurity development as the main context, content analysis of policy texts was conducted to examine the pathway of cybersecurity policy development in China between 1994 and 2019. The findings suggest that there is evidence for punctuated equilibrium with policy stagnation and incrementality being broken up by major events which shift policy attention and subsequently affects agenda setting and the choice of policy tools. The findings of this article will provide important implications for the development of strategic foresight of cybersecurity and the effective use of public resources to improve cybersecurity. [ABSTRACT FROM AUTHOR]

WeChat is the largest social instant messaging platform in China, with 1.1 billion monthly active users. “Top Stories” is a novel friend-enhanced recommendation engine in WeChat, in which users can read articles based on preferences of both their own and their friends. Specifically, when a user reads an article by opening it, the “click” behavior is private. Moreover, if the user clicks the “wow” button, (only) her/his direct connections will be aware of this action/preference. Based on the unique WeChat data, we aim to understand user preferences and “wow” diffusion in Top Stories at different levels. We have made some interesting discoveries. For instance, the “wow” probability of one user is negatively correlated with the number of connected components that are formed by her/his active friends, but the click probability is the opposite. We further study to what extent users’ “wow” and click behavior can be predicted from their social connections. To address this problem, we present a hierarchical graph representation learning based model DiffuseGNN, which is capable of capturing the structure-based social observations discovered above. Our experiments show that the proposed method can significantly improve the prediction performance compared with alternative methods. [ABSTRACT FROM AUTHOR]

Conventionally, the 2-parameter Weibull model, Arrhenius-Weibull model, has been used vastly for transformer aging-dependent unavailability modeling. However, this model only uses the lifetime feature to describe the transformer's degradation process and to calibrate the Weibull parameters, which harms the accuracy of aging-dependent unavailability forecasting. In response, this paper develops a 3-calibratable-parameter Weibull model for evaluating the transformer aging-dependent unavailability. In the proposed model, both the individualized aging threshold and lifetime are taken into the calibration of the Weibull parameters to accurately characterize the heterogeneity in transformer populations. First, a degree of polymerization (DP) analysis and Monte Carlo Simulation (MCS) based approach is proposed for estimating the transformers’ uncertain aging thresholds and lifetimes. Then, the Maximum Likelihood Estimate and Particle Swarm Optimization are jointly adopted to model the relationship among the calibratable Weibull parameters, aging threshold, and lifetime. Finally, the analytical formula of aging-dependent unavailability is derived from the established 3-calibratable-parameter Weibull model using an integral-discretization method. A real utility application example in China's Chongqing power system has been presented to validate and demonstrate the practicality and usefulness of this method. [ABSTRACT FROM AUTHOR]

Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. Consequently, automatic understanding of visual data collected from drones becomes highly demanding, bringing computer vision and drones more and more closely. To promote and track the developments of object detection and tracking algorithms, we have organized three challenge workshops in conjunction with ECCV 2018, ICCV 2019 and ECCV 2020, attracting more than 100 teams around the world. We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i.e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking. In this paper, we first present a thorough review of object detection and tracking datasets and benchmarks, and discuss the challenges of collecting large-scale drone-based object detection and tracking datasets with fully manual annotations. After that, we describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. Being the largest such dataset ever published, VisDrone enables extensive evaluation and investigation of visual analysis algorithms for the drone platform. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions. We expect the benchmark largely boost the research and development in video analysis on drone platforms. All the datasets and experimental results can be downloaded from https://github.com/VisDrone/VisDrone-Dataset. [ABSTRACT FROM AUTHOR]

This article introduces the measurement and analysis of the transient electric field (E-field) derived from the switching operation of the modular multilevel converter (MMC) and the high voltage direct current (HVdc) circuit breaker (CB) in a 200 kV converter station constructed in the Zhoushan Islands, China. It is revealed that the switching transient E-field in the converter station is a quasi-static field. Although the switching E-field pulse is a kind of electromagnetic interference to the secondary electronic circuit of the converter station, it can be used to accurately monitor the action moment of the MMC and the CB. The action moment of the MMC and the CB can be measured with an error of less than 0.005 ms based on the steep pulse front of the switching E-field pulse. This result, which is far more accurate compared to the measurement using the voltage recording system that comes with the converter station. This is of significance to the relay protection of the HVdc transmission system whose fault rapidly develops. Additionally, this method has the advantage of being noninvasive, flexible, and low cost. It has potential application value in the condition monitoring of the MMC and the HVdc CB. [ABSTRACT FROM AUTHOR]

High renewable energy penetration increases the electricity seasonal imbalance in the long-term timescale. Power system planning needs to consider the optimal configuration of various flexibility resources and electricity balance in different timescales. The coupling of multiple timescales largely increases the computation complexity of the power system planning problem. Thus, this paper presents an efficient source-grid-storage co-planning model which incorporates a year-round hourly operation simulation. To improve the computation efficiency of the planning model, from the temporal scale, a self-adaptive compact panorama time series (CPTS) model is applied, which greatly reduces the number of variables related to short-term decisions. From the spatial scale, a network-constrained relaxed clustered unit commitment (NC-RCUC) model is introduced, which significantly reduces the number of variables related to unit commitment decisions. Case studies on the modified Garver’s 6-node system and HRP-38 system prove the validation and efficiency of the proposed model (“HRP” stands for high renewable penetration). The studies on the China power grid in 2035 demonstrate the future planning results of generation, transmission and storage in China power systems based on the proposed model. [ABSTRACT FROM AUTHOR]

