Feasibility studies of a converter-free grid-connected offshore hydrostatic wind turbine

Owing to the increasing penetration of renewable power generation, the modern power system faces great challenges in frequency regulations and reduced system inertia. Hence, renewable energy is expected to take over part of the frequency regulation responsibilities from the gas or hydro plants and contribute to the system inertia. In this article, we investigate the feasibility of frequency regulation by the offshore hydrostatic wind turbine (HWT). The simulation model is transformed from NREL (National Renewable Energy Laboratory) 5-MW gearbox-equipped wind turbine model within FAST (fatigue, aerodynamics, structures, and turbulence) code. With proposed coordinated control scheme and the hydrostatic transmission configuration of the HWT, the `continuously variable gearbox ratio' in turbulent wind conditions can be realised to maintain the constant generator speed, so that the HWT can be connected to the grid without power converters in-between. To test the performances of the control scheme, the HWT is connected to a 5-bus grid model and operates with different frequency events. The simulation results indicate that the proposed control scheme is a promising solution for offshore HWT to participated in frequency response in the modern power system

Studying Spread Patterns of COVID-19 based on Spatiotemporal Data

The current COVID-19 epidemic have transformed every aspect of our lives, especially our behavior and routines. These changes have been drastically impacting the economy in each region, such as local restaurants and transportation systems. With massive amounts of ambient data being collected everywhere, we now can develop innovative algorithms to have a much greater understanding of epidemic spread patterns of COVID-19 based on spatiotemporal data. The findings will open up the possibility to design adaptive planning or scheduling systems that will help preventing the spread of COVID-19 and other infectious diseases. In this tutorial, we will review the trending state-of-theart machine learning techniques to model epidemic spread patterns with spatiotemporal data. These techniques are organized from two aspects: (1) providing a comprehensive review of recent studies about human routine behavior modeling, such as inverse reinforcement learning and graph neural network, and the impacts of behaviors on the spread patterns of infectious diseases based on GPS data; (2) introducing the existing literature on using remote sensing data to monitor the spatiotemporal pattern of the epidemic spread. Under current epidemic with unknown lasting time, we believe that modeling the spread patterns of COVID-19 epidemic is an important topic that will benefit to researchers and practitioners from both academia and industry

Wind Turbine Fault-Tolerant Control via Incremental Model-Based Reinforcement Learning

A reinforcement learning (RL) based fault-tolerant control strategy is developed in this paper for wind turbine torque & pitch control under actuator & sensor faults subject to unknown system models. An incremental model-based heuristic dynamic programming (IHDP) approach, along with a critic-actor structure, is designed to enable fault-tolerance capability and achieve optimal control. Particularly, an incremental model is embedded in the critic-actor structure to quickly learn the potential system changes, such as faults, in real-time. Different from the current IHDP methods that need the intensive evaluation of the state and input matrices, only the input matrix of the incremental model is dynamically evaluated and updated by an online recursive least square estimation procedure in our proposed method. Such a design significantly enhances the online model evaluation efficiency and control performance, especially under faulty conditions. In addition, a value function and a target critic network are incorporated into the main critic-actor structure to improve our method’s learning effectiveness. Case studies for wind turbines under various working conditions are conducted based on the fatigue, aerodynamics, structures, and turbulence (FAST) simulator to demonstrate the proposed method’s solid fault-tolerance capability and adaptability. Note to Practitioners —This work achieves high-performance wind turbine control under unknown actuator & sensor faults. Such a task is still an open problem due to the complexity of turbine dynamics and potential uncertainties in practical situations. A novel data-driven and model-free control strategy based on reinforcement learning is proposed to handle these issues. The designed method can quickly capture the potential changes in the system and adjust its control policy in real-time, rendering strong adaptability and fault-tolerant abilities. It provides data-driven innovations for complex operational tasks of wind turbines and demonstrates the feasibility of applying reinforcement learning to handle fault-tolerant control problems. The proposed method has a generic structure and has the potential to be implemented in other renewable energy systems

InviCloak: An End-to-End Approach to Privacy and Performance in Web Content Distribution

