Application of Complex Network Theory in Power System Security Assessment

The power demand increases every year around the world with the growth of population and the expansion of cities. Meanwhile, the structure of a power system becomes increasing complex. Moreover, increasing renewable energy sources (RES) has linked to the power network at different voltage levels. These new features are expected to have a negative impact on the security of the power system. In recent years, complex network (CN) theory has been studied intensively in solving practical problems of large-scale complex systems. A new direction for power system security assessment has been provided with the developments in the CN field. In this thesis, we carry out investigations on models and approaches that aim to make the security assessment from an overview system level with CN theory. Initially, we study the impact of the renewable energy (RE) penetration level on the vulnerability in the future grid (FG). Data shows that the capacity of RE has been increasing over by 10% annually all over the world. To demonstrate the impact of unpredictable fluctuating characteristics of RES on the power system stability, a CN model given renewable energy integration for the vulnerability analysis is introduced. The numerical simulations are investigated based on the simplified 14-generator model of the South Eastern Australia power system. Based on the simulation results, the impact of different penetrations of RES and demand side management on the Australian FG is discussed. Secondly, the distributed optimization performance of the communication network topology in the photovoltaic (PV) and energy storage (ES) combined system is studied with CN theory. A Distributed Alternating Direction Method of Multipliers (D-ADMM) is proposed to accelerate the convergence speed in a large dimensional communication system. It is shown that the dynamic performance of this approach is highly-sensitive to the communication network topology. We study the variation of convergence speed under different communication network topology. Based on this research, guidance on how to design a relatively more optimal communication network is given as well. Then, we focus on a new model of vulnerability analysis. The existing CN models usually neglect the detailed electrical characteristics of a power grid. In order to address the issue, an innovative model which considers power flow (PF), one of the most important characteristics in a power system, is proposed for the analysis of power grid vulnerability. Moreover, based on the CN theory and the Max-Flow theorem, a new vulnerability index is presented to identify the vulnerable lines in a power system. The comparative simulations between the power flow model and existing models are investigated on the IEEE 118-bus system. Based on the PF model, we improve a power system cascading risk assessment model. In this research the risk is defined by the consequence and probabilities of the failures in the system, which is affected by both power factors and the network structure. Furthermore, a cascading event simulation module is designed to identify the cascading chain in the system during a failure. This innovation can form a better module for the cascading risk assessment of a power system. Finally, we argue that the current cyber-physical network model have their limitations and drawbacks. The existing “point-wise” failure model is not appropriate to present the interdependency of power grid and communication network. The interactions between those two interdependent networks are much more complicated than they were described in some the prior literatures. Therefore, we propose a new interdependency model which is based on earlier research in this thesis. The simulation results confirm the effectiveness of the new model in explaining the cascading mechanism in this kind of networks

In situ electron paramagnetic resonance spectroscopy using single nanodiamond sensors

An ultimate goal of electron paramagnetic resonance (EPR) spectroscopy is to analyze molecular dynamics in place where it occurs, such as in a living cell. The nanodiamond (ND) hosting nitrogen-vacancy (NV) centers will be a promising EPR sensor to achieve this goal. However, ND-based EPR spectroscopy remains elusive, due to the challenge of controlling NV centers without well-defined orientations inside a flexible ND. Here, we show a generalized zero-field EPR technique with spectra robust to the sensor's orientation. The key is applying an amplitude modulation on the control field, which generates a series of equidistant Floquet states with energy splitting being the orientation-independent modulation frequency. We acquire the zero-field EPR spectrum of vanadyl ions in aqueous glycerol solution with embedded single NDs, paving the way towards \emph{in vivo} EPR

Identification of 27 abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function

Cardiovascular disease is a major threat to health and one of the primary causes of death globally. The 12-lead ECG is a cheap and commonly accessible tool to identify cardiac abnormalities. Early and accurate diagnosis will allow early treatment and intervention to prevent severe complications of cardiovascular disease. In the PhysioNet/Computing in Cardiology Challenge 2020, our objective is to develop an algorithm that automatically identifies 27 ECG abnormalities from 12-lead ECG recordings

Never Lost in the Middle: Improving Large Language Models via Attention Strengthening Question Answering

While large language models (LLMs) are equipped with longer text input capabilities than before, they are struggling to seek correct information in long contexts. The "lost in the middle" problem challenges most LLMs, referring to the dramatic decline in accuracy when correct information is located in the middle. To overcome this crucial issue, this paper proposes to enhance the information searching and reflection ability of LLMs in long contexts via specially designed tasks called Attention Strengthening Multi-doc QA (ASM QA). Following these tasks, our model excels in focusing more precisely on the desired information. Experimental results show substantial improvement in Multi-doc QA and other benchmarks, superior to state-of-the-art models by 13.7% absolute gain in shuffled settings, by 21.5% in passage retrieval task. We release our model, Ziya-Reader to promote related research in the community