Natural ester insulating oil not only has a biodegradation rate of almost 100% but also meets the carbon emission requirements of China’s “Carbon Peak and Carbon Neutrality” and the “European Green Deal” proposed by the European Union. It is considered to be a good substitute for mineral insulating oil. However, due to its higher kinematic viscosity than traditional mineral oil in low-temperature environments, it limits the safety promotion and application of transformers in cold regions. First, we design an experiment to test the kinematic viscosity and density of natural ester insulating oil. Under extremely harsh experimental conditions, we measure key experimental data such as kinematic viscosity. Second, the structure of the four main triglyceride molecules is optimized, and the molecular dynamics (MD) simulation technology is used to establish an MD model that can predict the kinematic viscosity of natural ester insulating oil (−20 °C to 20 °C). Finally, the model is further simplified by the free volume theory to better predict the kinematic viscosity of natural ester insulating oil. This study makes up for the lack of laboratory low-temperature testing of the kinematic viscosity of natural ester insulating oil and provides a convenient and reliable tool for predicting the kinematic viscosity of natural ester insulating oil. It can not only predict the natural ester insulating oil mixed in various proportions but also predict the kinematic viscosity of a certain natural ester molecule. Furthermore, it also provides a guiding direction for the improvement of low-temperature kinematic viscosity of natural ester insulating oil, as well as a strong reference for predicting other properties of other substances. [ABSTRACT FROM AUTHOR]

Planetary rovers may traverse terrains with complex geometries and variable physical properties, but their mobility behaviors are complicated and difficult to simulate precisely. This article focuses on high-fidelity dynamic modeling and simulation for a type of rovers that incorporate single-input-multi-output joints to enhance terrain adaptability, which has been used on China's Tianwen-1 Mars rover. A novel multibody dynamic model and its solutions are derived first with consideration of single-input-multi-output joints. Then, a unified terramechanics model is proposed, considering variable terrain surfaces and covering rover's motion states of skidding, slipping, and steering, solved the problem of simulation instability caused by model switching between soft and hard terrains. As the contact areas of wheels with various terrains and resultant sinkage are dominant factors to ensure fidelity but difficult to determine, a new terrain modeling method for calculating contact area and wheel sinkage is developed using digital elevation map with physical properties. A simulation system is developed, integrating all the above models, and verified with physical experiments and commercial software. The relative simulation errors that have been achieved are less than 5.9% for bogie angles, 6.1% for drawbar pull, and 3.4% for slip ratios, demonstrating high fidelity simulation results. [ABSTRACT FROM AUTHOR]

Long-term electricity consumption forecasting has been a hot issue due to its importance in power systems planning. Recently, system dynamics (SD) have been considered a potential method for electricity consumption forecasting. However, traditional SD methods are failed in forecasting future power systems toward low-carbon, due to the complex influencing factors and the simple SD model structure. In this study, a novel method combining SD and the ImPACT equation is proposed to solve the above problem. First, a new influencing factor system including emerging factors is established to consider the development of future power system. Subsequently, the ImPACT equation is employed to decompose the electricity consumption into four subsystems. By doing this, the linkage between factors and electricity consumption can be better revealed, which helps to improve the forecasting accuracy. Next, models for quantifying the correlation between subsystems and factors are established using SD. Finally, the proposed method is verified by forecasting the power consumption of China in the future year 2030 to 2050. Furthermore, various scenarios are set to analyze the changing trend of future power consumption by different influencing factors. The results of this article can provide important basic information for power supply and power grid planning. [ABSTRACT FROM AUTHOR]

3-D printing is an additive manufacturing process, which enables products to be custom designed. Obtaining the evolution trend and identifying the potential R&D hotspots of 3-D printing technology are important strategic issues for both nations and enterprises. In this article, a combination method is proposed using the Viterbi algorithm to identify technological terms in patents, using the latent Dirichlet allocation model to capture potential technological topics in patents, and using the hidden Markov model to analyze the distribution and evolution pattern of the technological topics. Based on the results, technological topics and future R&D hotspots in 3-D printing are identified. Among the technological topics, extrusion devices and laser powder molding are gradually losing R&D; model acquisition, extrusion devices, rotary printing, and denture are the key topics of technological evolution that are difficult to overcome; laser focusing, print head, color printing, recyclable, feeding, control panel, and denture will become the focus of R&D in the near future. Finally, a comparison and experiments are given to demonstrate the plausibility of our method. [ABSTRACT FROM AUTHOR]

With the proliferation of online social networks, the identification or classification of real-life relationship between users has been very useful for many applications such as financial fraud detection. In real life, usually people with different relationships would present gifts with special meanings to each other on different dates. In many Asian cultures, especially in Chinese culture, the red packet is a traditional form of monetary gift. With the rapid development of the Internet, people gradually began to give electronic red packets instead of paper ones as the means of money gifting on social network platforms. As motivated, in this paper we advocate a novel approach that exploits users’ red packet interactions for users relationship identification on WeChat, one of the largest social platforms in China. Specifically, we analyze the WeChat red packets network, identify the real-life relationship types between users through mining the semantic information of the amount and sending time of each red packet. In order to better capture the red packet gifting behaviors between users for relationship identification, on one hand, we construct an Amount-Date Graph and apply the graph embedding method to learn embeddings of the amount and sending date of each red packet. On the other hand, we propose a novel sequential model, Cross & Attention Sequence Model (CASM), which explicitly learns the interactions between the latent semantic information of each red packet’s amount and sending date in the red packets sequence between two users. To validate our approach, we conduct comprehensive experiments on a real-world WeChat Users Red Packets dataset that involves 8 kinds of real-life relationships. The experiments show that our proposed approach performs significantly better than baselines and achieves 81.70 percent prediction accuracy. [ABSTRACT FROM AUTHOR]

Voltage sag insurance (VSI) is an effective measure to reduce the voltage sag loss and risk for enterprises with insufficient mitigation confidence or economic ability. However, due to the lack of understanding of voltage sag risk (VSR), it is difficult for power utilities to identify the enterprises with VSI demands, and it is also unable to realize differentiated premium according to different enterprises' situations. To solve the above problems, this paper proposes an improved voltage sag premium model based on VSR. Firstly, the VSR factors are analyzed, such as the hazard, the vulnerability, the exposure. The process immune time is introduced to describe the vulnerability. Based on the traditional consideration of only the ability of users to resist voltage sag, the ability of users to recover from voltage sag is further considered. Then, the risk evaluation model of voltage sag is established. The evaluation results can help power utilities to judge whether enterprises have the demand of VSI. Finally, the improved premium model of VSI based on VSR is constructed to ensure the differentiation and rationalization of the premium. The correctness and rationality of this method are proved by ten manufacturing enterprises in China by analyzing the VSR and comparing the premium before and after the improvement. [ABSTRACT FROM AUTHOR]

