Small signal stability analysis for different types of PMSGs connected to the grid

Small signal stability of permanent magnet synchronous generator (PMSG)-based wind turbines connected to the power grid should be studied properly in order to facilitate damping strategy design. In this paper, unified small-signal models for different types of PMSGs are developed to study their small-signal stability. The models are composed of mechanical systems, electrical systems and control systems. A two-mass shaft model for the mechanical system is provided to analyze the dynamic and steady-state behaviors of the wind turbine and generator rotor. Meanwhile, PMSG, converter system and transmission line are separately modeled to build unified small-signal models for three PMSG-based wind turbine generator systems (WTGS). Then, based on unified small-signal models, eigenvalue analysis is conducted to determine the relation between different oscillation modes and state variables through calculating participation factors. With modal analysis, the developed small signal models are able to find out all types of oscillation modes for PMSGs connected to the power grid, which are subsynchronous oscillation (SSO), subsynchronous control interaction (SSCI) and low-frequency oscillation, including frequency and damping of each oscillation mode. Different initial values of the small signal models can influence both frequencies and damping ratios of oscillation modes, which lay basis for further damping strategy study.</p

Scientific Knowledge Communication in Online Q&A Communities: Linguistic Devices as a Tool to Increase the Popularity and Perceived Professionalism of Knowledge Contribution

With the popularity of question-and-answer (Q&A) communities, widespread dissemination of scientific knowledge has become more viable than ever before. However, those contributing high-quality professional scientific knowledge are confronted with the challenge of making their contributions popular, since non expert readers may not recognize the importance of their contributions given the massive amount of information available online. In this study, we show that non expert readers are capable of evaluating the professionalism of content contributed in such communities as well as experts. However, we discovered that a salient discrepancy exists between the content non experts favor and the content they perceive as professional. In line with studies that have suggested that writing techniques play an important role in how expert content is received by lay persons, we investigated how the use of linguistic devices affects both the perceived professionalism and the popularity of contributions in Q&A communities. Based on both secondary data and a scenario-based survey, we identified specific linguistic devices that can increase content popularity without reducing perceived professionalism. Additionally, we revealed linguistic devices that increase popularity at the expense of perceived professionalism in this context. Finally, we conducted a laboratory experiment to more firmly establish the causal effects of the linguistic device use. The triangulated findings have important implications for both research and practice on communicating scientific knowledge in Q&A communitie

PSYCHOSOCIAL FACTORS LEAD TO DELINQUENCY ITENTION ON ONLINE PEER-TO-PEER LENDING PLATFORM: A SURVEY EVIDENCE

In recent years, online P2P lending grows remarkably. Past studies mainly used direct second-hand data from P2P platforms to conclude many factors are related to people\u27s delinquency and default behaviours, lacking further exploration on how people\u27s social and psychological status could impact their behaviour during the borrowing and repayment process. On foundation of general strain theory (GST) and the model of frame selection (MFS), we used survey method and collected data from more than 700 Chinese subjects. A two-stage structural equation model was proposed. In the first stage, we investigated how people\u27s psychosocial factors (e.g. economic capacity, sense of fairness and sociability etc.) could shape their individual feelings and attitudes in social context (e.g. life satisfaction and self-esteem) as well as morality. In the second stage, we tested the relationship between life satisfaction, self-esteem, moral norm and people\u27s delinquency intention on P2P lending platform. The empirical results suggest that higher psychosocial status will be conductive to better individual feelings of life satisfaction and self-esteem. Moreover, better psychosocial factors will mostly lead to a higher moral norm of people. Therefore, these favourable feelings and morality further contribute to less delinquency intention on P2P lending platform. Our research has both academic and practical implications

Striking a Balance: Harnessing Both the Business and Informational Value of Online Reviews through Resource-matching

A majority of consumers now are getting used to consulting reviews before making any purchase decisions. Although we have witnessed fruitful studies in this stream of literature, there lacks sufficient knowledge regarding whether and how we can realize the information and business values simultaneously. We undertook to bridge this gap. Drawn from the cognitive tuning theory and resource-matching theory, we posit that review sentiment would intertwine with the information richness of a review to affect consumers’ judgment of review helpfulness and purchase decision. Our empirical results demonstrate that the information richness of a review, overall, moderates the U-shaped relationship between review sentiment and review helpfulness, as well as the inverted U-shaped relationship between review sentiment and consumer purchase likelihood. These findings unravel certain conditions under which increasing both purchases and review helpfulness could be achieved, which, therefore, offer non-trivial insights into business practice about review-featuring designs

Scheme design considering network topology and multi-attribute decision-making for under frequency load shedding

Power system emergency control is one key defense strategy in contingencies for protecting the system from cascading blackout. Under Frequency Load Shedding (UFLS) is one such strategy to ensure system stability by shedding load to retrieve balance between power supply and demand. Novel UFLS scheme design and a scheme optimization approach are proposed in this paper to find the optimal load-shedding schemes for different network partition resulted from contingencies. To obtain all possible UFLS schemes for a certain area, a candidate scheme set design algorithm based on value assigning of scheme parameters is proposed and then a relatively complete candidate scheme set is constructed. Considering network splitting caused by protection, several subsystems may exist from the reconstruction of independent network areas. The concept of homological area is defined and a graph-based method is used to analyse system topology change. Then, a multi-attribute decision-making (MADM) algorithm is introduced for order preference by similarity to an ideal solution (TOPSIS) for global optimizing candidate schemes. Optimal schemes for an area in both isolated area and homological area cases can be derived from all feasible UFLS schemes by MADM method. Simulation results demonstrate that the UFLS schemes can effectively restore system frequency in different network topologies.</p