Razvoj normaliziranog indeksa tla za urbane studije upotrebom podataka daljinskih mjerenja

This paper presents two novel spectral soil area indices to identify bare soil area and distinguish it more accurately from the urban impervious surface area (ISA). This study designs these indices based on medium spatial resolution remote sensing data from Landsat 8 OLI dataset. Extracting bare soil or urban ISA is more challenging than extracting water bodies or vegetation in multispectral Remote Sensing (RS). Bare soil and the urban ISA area often were mixed because of their spectral similarity in multispectral sensors. This study proposes Normalized Soil Area Index 1 (NSAI1) and Normalized Soil Area Index 2 (NSAI2) using typical multispectral bands. Experiments show that these two indices have an overall accuracy of around 90%. The spectral similarity index (SDI) shows these two indices have higher separability between soil area and ISA than previous indices. The result shows that percentile thresholds can effectively classify bare soil areas from the background. The combined use of both indices measured the soil area of the study area over 71 km2. Most importantly, proposed soil indices can refine urban ISA measurement accuracy in spatiotemporal studies.Ovaj rad prikazuje dva nova spektralna indeksa tla kako bi se identificiralo golo tlo te kako bi se bolje razlikovalo od urbanih nepropusnih površina (ISA). Ti indeksi su definirani na temelju srednje prostorne rezolucije daljinskih podataka Landsat 8 OLI skupa podataka. U multispektralnim daljinskim mjerenjima (RS) prepoznavanje golog tla ili urbane ISA podloge je složenije od prepoznavanja vodenih tijela ili podloge s vegetacijom. Zbog sličnosti spektara dobivenih multispektralnim senzorima golo tlo i urbana ISA površina često se ne razlučuju. Ova studija predlaže dva normalizirana indeksa tla (NSAI1 i NSAI2) korištenjem tipičnih multispektralnih pojaseva. Eksperimenti pokazuju da ta dva indeksa imaju sveukupnu točnost od približno 90%. Indeks spektralne sličnosti (SDI) pokazuje da ta dva indeksa razlikuju golo tlo od urbane ISA podloge bolje nego dosadašnji indeksi. Rezultati pokazuju da percentilni pragovi mogu efikasno razlučiti površine s golim tlom od pozadine. Kombiniranom upotrebom oba indeksa izmjerena je površina tla veća od 71 km2. Najznačajniji rezultat je taj da predloženi indeksi tla mogu poboljšati točnost mjerenja urbanih ISA u u prostorno-vremenskim studijama

Maternal super-obesity and perinatal outcomes in Australia: A national population-based cohort study

© 2015 Sullivan et al. Background: Super-obesity is associated with significantly elevated rates of obstetric complications, adverse perinatal outcomes and interventions. The purpose of this study was to determine the prevalence, risk factors, management and perinatal outcomes of super-obese women giving birth in Australia. Methods: A national population-based cohort study. Super-obese pregnant women (body mass index (BMI) >50 kg/m2 or weight >140 kg) who gave birth between January 1 and October 31, 2010 and a comparison cohort were identified using the Australasian Maternity Outcomes Surveillance System (AMOSS). Outcomes included maternal and perinatal morbidity and mortality. Prevalence estimates calculated with 95 % confidence intervals (CIs). Adjusted odds ratios (ORs) were calculated using multivariable logistic regression. Results: 370 super-obese women with a median BMI of 52.8 kg/m2 (range 40.9-79.9 kg/m2) and prevalence of 2.1 per 1 000 women giving birth (95 % CI: 1.96-2.40). Super-obese women were significantly more likely to be public patients (96.2 %), smoke (23.8 %) and be socio-economically disadvantaged (36.2 %). Compared with other women, super-obese women had a significantly higher risk for obstetric (adjusted odds ratio (AOR) 2.42, 95 % CI: 1.77-3.29) and medical (AOR: 2.89, 95 % CI: 2.64-4.11) complications during pregnancy, birth by caesarean section (51.6 %) and admission to special care (HDU/ICU) (6.2 %). The 372 babies born to 365 super-obese women with outcomes known had significantly higher rates of birthweight ≥4500 g (AOR 19.94, 95 % CI: 6.81-58.36), hospital transfer (AOR 3.81, 95 % CI: 1.93-7.55) and admission to Neonatal Intensive Care Unit (NICU) (AOR 1.83, 95 % CI: 1.27-2.65) compared to babies of the comparison group, but not prematurity (10.5 % versus 9.2 %) or perinatal mortality (11.0 (95 % CI: 4.3-28.0) versus 6.6 (95 % CI: 2.6- 16.8) per 1 000 singleton births). Conclusions: Super-obesity in pregnancy in Australia is associated with increased rates of pregnancy and birth complications, and with social disadvantage. There is an urgent need to further address risk factors leading to super-obesity among pregnant women and for maternity services to better address pre-pregnancy and pregnancy care to reduce associated inequalities in perinatal outcomes