China is the largest coal consumer in the world. The massive exploitation and utilization of coal resources have resulted in serious problems of heavy metal pollution and environmental contamination, such as soil degradation, water pollution, crop damage, and even threatening human lives. Therefore, monitoring soil heavy metal pollution quickly and in real time is an urgent task at present. This research not only formulated a new preprocessing method enlightened by few-shot learning for soil hyperspectral data but also combined it with other soil-related auxiliary information to extract effective information from the soil hyperspectrum, at the end of which different regression methods were adopted to predict soil heavy metal contamination. This test used 168 actual soil samples from the Eastern Junggar coalfield in Xinjiang for verification. Since copper in the soil is a trace element and the corresponding spectral characteristics are affected by other impurities, improper use of hyperspectral preprocessing methods may introduce interference information or may delete useful information, which makes the model effect unsatisfied. To effectively address the above-mentioned problems, the preprocessing method of this experiment combined the second-order differential derivation, and the data enhancement (DA) method together with the addition of auxiliary information to allow more effective features to be entered into the model. Next, the attentive interpretable tabular learning (TabNet) model was improved in three different ways using the original TabNet model and three improved TabNet models to create regression models. One of the improved TabNet models had the best effect, with a list of the top 30 features according to the degree of importance. Meanwhile, the regression prediction of Cu content using four different convolutional neural networks (CNNs) revealed that the model with the residual block was the strongest and slightly outperformed the improved TabNet model, but lacked interpretation of the input data. Besides, this experiment also employed different preprocessing methods for regression prediction on various models and found that the traditional preprocessing methods performed best in traditional regression models [e.g., partial least square regression (PLSR)] and underperformed in deep learning models. The selected optimal model was compared with PLSR and CNN models. The results indicated that both the improved TabNet model and the improved CNN model had better performance using the new preprocessing approach proposed in this article, with improved TabNet yielding a coefficient of determination ($\text{R}^{2}$), root-mean-square error (RMSE), and the ratio of performance to interquartile range (RPIQ) of 0.94, 1.341, and 4.474, respectively. The improved CNN model had a coefficient of determination of 0.942, an RMSE of 1.324, and an interquartile range of 4.531 in the test dataset. [ABSTRACT FROM AUTHOR]

Continuously monitoring surface ozone (O3) spatial distribution and forecasting its variations are beneficial to improving air quality and ensuring public health in China, although achieving this goal faces challenges from currently available observations and retrieval techniques. Hence, we introduce a coupled surface O3 estimation framework (LESO) to address these challenges by integrating ground-level observing networks and satellite remote sensing. LESO features easy-to-use deep learning algorithms, independence on chemical transportation models (CTMs), and consistent performance using data from different satellites. LESO includes a deep forest 21 (DF21) model to interpolate O3 concentration by learning spatial patterns and a long short-term memory (LSTM) model to forecast O3 concentration by learning data from the past. We used sites of city-level in situ networks as the control sites to manifest short-distance O3 transportation. Satellite-based observations of O3 precursor indicators were incorporated to capture O3 photochemical reactions. DF21 explained a larger fraction of O3 variability (90%) with a mean bias error (MBE) of smaller than $1~\mu \text {g/m}^{3}$. We also investigated the impact of the number of training sites on the DF21 performance, which suggested that five training sites could ensure a good DF21 performance for the most areas ($R^{2} > 0.85$ and bias $< 2~\mu \text {g/m}^{3}$). The forecast O3 concentration via LSTM showed a good and stable agreement ($R^{2}\approx 0.85$ and bias $< 5~\mu \text {g/m}^{3}$) with ground-based measurements for 8-, 24-, 28-, and 72-h time periods, respectively. Overall, LESO aims to bring convenient functionality and reliable surface O3 estimates for broad users. [ABSTRACT FROM AUTHOR]

Three-phase unbalance could have significant impacts on the secure and economic operation of a low-voltage distribution system. With the deployment of smart meters and the emergence of phase switch devices (PSDs), more efficient schemes for mitigating three-phase unbalance are expected by utilizing numerous measurement data and the installed PSDs in low-voltage areas. Therefore, a bi-level optimal phase switch device placement model for mitigating three-phase unbalance is proposed in this work. In the upper-level model, the PSD placement strategy is optimized by minimizing the total costs of PSD installation and operation costs of low-voltage areas determined in the lower-level model. In the lower-level model, the operation costs of low-voltage areas for typical days are minimized based on the PSDs and their locations determined in the upper-level model. The proposed bi-level model is tested by an actual low-voltage area of Zhejiang province, China, and the effectiveness of the optimal PSD placement model for reducing the total investment and operation costs of the low-voltage area demonstrated. [ABSTRACT FROM AUTHOR]

The High Energy Photo Source (HEPS) is being built in China and the magnets are in batch production. The octupole magnets with skew quadrupole component used in the storage ring have been designed and manufactured. The octupole has an aperture of 30 mm, a field gradient of 735000 T/m3 and a length of 0.26 m. In this paper, detailed magnetic design of HEPS octupole magnets is presented. Different design schemes of the skew quadrupole coils are compared, and the temperature effect on magnetic center and the integral field in the first batch of octupole magnets are also given. In addition, the influence of the combined power-on mode on the magnetic field is studied. [ABSTRACT FROM AUTHOR]

Traditional steganography methods are usually designed on a lossless channel; thus, messages are often not extracted correctly from an image transmitted over a lossy channel that includes attacks such as scaling. To address this issue, in recent years, the field of robust steganography has emerged. In this paper, the process of image scaling by interpolation is first observed and serves as the basis for proposing the idea of inverse interpolation. Subsequently, the idea of constructing an inverse interpolation equation set is proposed to solve the problem of intersectional blocks during the inverse interpolation process. Then, the scaling factor’s valid range of inverse interpolation is analyzed. Next, the inverse interpolation is successfully applied in robust image steganography. A method that combines antiscaling and antidetection is proposed. Afterward, actual tests on the top 9 mobile phone brands with 28 models and 2 social communication apps that are currently popular in China are done. The scaling factor’s valid range of the proposed method is verified to match the actual lossy channel. The experimental results show that the proposed method achieves a reliable extraction of embedded messages for common interpolation scaling attacks while maintaining high statistical detection resistance. [ABSTRACT FROM AUTHOR]

