Quantitative Control Approach for Wind Turbine Generators to Provide Fast Frequency Response with Guarantee of Rotor Security

Wind generation is expected to reach substantially higher levels of penetration in the near future. With the converter interface, the rotor inertia of doubly-fed induction generator (DFIG) based wind turbine generator is effectively decoupled from the system, causing a reduction in inertial response. This can be compensated by enabling the DFIG to provide fast frequency response. This paper proposes a quantitative control approach for DFIG to deliver fast frequency response in the inertial response time scale. A supplementary power surge function is added to the active power reference of DFIG. The exact amount of power surge that is available from DFIG-based wind turbine is quantified based on estimation of maximum extractable energy. Moreover, the operational constraints such as rotor limits and converter over-load limit are considered at the same time. Thus, the proposed approach not only provides adequate inertial response but also ensures the rotor speed is kept within a specified operating range. Rotor safety is guaranteed without the need for an additional rotor speed protection scheme.Comment: 5 page

NONINVASIVE ASSESSMENT AND MODELING OF DIABETIC CARDIOVASCULAR AUTONOMIC NEUROPATHY

Noninvasive assessment of diabetic cardiovascular autonomic neuropathy (AN): Cardiac and vascular dysfunctions resulting from AN are complications of diabetes, often undiagnosed. Our objectives were to: 1) determine sympathetic and parasympathetic components of compromised blood pressure regulation in patients with polyneuropathy, and 2) rank noninvasive indexes for their sensitivity in diagnosing AN. Continuous 12-lead electrocardiography (ECG), blood pressure (BP), respiration, regional blood flow and bio-impedance were recorded from 12 able-bodied subjects (AB), 7 diabetics without (D0), 7 with possible (D1) and 8 with definite polyneuropathy (D2), during 10 minutes supine control, 30 minutes 70-degree head-up tilt and 5 minutes supine recovery. During the first 3 minutes of tilt, systolic BP decreased in D2 while increased in AB. Parasympathetic control of heart rate, baroreflex sensitivity, and baroreflex effectiveness and sympathetic control of heart rate and vasomotion were reduced in D2, compared with AB. Baroreflex effectiveness index was identified as the most sensitive index to discriminate diabetic AN. Four-dimensional multiscale modeling of ECG indexes of diabetic autonomic neuropathy: QT interval prolongation which predicts long-term mortality in diabetics with AN, is well known. The mechanism of QT interval prolongation is still unknown, but correlation of regional sympathetic denervation of the heart (revealed by cardiac imaging) with QT interval in 12-lead ECG has been proposed. The goal of this study is to 1) reproduce QT interval prolongation seen in diabetics, and 2) develop a computer model to link QT interval prolongation to regional cardiac sympathetic denervation at the cellular level. From the 12-lead ECG acquired in the study above, heart rate-corrected QT interval (QTc) was computed and a reduced ionic whole heart mathematical model was constructed. Twelve-lead ECG was produced as a forward solution from an equivalent cardiac source. Different patterns of regional denervation in cardiac images of diabetic patients guided the simulation of pathological changes. Minimum QTc interval of lateral leads tended to be longer in D2 than in AB. Prolonging action potential duration in the basal septal region in the model produced ECG and QT interval similar to that of D2 subjects, suggesting sympathetic denervation in this region in patients with definite neuropathy

Consumers Beware: How Are Your Favorite Free Investment Apps Regulated?

The proliferation of free or low-cost investment apps has disrupted the financial industry in recent years. Major brokerage firms have been pressured to go to zero fees due to intense competition from their fintech counterparts. While these apps have extended their products and services to those underserved by traditional brokers, some of their practices raise consumer protection concerns. Namely, the practice of “payment for order flow,” which helps fintech startups sustain a zero-commission model, could lead to subordinating customers’ best interest to market makers who acquire their retail orders from these fintech startups. Further, “cash management accounts,” newly popular among fintech startups with an ambition to compete with chartered banks raise questions about the use of idle customer assets and the protections afforded to these accounts in case of liquidation. This Note considers the products and services of these investment apps in the context of existing U.S. regulations and regulators for broker-dealers, investment advisors, and chartered banks. To illustrate this, this Note analyzes the potential consumer financial protection issues arising out of these fintech-based investment platforms’ distinctive business models and the services they provide

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual networks. This deep neural network allows us to model very complex sequence-contact relationship as well as long-range inter-contact correlation. Our method greatly outperforms existing contact prediction methods and leads to much more accurate contact-assisted protein folding. Tested on three datasets of 579 proteins, the average top L long-range prediction accuracy obtained our method, the representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints can yield correct folds (i.e., TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively. Further, our contact-assisted models have much better quality than template-based models. Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast, when the training proteins of our method are used as templates, homology modeling can only do so for 10 of them. One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method works very well on membrane protein prediction. Finally, in recent blind CAMEO benchmark our method successfully folded 5 test proteins with a novel fold

Modified Kernel MLAA Using Autoencoder for PET-enabled Dual-Energy CT

Combined use of PET and dual-energy CT provides complementary information for multi-parametric imaging. PETenabled dual-energy CT combines a low-energy x-ray CT image with a high-energy &\gamma&-ray CT (GCT) image reconstructed from time-of-flight PET emission data to enable dual-energy CT material decomposition on a PET/CT scanner. The maximumlikelihood attenuation and activity (MLAA) algorithm has been used for GCT reconstruction but suffers from noise. Kernel MLAA exploits an x-ray CT image prior through the kernel framework to guide GCT reconstruction and has demonstrated substantial improvements in noise suppression. However, similar to other kernel methods for image reconstruction, the existing kernel MLAA uses image intensity-based features to construct the kernel representation, which is not always robust and may lead to suboptimal reconstruction with artifacts. In this paper, we propose a modified kernel method by using an autoencoder convolutional neural network (CNN) to extract an intrinsic feature set from the x-ray CT image prior. A computer simulation study was conducted to compare the autoencoder CNN-derived feature representation with raw image patches for evaluation of kernel MLAA for GCT image reconstruction and dual-energy multimaterial decomposition. The results show that the autoencoder kernel MLAA method can achieve a significant image quality improvement for GCT and material decomposition as compared to the existing kernel MLAA algorithm. A weakness of the proposed method is its potential over-smoothness in a bone region, indicating the importance of further optimization in future work. The codes is available on https://github.com/SiqiLi1020/Autoencoder- Kernel-MLAA