In today's web ecosystem, a website that uses a Content Delivery Network (CDN) shares its Transport Layer Security (TLS) private key or session key with the CDN. In this paper, we present the design and implementation of InviCloak, a system that protects the confidentiality and integrity of a user and a website's private communications without changing TLS or upgrading a CDN. InviCloak builds a lightweight but secure and practical key distribution mechanism using the existing DNS infrastructure to distribute a new public key associated with a website's domain name. A web client and a website can use the new key pair to build an encryption channel inside TLS. InviCloak accommodates the current web ecosystem. A website can deploy InviCloak unilaterally without a client's involvement to prevent a passive attacker inside a CDN from eavesdropping on their communications. If a client also installs InviCloak's browser extension, the client and the website can achieve end-to-end confidential and untampered communications in the presence of an active attacker inside a CDN. Our evaluation shows that InviCloak increases the median page load times (PLTs) of realistic web pages from 2.0s to 2.1s, which is smaller than the median PLTs (2.8s) of a state-of-the-art TEE-based solution

Lentiviral-Mediated shRNA Silencing of PDE4D Gene Inhibits Platelet-Derived Growth Factor-Induced Proliferation and Migration of Rat Aortic Smooth Muscle Cells

Phosphodiesterase 4D (PDE4D) is a member of the large superfamily of phosphodiesterases. PDE4D polymorphisms have been found to associate with ischemic stroke. Proliferation and migration of vascular smooth muscle cells (VSMCs) play a critical role in the pathogenesis of atherosclerosis. In this study, infection of VSMCs with lentivrius particles carrying shRNA direct against PDE4D significantly inhibited platelet-derived growth factor-induced VSMC proliferation and migration, and the inhibitory effects were not associated with global intracellular cAMP level. Our results implicate that PDE4D has an important role in VSMC proliferation and migration which may explain its genetic susceptibility to ischemic stroke

All that Glitters is not Gold: Understanding the Impacts of Platform Recommendation Algorithm Changes on Complementors in the Sharing Economy

Sharing platforms often leverage recommendation algorithms to reduce matching costs and improve buyer satisfaction. However, the economic impacts of different recommendation algorithms on the business operations of complementors remains unclear. This study uses natural quasi-experiments and proprietary data from a home-cooked food-sharing platform with two recommendation algorithms: word-of-mouth recommendation (WMR) and botler personalization recommendation (BPR). Results show the WMR negatively affects revenue while BPR has a positive effect. The contrast revenue effects have been attributed to capacity constraints for complementors and matching frictions for consumers. WMR encourages sellers to specialize in high-quality products but limits new product development. BPR promotes innovation to suit diverse customer tastes but may reduce quality. This reflects the exploration-exploitation trade-off: WMR exploits existing competences, while BPR explores new products to satisfy personal preferences. The authors discuss implications for how to utilize recommendation algorithms and artificial intelligence for the prosperity of sharing economy platforms

Nonlinear Inertia Classification Model and Application

Classification model of support vector machine (SVM) overcomes the problem of a big number of samples. But the kernel parameter and the punishment factor have great influence on the quality of SVM model. Particle swarm optimization (PSO) is an evolutionary search algorithm based on the swarm intelligence, which is suitable for parameter optimization. Accordingly, a nonlinear inertia convergence classification model (NICCM) is proposed after the nonlinear inertia convergence (NICPSO) is developed in this paper. The velocity of NICPSO is firstly defined as the weighted velocity of the inertia PSO, and the inertia factor is selected to be a nonlinear function. NICPSO is used to optimize the kernel parameter and a punishment factor of SVM. Then, NICCM classifier is trained by using the optical punishment factor and the optical kernel parameter that comes from the optimal particle. Finally, NICCM is applied to the classification of the normal state and fault states of online power cable. It is experimentally proved that the iteration number for the proposed NICPSO to reach the optimal position decreases from 15 to 5 compared with PSO; the training duration is decreased by 0.0052 s and the recognition precision is increased by 4.12% compared with SVM