The hybrid LCC/VSC cascaded HVDC (HC-HVDC) scheme, in which the LCC is adopted as rectifier, and one LCC in series with multiple paralleled VSC based MMCs is adopted as inverter, combines the advantages of both LCC and VSC-MMC technologies. Presently, a HC-HVDC project is planned to deliver the hydro power from Baihetan to Jiangsu in China. One of the major challenges is to suppress the DC overcurrent issue of VSC-MMCs especially under commutation failure condition of LCC at inverter side. Based on Baihetan-Jiangsu project under construction, the model of HC-HVDC system is developed in PSCAD/EMTDC. In the system, the rectifier employs a dual 12-pulse LCC, while the inverter adopts a hybrid scheme, i.e., one LCC converter in series with three paralleled VSC-MMC converters. This paper addresses the main cause and feature of the DC overcurrent, then proposes an overcurrent suppression control method for HC-HVDC system based on fuzzy clustering and identification approach. And the system transient performances with the proposed control approach are evaluated under AC fault conditions. The results show that the proposed approach can not only suppress the DC overcurrent following the commutation failure to a certain extent, but also improve the post-fault recovery performance of the overall system. [ABSTRACT FROM AUTHOR]

This paper proposes a load transfer optimization (LTO) model involving hot-spot temperature (HST) and top-oil temperature (TOT) limits of transformers. This work aims to address the overload problem and transformer HST and TOT excursions via LTO. First, the equivalent thermal model for the HST and TOT of transformers under external cooling conditions are discussed, and then the corresponding linearized HST and TOT constraints with varied thermal resistances are derived using the discretization method. Moreover, the proposed LTO model is formulated on the reconfigurable topology of high voltage distribution networks (HVDNs) in China. This proposed LTO model can achieve the optimal network reconfiguration scheme subject to temperature limits and system operation constraints, with the least switching operation cost and the maximum margins of reactive power exchange between HVDNs and transmission systems. Finally, the effectiveness of the proposed LTO model is validated by the modified IEEE-14 node system and the practical HVDNs in China. [ABSTRACT FROM AUTHOR]

Traditional magnetotelluric (MT) impedance estimations are based on Fourier theory and carried out in the frequency domain, which has a strict stationarity requirement for the analyzed signal. However, the stationarity assumption cannot be satisfied when the data possess a low signal-to-noise ratio (SNR) and/or a short observation period. These shortcomings can cause significant errors in the MT impedance estimations, especially in the low-to-medium frequency bands. The alternating direction method of multipliers (ADMMs) and polarization analysis of the electromagnetic signal can be attached to the time-domain MT impedance estimation to address these shortcomings. This time-domain technique calculates the impedance in the time domain without Fourier transformation, and the ADMM and polarization analysis are applied to further improve the stability and convergence of the impedance estimation. Here, we present an ADMM-based method for MT impedance estimations (ADMM-MT). Various noise are added to a noise-free MT signal to test the performance of ADMM-MT. The results show that ADMM-MT yields impedance estimates with relative recovery errors below 0.2, even when the SNR of the data is 0 dB and the observation period is 60 min. We then apply ADMM-MT to field data in Inner Mongolia, China. The results indicate that the apparent resistivity curves obtained by ADMM-MT using a short time series are smooth over the 0.001–1-Hz band, which is consistent with the remote reference (RR) processing results obtained using a long time series. In contrast, the curves obtained by traditional robust estimation method are strongly biased. [ABSTRACT FROM AUTHOR]

With the ever-increasing installation of large-capacity ultrahigh-voltage direct current (UHVdc) links, the “strong dc link and weak ac network” issue has become prominent for many regional grids in China. The transmission expansion planning (TEP) problem of such grids has been facing a significant challenge to cope with the coordination between the UHVdc links and the ultrahigh/high voltage alternating current (UHVac/HVac) network. To handle this issue, this article proposes a multistage robust TEP (RTEP) model of ultrahigh-voltage ac–dc hybrid grids. This model explicitly takes into account multiple types of uncertainties, i.e., credible contingencies occurring in the ac network, large power imbalances due to the UHVdc bipole blocking, as well as the long-/short-term uncertainty. Wasserstein metric is used to reformulate the uncertainty set and the worst-case constraints. A column-and-constraint generation based solution approach is developed in order to ensure tractability of the RTEP model. The effectiveness of the proposed RTEP is verified by numerical testing on a practical large-scale power grid. [ABSTRACT FROM AUTHOR]

Power system planning is essential for ensuring development of a safe and stable power system. Articles on power system planning have been published in recent decades. In this paper, bibliometric analysis is applied to power system planning to identify its basic characteristics, and to summarize the research hotspots and trends. A total of 4562 publications were obtained from the Web of Science during 1971–2020. The 4562 publications cover 8136 keywords, 1526 journals, 107 countries/territories, and 2519 institutions. IEEE Transactions on Power Systems is the key journal in power system planning; USA, China, and England are the countries that dominate publication production. Research hotspots and trends were analyzed using author-keyword co-occurrence analysis and keyword burst detection. Four clusters were identified and topics from Cluster I (central nodes as power system stability and transient stability) and Cluster IV (central nodes as power system simulation and economics) are important for power system planning research. However, their relative popularities have declined over the past two decades. This indicates that Cluster II (central nodes as renewable energy, wind power, and energy storage) and Cluster III (central nodes as distributed generation and smart grids) will be the focus of future research. [ABSTRACT FROM AUTHOR]

