Fault line selection in cooperation with multi-mode grounding control for the floating nuclear power plant grid

The Floating nuclear power plant grid is composed of power generation, in-station power supply and external power delivery. To ensure the safety of the nuclear island, the in-station system adopts a special power supply mode, while the external power supply needs to be adapted to different types of external systems. Because of frequent single phase-ground faults and various fault forms, the fault line selection protection should be accurate, sensitive and adaptive. This paper presents a fault line selection method in cooperation with multi-mode grounding control. Based on the maximum united energy entropy ratio (MUEER), the optimal wavelet basis function and decomposition scale are adaptively chosen, while the fault line is selected by wavelet transform modulus maxima (WTMM). For high-impedance faults (HIFs), to enlarge the fault feature, the system grounding mode can be switched by the multi-mode grounding control. Based on the characteristic of HIFs, the fault line can be selected by comparing phase differences of zero-sequence current mutation and fault phase voltage mutation before and after the fault. Simulation results using MATLAB/Simulink show the effectiveness of the proposed method in solving the protection problems

Forecasting model for short-term wind speed using robust local mean decomposition, deep neural networks, intelligent algorithm, and error correction

Wind power generation has aroused widespread concern worldwide. Accurate prediction of wind speed is very important for the safe and economic operation of the power grid. This paper presents a short-term wind speed prediction model which includes data decomposition, deep learning, intelligent algorithm optimization, and error correction modules. First, the robust local mean decomposition (RLMD) is applied to the original wind speed data to reduce the non-stationarity of the data. Then, the salp swarm algorithm (SSA) is used to determine the optimal parameter combination of the bidirectional gated recurrent unit (BiGRU) to ensure prediction quality. In order to eliminate the predictable components of the error further, a correction module based on the improved salp swarm algorithm (ISSA) and deep extreme learning machine (DELM) is constructed. The exploration and exploitation capability of the original SSA is enhanced by introducing a crazy operator and dynamic learning strategy, and the input weights and thresholds in the DELM are optimized by the ISSA to improve the generalization ability of the model. The actual data of wind farms are used to verify the advancement of the proposed model. Compared with other models, the results show that the proposed model has the best prediction performance. As a powerful tool, the developed forecasting system is expected to be further used in the energy system

Exploiting network compressibility and topology in zero-cost NAS

Neural Architecture Search (NAS) has been widely used to discover high-performance neural network architectures over manually designed approaches. Despite their success, current NAS approaches often require extensive evaluation of candidate architectures in the search space, or the training of large super networks. To reduce the search cost, zerocost proxies have recently been proposed as a way to effciently predict the performance of an architecture. Though many novel proxies have been put forward in recent years, relatively little attention has been dedicated to pushing our understanding of the existing ones. Contrary to that trend, in our work, we argue that it is worth revisiting and analysing the existing proxies in order to further push the boundaries of zero-cost NAS. Towards that goal, we propose to view the existing proxies through a common lens of network compressibility, trainability, and expressivity. Notably, doing so allows us to build a better understanding of the high-level relationship between different proxies as well as refine some of them into their more informative variants. We leverage these insights to design a novel saliency and metric aggregation method informed by compressibility, orthogonality, and network topology. We show that our proposed methods are simple but powerful and yield state-of-the-art results across popular NAS benchmarks

The Effects of the Reverse Current Caused by the Series Compensation on the Current Differential Protection

The series capacitor compensation is one of the key technologies in the EHV and UHV long distance power transmission lines. This paper analyzes the operation characteristics of the main protection combined with the engineering practice when the transmission line overcompensation due to the series compensation system is modified and analyzes the influence of the transition resistance and the system operation mode on the current differential protection. According to the simulation results, it presents countermeasure on improving the sensitivity of differential current protection

Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction

We develop a novel framework that adds the regularizers of the sparse group lasso to a family of adaptive optimizers in deep learning, such as Momentum, Adagrad, Adam, AMSGrad, AdaHessian, and create a new class of optimizers, which are named Group Momentum, Group Adagrad, Group Adam, Group AMSGrad and Group AdaHessian, etc., accordingly. We establish theoretically proven convergence guarantees in the stochastic convex settings, based on primal-dual methods. We evaluate the regularized effect of our new optimizers on three large-scale real-world ad click datasets with state-of-the-art deep learning models. The experimental results reveal that compared with the original optimizers with the post-processing procedure which uses the magnitude pruning method, the performance of the models can be significantly improved on the same sparsity level. Furthermore, in comparison to the cases without magnitude pruning, our methods can achieve extremely high sparsity with significantly better or highly competitive performance. The code is available at https://github.com/intelligent-machine-learning/dlrover/blob/master/tfplus.Comment: 24 pages. Published as a conference paper at ECML PKDD 2021. This version includes Appendix which was not included in the published version because of page limi

Reconstructing the earliest known composite-tiled roofs from the Chinese Loess Plateau

The origins of composite tiles, one of the oldest forms of roofing, are still unclear. This study is based on a set of over 5000 clay tile fragments excavated from a single context in the Qiaocun site on the Chinese Loess Plateau, dated to ~ 2400-2200 BCE (Early Longshan Period). By combining morphological measurement statistics, 3D modeling, computer-based simulations, and reference to historical and archaeological records, we reconstruct the earliest known composite-tile roofing techniques and demonstrate that tile production was under a low-level standardization, with manual control forming a key agent during the roofing process. The quantitative study of the composite roof tiles from Qiaocun was then placed in its archaeological context and compared with other sites on the Loess Plateau. It was found that tile-roofed buildings were, by necessity, community projects. Such structures served as nodes in larger social communication networks; additionally, their appearance was linked to intensified social complexity in public affairs during the Longshan Period. The invention of clay tiles was associated with the inception of thick rammed-earth walls which had sufficient strength to serve as load-bearing structures for heavy tiled roofs. The roof tiles excavated from Qiaocun site indicate that the Loess Plateau was a key center for the origin and spread of composite tiles and related roofing and construction methods, suggesting a Longshan-Western Zhou tradition of roofing techniques in East Asia

Gender-Related Differences in the Dysfunctional Resting Networks of Migraine Suffers

BACKGROUND: Migraine shows gender-specific incidence and has a higher prevalence in females. However, little is known about gender-related differences in dysfunctional brain organization, which may account for gender-specific vulnerability and characteristics of migraine. In this study, we considered gender-related differences in the topological property of resting functional networks. METHODOLOGY/PRINCIPAL FINDINGS: Data was obtained from 38 migraine patients (18 males and 20 females) and 38 healthy subjects (18 males and 20 females). We used the graph theory analysis, which becomes a powerful tool in investigating complex brain networks on a whole brain scale and could describe functional interactions between brain regions. Using this approach, we compared the brain functional networks between these two groups, and several network properties were investigated, such as small-worldness, network resilience, nodal centrality, and interregional connections. In our findings, these network characters were all disrupted in patients suffering from chronic migraine. More importantly, these functional damages in the migraine-affected brain had a skewed balance between males and females. In female patients, brain functional networks showed worse resilience, more regions exhibited decreased nodal centrality, and more functional connections revealed abnormalities than in male patients. CONCLUSIONS: These results indicated that migraine may have an additional influence on females and lead to more dysfunctional organization in their resting functional networks