Structurally Diverse Nitric Oxide-Releasing Poly(propylene imine) Dendrimers

Structurally diverse secondary amine-functionalized poly(propylene imine) (PPI) dendrimers capable of tunable nitric oxide (NO) release were synthesized in a straightforward, one-step manner using ring-opening or conjugate-addition reactions with propylene oxide (PO), styrene oxide (SO), acrylonitrile (ACN), poly(ethylene glycol) methyl ether acrylate (average Mn = 480) (PEG) or 1,2-epoxy-9-decene (ED). N-Diazeniumdiolate nitric oxide donors were formed on the resulting secondary amine-functionalized G2–G5 PPI dendrimers by reaction with NO gas in basic solution. The NO storage and release kinetics for the resulting dendritic scaffolds were diverse (0.9–3.8 μmol NO/mg totals and 0.3 to 4.9 h half lives), illustrating the importance of the exterior chemical modification (e.g., steric environments, hydrophobicity, etc.) on diazeniumdiolate stability/decomposition. Tunable NO release was demonstrated by combining two donor systems on the exterior of one macromolecular scaffold. Additionally, a mathematical model was developed that allows for the simulation of dual NO release kinetics using the NO release data from the two single NO donor systems. The approaches described herein extend the range and scope of NO-releasing macromolecular scaffolds by unlocking a series of materials for use as dopants in biomedical polymers or stand-alone therapeutics depending on the exterior modification

Blockchain-Based Water-Energy Transactive Management with Spatial-Temporal Uncertainties

Water resources are vital to the energy conversion process but few efforts have been devoted to the joint optimization problem which is fundamentally critical to the water-energy nexus for small-scale or remote energy systems (e.g., energy hubs). Traditional water and energy trading mechanisms depend on centralized authorities and cannot preserve security and privacy effectively. Also, their transaction process cannot be verified and is subject to easy tampering and frequent exposures to cyberattacks, forgery, and network failures. Toward that end, water-energy hubs (WEHs) offers a promising way to analyse water-energy nexus for greater resource utilization efficiency. We propose a two-stage blockchain-based transactive management method for multiple, interconnected WEHs. Our method considers peer-topeer (P2P) trading and demand response, and leverages blockchain to create a secure trading environment. It features auditing and resource transaction record management via system aggregators enabled by a consortium blockchain, and entails spatial-temporal distributionally robust optimization (DRO) for renewable generation and load uncertainties. A spatial-temporal ambiguity set is incorporated in DRO to characterize the spatial-temporal dependencies of the uncertainties in distributed renewable generation and load demand. We conduct a simulation-based evaluation that includes robust optimization and the moment-based DRO as benchmarks. The results reveal that our method is consistently more effective than both benchmarks. Key findings include i) our method reduces conservativeness with lower WEH trading and operation costs, and achieves important performance improvements by up to 6.1%; and ii) our method is efficient and requires 18.7% less computational time than the moment-based DRO. Overall, this study contributes to the extant literature by proposing a novel two-stage blockchain-based WEH transaction method, developing a realistic spatialtemporal ambiguity set to effectively hedge against the uncertainties for distributed renewable generation and load demand, and producing empirical evidence suggesting its greater effectiveness and values than several prevalent methods.</p

Blockchain-Based Water-Energy Transactive Management with Spatial-Temporal Uncertainties

Water resources are vital to the energy conversion process but few efforts have been devoted to the joint optimization problem which is fundamentally critical to the water-energy nexus for small-scale or remote energy systems (e.g., energy hubs). Traditional water and energy trading mechanisms depend on centralized authorities and cannot preserve security and privacy effectively. Also, their transaction process cannot be verified and is subject to easy tampering and frequent exposures to cyberattacks, forgery, and network failures. Toward that end, water-energy hubs (WEHs) offers a promising way to analyse water-energy nexus for greater resource utilization efficiency. We propose a two-stage blockchain-based transactive management method for multiple, interconnected WEHs. Our method considers peer-topeer (P2P) trading and demand response, and leverages blockchain to create a secure trading environment. It features auditing and resource transaction record management via system aggregators enabled by a consortium blockchain, and entails spatial-temporal distributionally robust optimization (DRO) for renewable generation and load uncertainties. A spatial-temporal ambiguity set is incorporated in DRO to characterize the spatial-temporal dependencies of the uncertainties in distributed renewable generation and load demand. We conduct a simulation-based evaluation that includes robust optimization and the moment-based DRO as benchmarks. The results reveal that our method is consistently more effective than both benchmarks. Key findings include i) our method reduces conservativeness with lower WEH trading and operation costs, and achieves important performance improvements by up to 6.1%; and ii) our method is efficient and requires 18.7% less computational time than the moment-based DRO. Overall, this study contributes to the extant literature by proposing a novel two-stage blockchain-based WEH transaction method, developing a realistic spatialtemporal ambiguity set to effectively hedge against the uncertainties for distributed renewable generation and load demand, and producing empirical evidence suggesting its greater effectiveness and values than several prevalent methods.</p