Construction of the linearized state-space model is the key step of eigenvalue analysis for SSO study of power systems with large-scale renewable power generation (RPG) integrated. This paper proposes a novel method to construct the state matrix of the AC/DC hybrid power system with RPG within the discrete-time domain. The continuous-time state-space model of each AC/DC component as well as RPG units in the power system, is discretized together with their corresponding control. The discretized equation of each component is then represented by an equivalent circuit consisting of parallel historical current and conductance. According to the power system configuration, the discrete-time equivalent circuit of the whole AC/DC hybrid system is established, and the nodal analysis method is applied for the construction of the discrete-time state-space model of the power system. In this way, the state matrix can be acquired much more conveniently than that of continuous-time state-space method, which needs topology analysis to find independent state variables of the network from the proper tree. The proposed method has been applied in the eigenvalue analysis for the SSO study of a case system adapted from the AC/DC hybrid transmission system in northwest China. Eigenvalue results are in high accordance with time-domain simulation results in PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

Electricity theft behaviors have caused great harm to the economic benefits of power companies and the secure operation of power systems, thus electricity theft detection is paid much attention in the actual power supply management. In this work, a two-step electricity theft detection strategy is proposed to identify electricity theft users and predict potentially stolen electricity (PSE) for maximizing economic return. In the first step, a neural network model called convolutional autoencoder (CAE) is proposed for electricity theft identification, and the convolutional layer is adopted in CAE to extract and identify the abnormalities of electricity theft users against the uniformity and periodicity of normal power consumption features. In the second step, the PSE of each identified electricity theft user is predicted by the improved regression algorithm named Tr-XGBoost, which combines the extreme gradient boosting (XGBoost) algorithm and transfer adaptive boosting (TrAdaBoost) training strategy. The propsoed Tr-XGBoost could learn the relationship between the extracted electricity features and the PSE of each electricity theft user, and then the predicted PSE can be used to determine the list of electricity theft users to be inspected for maximizing economic return. Case studies on both the IEEE 33-bus test system and a low-voltage distribution system of a province in China show that the proposed two-step electricity theft detection strategy can improve the accuracy of electricity theft identification, and obtain a larger economic return because of a more accurate result of PSE prediction than other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Machine learning approaches have exhibited high potential in power system transient stability assessment (TSA), yet their initial preparation stages of stability knowledge base generation (SKBG) based on time-domain simulations often undergo high computational costs. In fact, how to ease the heavy computational burden of SKBG without sacrificing the reliability is still a significant challenge. To address this problem, this paper develops a semi-supervised ensemble learning (SSEL) framework for reliable SKBG acceleration, without additional need for computing hardware upgrading. In particular, it performs detailed simulations for a minority of cases and fast simulations for the majority ones to reduce the total computation time. Considering the absence of stability status information of those fast simulated cases, an SSEL scheme is systematically designed to reliably label their stability status. Before implementing SSEL, two concise feature descriptors are first introduced to efficiently extract transient features from multiplex system trajectories. Then, all the cases are characterized in a unified feature space, whereby a series of semi-supervised support vector machines are trained in randomly formed subspaces. Afterward, these single machines are systematically combined to derive an enhanced SSEL model, which is able to make reliable and robust labeling decisions. Further, a backtrace strategy is carefully devised for SSEL, so as to maintain the high reliability of SKBG. Test results on the IEEE 39-bus system and the realistic GD Power Grid in South China illustrate the excellent performances of the proposed framework on SKBG acceleration. [ABSTRACT FROM AUTHOR]

Purpose: This article aims to provide an integrated model and methodology for discovering valuable innovation knowledge and creative users in online innovative communities. Design/methodology/approach: The super-network integration approach is applied in constructing the user's innovation knowledge super-network (UIKSN) model based on knowledge fragments discovered from user-generated content (UGC) data using text mining methods. The social network analysis (SNA) methodology is then used to analyze the KSN model. An empirical research is conducted with China's Xiaomi online community to illustrate the UIKSN model and the SNA methodology. Findings: Compared with current methods, the proposed KSN model and SNA methodology are demonstrated to be more effective and valid in discovering valuable innovation knowledge including valuable innovations, innovation trends, creative users and their knowledge background, etc. Research limitations/implications: The results of KSN modeling and analysis are inevitably influenced by the performance of text mining methods. Practical implications: More and more companies start to adopt online community user's innovations in their new product developments. However, it is difficult to discover users’ opinions and innovations from the UGC due to its tremendous volume. Therefore, the KSN model and SNA methodology presented in this article are helpful for companies to manage online communities and to utilize users’ innovations effectively and efficiently. Originality/value: The article provides two main contributions in the study of online communities: 1) a well-justified model (users' innovation KSN, UIKSN) to explore online users’ contributions; 2) a new and more effective methodology to discover and analyze valuable innovations and innovative users. [ABSTRACT FROM AUTHOR]

Due to the continuous intensification of human activities in the ocean, the frequent outbreaks of red tide have caused great harm to the marine environment and ecology. Thus, the rapid detection and monitoring of red tide become particularly important. At present, the main monitoring methods depend on artificial and buoy data, as well as optical satellite remote sensing. However, these methods may not be able to effectively deal with the characteristics of red tide bloom, such as suddenness and unpredictability. The global navigation satellite system-reflectometry (GNSS-R) is an emerging technology that makes use of navigation signals as a remote sensing opportunity to obtain Earth surface information. GNSS-R has already been proved to be capable of retrieving sea surface parameters (e.g., dielectric constant and sea surface roughness) closely related to the outbreak of a red tide. In this article, we proposed a new method to estimate red tide density, which utilizes an all-new model associating GNSS-R observations with sea surface red tide density. This method can remove the weather influence and greatly decrease the revisit period, which is much longer for optical red tide remote sensing methods. The Landsat-8 near-infrared data and TechDemoSat-1 (TDS-1) GNSS-R data of a red tide outbreak in the sea off the Tsingtao coast in China are used to build and test the proposed method. The results demonstrate that the correlation coefficient is 0.73, and the root mean square error of retrieved red tide density is 2.84%, which shows that the GNSS-R technology shows great potential to perform the rapid and preliminary red tide monitoring and judgment. [ABSTRACT FROM AUTHOR]

