The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

An Innovative Planning Method for the Optimal Capacity Allocation of a Hybrid Wind–PV–Pumped Storage Power System

In recent years, wind and photovoltaic power (PV) have been the renewable energy sources (RESs) with the greatest growth, and both are commonly recognized as the major driving forces of energy system revolution. However, they are characterized by intermittency, volatility and randomness. Therefore, their stable and efficient implementation is one of the most significant topics in the field of renewable energy research. In order to improve the stability of RESs and reduce the curtailment of wind and solar energy, this paper proposes an innovative planning method for optimal capacity allocation. On one hand, a new power generation system is introduced which combines a pumped storage power station with a wind farm and PV; on the other hand, the sequential Monte Carlo method is utilized to analyze the economy and reliability of the system under different capacity configurations considering investment cost, operating characteristics and influence factors of wind and solar energy. Then, optimal capacity allocation can be achieved. In summary, this proposed scheme provides an effective solution for the planning and construction of a new power generation system with RESs

Earth-Mover-Distance-Based Detection of False Data Injection Attacks in Smart Grids

The high integration of power information physical system improves the efficiency of power transmission, but it also brings new threats to power grid. False data injection attacks can use traditional bad data to detect vulnerabilities and maliciously tamper with measurement data to affect the state estimation results. In order to achieve a higher security level for power systems, we propose an earth mover distance method to detect false data injection attacks in smart grids. The proposed method is built on the dynamic correlation of measurement data between adjacent moments. Firstly, a joint-image-transformation-based scheme is proposed to preprocess the measurement data variation, so that the distribution characteristics of measurement data variation are more significant. Secondly, the deviation between the probability distribution of measurement data variation and the histogram are obtained based on the earth’s mover distance. Finally, a reasonable detection threshold is selected to judge whether there are false data injection attacks. The proposed method is tested using IEEE 14 bus system considering the state variable attacks on different nodes. The results verified that the proposed method has a high detection accuracy against false data injection attacks

A novel bidirectional mechanism based on time series model for wind power forecasting

A novel bidirectional mechanism and a backward forecasting model based on extreme learning machine (ELM) are proposed to address the issue of ultra-short term wind power time series forecasting. The backward forecasting model consists of a backward ELM network and an optimization algorithm. The reverse time series is generated to train backward ELM, assuming that the value to be forecasted is already known whereas one of the previous measurements is treated as unknown. In the framework of bidirectional mechanism, the forward forecast of a standard ELM network is incorporated as the initial value of optimization algorithm, by which error between the backward ELM output and the previous measurement is minimized for backward forecasting. Then the difference between forward and backward forecasting results is used as a criterion to develop the methods to correct forward forecast. If the difference exceeds a predefined threshold, the final forecast equals to the average of forward forecast and latest measurement. Otherwise the forward forecast keeps as the final forecast. The proposed models are applied to forecast wind farm production in six time horizons: 1–6 h. A comprehensive error analysis is carried out to compare the performance with other approaches. Results show that forecast improvement is observed based on the proposed bidirectional model. Some further considerations on improving wind power short term forecasting accuracy by use of bidirectional mechanism are discussed as well

A Novel GPU-Accelerated Strategy for Contingency Screening of Static Security Analysis

Graphics processing unit (GPU) has been applied successfully in many computation and memory intensive realms due to its superior performances in float-pointing calculation, memory bandwidth and power consumption, and has great potential in power system applications. Contingency screening is a major time consuming part of contingency analysis. In the absence of relevant existing research, this paper is the first of its kind to propose a novel GPU-accelerated algorithm for direct current (DC) contingency screening. Adapting actively unique characteristics of GPU software and hardware, the proposed GPU algorithm is optimized from four aspects: data transmission, parallel task allocation, memory access, and CUDA (Compute Unified Device Architecture) stream. Case studies on a 3012-bus system and 8503-bus system have shown that the GPU-accelerated algorithm, in compared with its counterpart CPU implementation, can achieve about 20 and 50 times speedup respectively. This highly promising performance has demonstrated that carefully designed performance tuning in conjunction with GPU programing architecture is imperative for a GPU-accelerated algorithm. The presented performance tuning strategies can be applicable to other GPU applications in power systems

GPU-Accelerated Batch-ACPF Solution for N-1 Static Security Analysis

Graphics processing unit (GPU) has been applied successfully in many scientific computing realms due to its superior performances on float-pointing calculation and memory bandwidth, and has great potential in power system applications. The N-1 static security analysis (SSA) appears to be a candidate application in which massive alternating current power flow (ACPF) problems need to be solved. However, when applying existing GPU-accelerated algorithms to solve N-1 SSA problem, the degree of parallelism is limited because existing researches have been devoted to accelerating the solution of a single ACPF. This paper therefore proposes a GPU-accelerated solution that creates an additional layer of parallelism among batch ACPFs and consequently achieves a much higher level of overall parallelism. First, this paper establishes two basic principles for determining well-designed GPU algorithms, through which the limitation of GPU-accelerated sequential-ACPF solution is demonstrated. Next, being the first of its kind, this paper proposes a novel GPU-accelerated batch-QR solver, which packages massive number of QR tasks to formulate a new larger-scale problem and then achieves higher level of parallelism and better coalesced memory accesses. To further improve the efficiency of solving SSA, a GPU-accelerated batch-Jacobian-Matrix generating and contingency screening is developed and carefully optimized. Lastly, the complete process of the proposed GPU-accelerated batch-ACPF solution for SSA is presented. Case studies on an 8503-bus system show dramatic computation time reduction is achieved compared with all reported existing GPU-accelerated methods. In comparison to UMFPACK-library-based single-CPU counterpart using Intel Xeon E5-2620, the proposed GPU-accelerated SSA framework using NVIDIA K20C achieves up to 57.6 times speedup. It can even achieve four times speedup when compared to one of the fastest multi-core CPU parallel computing solution using KLU library. The proposed batch-solving method is practically very promising and lays a critical foundation for many other power system applications that need to deal with massive subtasks, such as Monte-Carlo simulation and probabilistic power flow