The online retail market shows a sharp increase in traffic during holiday sales. The ability to distinguish customers who will likely purchase is critical for provisioning traffic and for providing cost-effective promotions. This paper uniquely studies the browsing and purchasing behaviors of online shoppers during a yearly sale event in China, the world’s largest online marketplace. Based on 31 million action logs gathered from wide residential areas, we characterize the steps leading to purchases and determine their precursors. We investigate the effect of time (e.g., date, time of date), environment (e.g., platform, viewed category), and action (e.g., session time, clicks, sequence) on purchases. Action cues from shopping behaviors can be used for early detection. While most shoppers start with strong intentions to purchase, yet the moment of ordering comes rather impulsively within 30 seconds to several minutes of browsing. The predictive accuracy reaches as a high AUC of 0.924. The findings in this paper provide an understanding of traffic during mega sale events that can help online shops plan and provide a better user experience for upcoming shopping festivals. [ABSTRACT FROM AUTHOR]

Surface monitoring of microseismic monitoring events is generally challenging because microseismic data have a low signal-to-noise ratio (SNR). Traditional event-detection methods struggle to detect weak microseismic events. A variance fractal dimension (VFD) method for automatic microseismic event detection via multitrace energy envelope stacking (MTEES) is introduced. In the first stage, we propose a processing microseismic data method based on the MTEES method. It increases the energy of weak microseismic data to avoid missed and false microseismic detection. Furthermore, it can greatly improve computational efficiency to satisfy real-time processing requirements. In the second stage, the VFD algorithm is applied to the data processed in the first stage to improve the feasibility and validity of microseismic event detection. A simulation test with perforation data shows the reliability of the new method in the automatic detection of microseismic events. In addition, we demonstrate that analogous results can be obtained when perforation data are not available by introducing a novel approach based on synthetic correction time. The new approach is particularly useful when perforation data are not recorded, representing a significant advantage over previous approaches. We describe the application of the novel method to a real microseismic data example from monitoring hydraulic fracture treatments in Shanxi Province, China, with and without perforation data. The new method yields improvement in microseismic event detection for microseismic monitoring. Therefore, we find a wide range of applications requiring analysis of microseismic data. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) change detection aims to identify the differences in multitemporal HSIs. Recently, a graph convolutional network (GCN) has attracted increasing attention in the field of remote sensing due to its advantages in processing irregular data. In comparison with a convolutional neural network (CNN) that can only perform convolution operations on data with the assumption of the Euclidean structure, GCN adopts a graph structure to flexibly capture the characteristics and structure information of non-Euclidean data. In this article, we propose a novel dual-branch difference amplification GCN (D2AGCN) for HSI change detection with limited samples, which allows the network to fully extract and effectively amplify the difference features of multitemporal HSIs for change detection. The dual-branch structure can effectively extract sufficient different features to facilitate the detection of the changed areas. As far as we know, this is the first time that GCN has been introduced into HSI change detection. A difference magnification module is designed to suppress similar regions and highlight the feature differences between the multitemporal HSIs in the dual-branch structure, which increases the distinction between change and nonchange classes. The visual and quantitative experimental results on three real hyperspectral datasets (i.e., China, Bay Area, and Santa Barbara) show that the proposed D2AGCN outperforms most of the state-of-the-art methods in HSI change detection with limited training samples. [ABSTRACT FROM AUTHOR]

An increasing number of residential customers have installed hybrid rooftop solar battery systems (HRSBSs). Currently, most HRSBSs are installed behind-the-meter (BTM), where only customers’ net load is measured by smart meters. This invisibility poses significant challenges to the system operation. Disaggregating BTM PV generation and battery charging/discharging profile of customers can enhance the grid-edge observability. This article proposes an energy disaggregation method to jointly separate the PV generation and battery charging/discharging power from the net load. First, a Home Smart Battery Management model is built to simulate the battery charging/discharging profile. Second, an optimal disaggregation model is established based on a contextually supervised source separation method. Furthermore, the feature vectors of PV, load, and battery are extracted as the input of the disaggregation model. Case studies on two datasets from Australia and China show that the proposed method has a promising disaggregation performance. [ABSTRACT FROM AUTHOR]

Iron and steel industry is the pillar industry of China, providing a solid foundation for China's industrialization. While steel plants are energy intensive mainly consuming coal and electricity, and their power consumption tends to be higher proportional in the near future, due to global confidence of peak and neutrality carbon dioxide emission, electrical substitution, and ongoing energy use right transaction. Thus, these plants will have larger potential to participate in the friendly interaction between power supply and demand. The adjustable potential of purchased power load exists in two aspects: one is the controllable resources of inner energy supply and use system, and the other is the energy schedule regulation accompanied by production process adjustment, which is rarely considered in current energy management research of steel plants. Based on the flexibility analysis of production process control without affecting productivity, the adjustment model of steel-rolling lines, air separation and electric arc furnaces on iron-making line is formulated here, as well as the corresponding energy consumed in these processes. Combined with the optimization of multienergy coupling system and production processes, a joint evaluation model of maximum dispatching potential and duration is proposed and solved by mixed-integer linear programming solver. Simulation result shows the feasibility of the proposed dispatching potential evaluation method and the effectiveness to take the production process adjustment with energy-systemscheduling. [ABSTRACT FROM AUTHOR]

The community-integrated energy system (CIES) is an essential energy internet carrier that has recently been the focus of much attention. A scheduling model based on chance-constrained programming is proposed for integrated demand response (IDR) enabled CIES in uncertain environments to minimize the system operating costs, where an IDR program is used to explore the potential interaction ability of electricity–gas–heat flexible loads and electric vehicles. Moreover, power to gas (P2G) and microgas turbine (MT), as the links of multienergy carriers, are adopted to strengthen the coupling of different energy subsystems. Sequence operation theory and linearization methods are employed to transform the original model into a solvable mixed-integer linear programming model. The simulation results on a practical CIES in North China demonstrate an improvement in the CIES operational economy via the coordination of IDR and renewable uncertainties, with P2G and MT enhancing the system operational flexibility and user comprehensive satisfaction. The CIES operation is able to achieve a tradeoff between the economy and system reliability by setting a suitable confidence level for the spinning reserve constraints. Besides, the proposed solution method outperforms the hybrid intelligent algorithm in terms of both optimization results and calculation efficiency. [ABSTRACT FROM AUTHOR]

As a kind of zero-carbon and high-efficiency energy, hydrogen has attracted more attention with clean energy technology development. However, large-scale green hydrogen application is difficult to promote because of its high production cost. China is rich in water resources, but the utilization rate is low, which causes serious water abandonment problems. This article studies the optimal operation of combined cascade reservoir and hydrogen system to improve the utilization of water resources and the system's economic benefits. In this article, a multi-objective nonlinear model is established. An expanded progressive optimization algorithm is adopted to solve the problem more efficiently and accurately. The weights of objective functions are discussed with data from the Hongru River Basin in China. The rationality and effectiveness of combining cascade reservoir and hydrogen system are verified through the optimal operation of water resources and the optimal allocation of power and hydrogen. In addition, the overall economic benefits and reservoir operation efficiency are improved. [ABSTRACT FROM AUTHOR]

As a new energy vehicle, fuel cell vehicles (FCVs) have the advantages of pollution-free and zero-emission, which have attracted wide attention from all over the world. By predicting the ownership of FCVs, decision-makers can grasp the future development potential of FCVs, so that the national government can adopt corresponding policies and measures. To this end, based on analyzing the development trend of FCVs, this article proposes an FCVs ownership prediction method based on the generalized Bass model. From the perspective of technology, application, and market conditions of FCVs, this article first analyzes the development trend of FCVs in the future, which provides the basis for the establishment of the ownership prediction method and the estimation of model parameters. Then, considering the influence of levelized cost of driving (LCD) reduction and hydrogen refueling stations (HRSs) constructions, a prediction method of FCVs ownership based on the generalized Bass model is proposed, and the model parameters are estimated by Grey Verhulst model, electric vehicles data, and liquefied natural gas vehicles data. Through the proposed method, the ownership of FCVs in China from 2021 to 2070 is predicted and the results show that the ownership of FCVs in China will reach about 1.43 million in 2030 and about 10.41 million in 2040. Finally, through sensitivity analysis, this article discusses the influence of external factors such as policy, LCD, and HRSs on the market diffusion of FCVs, and puts forward corresponding suggestions. [ABSTRACT FROM AUTHOR]

The ZY1-02D satellite, which was launched in 2019, is China’s first civil hyperspectral satellite. However, the laboratory calibration and vicarious calibration methods could not provide accurate radiometric calibration coefficients after the satellite had been launched. In this article, we describe how a cross-calibration method was utilized to calibrate the ZY1-02D hyperspectral imager using the well-calibrated Gaofen-5 Advanced Hyperspectral Imager (GF-5 AHSI). The 6S radiative transfer model was selected to simulate the apparent reflectance of the two hyperspectral sensors under corresponding imaging conditions, and the calibration coefficients were calculated by spectral channel matching. The reflectance-based vicarious calibration was carried out for comparison. Through the validation experiments, it is shown that the reflectance data obtained by cross-calibration and vicarious calibration are basically consistent, showing a stable radiation performance. At the Dunhuang calibration site, the ratio of measured surface reflectance to the cross-calibrated image reflectance is between 0.9 and 1.1, the $R^{2}$ values are more than 0.96, and the spectral angles are less than 3°. The validation results for different ground features also show the applicability of the corrected coefficients. When compared with different sensors, the maximum difference between the ZY1-02D reflectance results after cross-calibration and Landsat-8/Sentinel-2 is less than 0.04 and the mean difference is less than 0.02, which further proves that the ZY1-02D hyperspectral imager has a high radiation accuracy after cross-calibration. The proposed cross-calibration method could be used as an effective supplement to the on-orbit calibration method and could also be extended to other satellite hyperspectral imagers. [ABSTRACT FROM AUTHOR]

Calculation of geomagnetically induced current (GIC) flowing through power system during the geomagnetic storm has attracted more attention recently. However, for high voltage power systems with transmission lines over hundreds or even thousands of kilometers, the earth model and geomagnetical field generally vary significantly. So, it is essential to take them into consideration using limited earth survey sites and geomagnetic observatories. To address this problem, a Kriging method is introduced in this paper to make earth model and geomagnetical field interpolations. It has the characteristic of spatial autocorrelation by considering not only the distances between predicted points and training points but also the distances between training points themselves. Finally, a case study of the Central China 1000 kV ultra-high voltage (UHV) grid is carried out to illustrate the applicability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) sensors, such as Advanced Land Observing Satellite-2 (ALOS-2) and Sentinel-1, provide significant opportunities for soil moisture content (SMC) retrieval with relatively high spatial resolutions (10~30 m). In this work, an artificial neural network (ANN) SMC retrieval algorithm combined with the water cloud model, the advanced integral equation model, and the Oh model database was proposed. The SAR copolarization backscatter, the local incidence angle (LIA), and the normalized difference vegetation index were used in input vectors for the ANN algorithm for the retrieval and mapping of the ALOS-2 and Sentinel-1 SMC at a 30-m resolution. The results of the comparison between the SMC retrievals and the measured SMC show that Sentinel-1 and ALOS-2 SMC retrievals with high accuracy correspond to low-vegetation areas (crop, grass, and shrub), with a root mean square error (RMSE) of 0.021 and 0.033 cm3/cm3, respectively. ALOS-2 SMC retrievals provide higher accuracy (RMSE = 0.076 cm3/cm3) than Sentinel-1 SMC retrievals at high vegetation (e.g., forest). However, it remains challenging for soil moisture retrieval in forest land. The C-band and L-band SMC retrievals have higher RMSE (up to 0.047 cm3/cm3) at low incidence angle (<20°) and high incidence angle (>50°). In addition, by considering the impact of rainfall on the SMC, it appears that the Sentinel-1 and ALOS-2 SMC have a good response to the rainfall events. Finally, the results of the comparison between the SMC retrievals and the Soil Moisture Active Passive (SMAP) L2 SMC product show that the correlation coefficients between Sentinel-1, ALOS-2, and SMAP are higher in September when the vegetation is drying than in July when the vegetation is growing. [ABSTRACT FROM AUTHOR]

Converting wind energy into ammonia (WtA) has been recognized as a promising pathway to produce “green” ammonia compared with traditional coal-based technologies. As the key part of WtA, Power-to-Ammonia (PtA) has great potential to facilitate the usage of wind generation. This paper proposes a co-planning approach for regional wind resources-based ammonia industry and the electric network (EN). To this end, PtA is first modeled as a flexible power load of power systems with spatial and temporal constraints on hydrogen supply chains (HSC). Then a novel co-planning model of WtA and EN is established to optimize the WtA configuration and the EN expansion. An alternating direction method of multipliers (ADMM) based algorithm is introduced to effectively solve this model. Real data of Inner Mongolia Province in China is adopted to verify the effectiveness and significance of the proposed approach. It is shown that the siting and operation flexibility of PtA with HSC can reduce the expansion burden of EN. The co-planning of WtA and EN can significantly enhance wind power utilization and reduce total investment costs. Furthermore, feasibility analysis on WtA in comparison with coal-to-ammonia (CtA) and ultra-high voltage transmission (UHV) provides helpful guidelines for the realization of WtA. [ABSTRACT FROM AUTHOR]

Deep learning is considered to be a promising method for total transfer capability (TTC) evaluation due to its high speed and accuracy for nonlinear and complicated problems. However, the operating scenarios of power systems are complex and ever changing. It is difficult to train a single deep model using an overly complex dataset containing operating scenarios with high variability. In addition, making the deep model transparent to validate for operators is another challenge to its application in the power system. To enable the practical application of deep models in TTC evaluation, the operating sample space is clustered into simpler spaces via a two-stage clustering method, and the idea of neighborhood model for the deep TTC evaluation network (DTEN) is proposed. Then, the local post hoc interpretability for the DTEN based on first-order control variable sensitivity is presented. During the interpretability analysis, the quasi-steady state is introduced to assess the coupling effects among different inputs. Case studies on the IEEE 39-bus system and a real-world regional system in China validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

While many machine learning approaches have been widely applied to power system online dynamic stability assessment, how to sufficiently learn spatial-temporal correlations from system transients without losing the interpretability is still a challenging issue. In this paper, a novel networked time series shapelet learning approach is proposed to learn spatial-temporal correlations for transient stability assessment (TSA) in an interpretable manner. Specifically, a network impedance-based adjacency matrix is first introduced to characterize spatial networked correlations. Based on graph structural regularization, this matrix is effectively incorporated into the subsequent learning procedure as spatial constraints. Taking time series trajectories acquired from multiple buses as the inputs, networked shapelet learning is heuristically performed to learn critical sequential features, i.e., networked shapelets, for TSA model derivation. With the learning procedure strategically guided by inherent spatial-temporal correlations of the system, the obtained data-driven TSA model is able to perform highly reliable and interpretable online TSA. Numerical test results on the IEEE 39-bus test system and the realistic GD Power Grid in China verify the superior performances of the proposed approach. [ABSTRACT FROM AUTHOR]

Impedance modeling of a permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS) usually ignores the machine-side dynamics, instead, the machine-side system is simplified as a constant power source, while merely keeping the detailed grid-side converter model. This simplification is mainly based upon the intuition that the dc-bus capacitor is large enough so that the machine- and grid-side dynamics can be decoupled. However, the simplification probably does not hold in practice since there is always a tendency to minimize the capacitor size to achieve a smaller package for installation. Therefore, the machine-side system should not be ignored, otherwise maybe leading to error for stability analysis. To address this issue, this paper developed a complete ac-side impedance model of the PMSG-based WECS. Then, impact factors about the coupling characteristics between machine- and grid-side systems of the PMSG-based WECS are discussed. Finally, a case study of sub/super synchronous oscillation is carried out on a practical offshore wind farm integrated with a modular multilevel converter based high-voltage dc (MMC-HVDC) transmission system in China. The results show that ignoring the machine-side system would lead to an optimistic stability judgement, which confirms the necessity of the complete model of PMSG-based WECS. Furthermore, the system can be stabilized by either changing the couplings between machine- and grid-side systems of PMSG-based WECS or implementing active damping in the wind farm side MMC. [ABSTRACT FROM AUTHOR]

FinTech, the merging of finance and modern Internet-based technology, has rapidly presented itself as a disruptor to traditional business financing, notably in the form of crowdfunding and peer-to-peer lending. In this article, we examine the determinants of the use of FinTech finance by businesses, with a particular focus on the ownership, governance, and business practices that may modify the relationships with conventional motivations for external finance. Using a comprehensive sample of Chinese hi-tech small and medium-sized enterprises (SMEs), we find that state-owned enterprises (SOEs) and family firms and financially constrained firms are respectively much less and much more likely to seek FinTech finance. In the Chinese context, we argue that part of this may be because of the relative ease with which SOE SMEs can access cheaper conventional finance through SOE banks. The quality of traditional relationship banking also affects the relative desirability of FinTech financing. As for family firms, we find that innovation, as exemplified by R&D activity, effectively overrides any conventional reluctance to access external finance, suggesting the relative benefits of FinTech finance for innovative high-growth firms. [ABSTRACT FROM AUTHOR]

Traffic radar-based speed meter (TRSM) is an important measuring instrument for measuring the speed of target vehicle based on the radar Doppler effect, and has been legally used in road vehicle speeding enforcement for more than 30 years in China. TRSM with target position determination (TRSM-TPD) is a new generation of TRSM with the advantages of speeding enforcement by lane, high accuracy of speed measurement and target position determination, high efficiency, low cost, etc. This paper proposes a new verification method for TRSM-TPD in road vehicle speeding enforcement. The proposed verification method includes the simulated speed calibration method for initial verification in the laboratory, and the online speed and position calibration method for subsequent verification in the field. The range of simulated radial speed is up to (1∼400) km/h and the maximum permissible error is within ±0.03 km/h, which can satisfy the full-range and high-accuracy speed calibration requirements for TRSM-TPD. The uncertainty of the reference facility in actual traffic is 0.12% within the speed range of (60∼250) km/h, where coverage factor k = 3. The simulated and online calibration results validate the effectiveness of the proposed verification method and the feasibility of the realized target speed simulator and the established reference facility. [ABSTRACT FROM AUTHOR]