GIS-analysis of the power grid sustainability to climatic loads

2019 ◽  
Vol 943 (1) ◽  
pp. 127-135
Author(s):  
B.A. Novakovskiy ◽  
P.E. Kargashin ◽  
A.M. Karpachevskiy
Keyword(s):  
Energy Supply ◽  
Climate Modeling ◽  
Power Grids ◽  
Electrical Networks ◽  
End User ◽  
Gis Modeling ◽  
Comprehensive Method ◽  
Combined Use ◽  
Power Centers ◽  
The Stability

This paper presents a comprehensive method of GIS-modeling and mapping the sustainability of electrical networks to climatic affects (including wind and sleet loads) on the example of the southwestern part of the Krasnodar region. We have proposed a number of improvements in the part of climate modeling (the use of morphometric indicators, spatially weighted regression) on the example of maximum sleet loads during 25 years period. We have also suggested an original approach to the assessment of structural sustainability of power grids. It provides the stability of energy supply to the end user due to the availability of alternative options for connecting to the power centers. Structural stability assessment is carried out on the basis of network modeling and acts as an indicator of the network parts which are the most vulnerable to natural disasters. The combined use of climate and network model allows justifying the strategy of the energy systemdevelopment by increasing the reliability of energy supply to the end user.

Applications of random graphs

2017 ◽  
Author(s):  
A.C.C. Coolen ◽  
A. Annibale ◽  
E.S. Roberts
Keyword(s):  
Social Networks ◽  
Random Graphs ◽  
Interaction Network ◽  
Power Grids ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
First Case ◽  
The Stability ◽  
Protein Protein Interaction Network

This chapter reviews graph generation techniques in the context of applications. The first case study is power grids, where proposed strategies to prevent blackouts have been tested on tailored random graphs. The second case study is in social networks. Applications of random graphs to social networks are extremely wide ranging – the particular aspect looked at here is modelling the spread of disease on a social network – and how a particular construction based on projecting from a bipartite graph successfully captures some of the clustering observed in real social networks. The third case study is on null models of food webs, discussing the specific constraints relevant to this application, and the topological features which may contribute to the stability of an ecosystem. The final case study is taken from molecular biology, discussing the importance of unbiased graph sampling when considering if motifs are over-represented in a protein–protein interaction network.

scholarly journals Asymmetry underlies stability in power grids

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ferenc Molnar ◽  
Takashi Nishikawa ◽  
Adilson E. Motter
Keyword(s):  
Symmetry Breaking ◽  
Real World ◽  
Power Grids ◽  
Stable Operation ◽  
World Systems ◽  
Symmetric State ◽  
Network Systems ◽  
Additional Improvement ◽  
The Stability ◽  
General Method

AbstractBehavioral homogeneity is often critical for the functioning of network systems of interacting entities. In power grids, whose stable operation requires generator frequencies to be synchronized—and thus homogeneous—across the network, previous work suggests that the stability of synchronous states can be improved by making the generators homogeneous. Here, we show that a substantial additional improvement is possible by instead making the generators suitably heterogeneous. We develop a general method for attributing this counterintuitive effect to converse symmetry breaking, a recently established phenomenon in which the system must be asymmetric to maintain a stable symmetric state. These findings constitute the first demonstration of converse symmetry breaking in real-world systems, and our method promises to enable identification of this phenomenon in other networks whose functions rely on behavioral homogeneity.

scholarly journals Retraining Convolutional Neural Networks for Specialized Cardiovascular Imaging Tasks: Lessons from Tetralogy of Fallot

2021 ◽  
Author(s):  
Animesh Tandon ◽  
Navina Mohan ◽  
Cory Jensen ◽  
Barbara E. U. Burkhardt ◽  
Vasu Gooty ◽  
...  
Keyword(s):  
Tetralogy Of Fallot ◽  
Hausdorff Distance ◽  
Volumetric Analysis ◽  
Dice Similarity Coefficient ◽  
Algorithm Performance ◽  
Comprehensive Method ◽  
Combined Use ◽  
Median Error ◽  
Structural Abnormalities ◽  
Testing Data

AbstractVentricular contouring of cardiac magnetic resonance imaging is the gold standard for volumetric analysis for repaired tetralogy of Fallot (rTOF), but can be time-consuming and subject to variability. A convolutional neural network (CNN) ventricular contouring algorithm was developed to generate contours for mostly structural normal hearts. We aimed to improve this algorithm for use in rTOF and propose a more comprehensive method of evaluating algorithm performance. We evaluated the performance of a ventricular contouring CNN, that was trained on mostly structurally normal hearts, on rTOF patients. We then created an updated CNN by adding rTOF training cases and evaluated the new algorithm’s performance generating contours for both the left and right ventricles (LV and RV) on new testing data. Algorithm performance was evaluated with spatial metrics (Dice Similarity Coefficient (DSC), Hausdorff distance, and average Hausdorff distance) and volumetric comparisons (e.g., differences in RV volumes). The original Mostly Structurally Normal (MSN) algorithm was better at contouring the LV than the RV in patients with rTOF. After retraining the algorithm, the new MSN + rTOF algorithm showed improvements for LV epicardial and RV endocardial contours on testing data to which it was naïve (N = 30; e.g., DSC 0.883 vs. 0.905 for LV epicardium at end diastole, p < 0.0001) and improvements in RV end-diastolic volumetrics (median %error 8.1 vs 11.4, p = 0.0022). Even with a small number of cases, CNN-based contouring for rTOF can be improved. This work should be extended to other forms of congenital heart disease with more extreme structural abnormalities. Aspects of this work have already been implemented in clinical practice, representing rapid clinical translation. The combined use of both spatial and volumetric comparisons yielded insights into algorithm errors.

scholarly journals Comprehensive Method to Test the Stability of High Bedding Rock Slop Subjected to Atomized Rain

2020 ◽  
Vol 10 (5) ◽  
pp. 1577
Author(s):  
Zheng-jun Hou ◽  
Bao-quan Yang ◽  
Lin Zhang ◽  
Yuan Chen ◽  
Geng-xin Yang
Keyword(s):  
Slope Failure ◽  
Rock Slope ◽  
Evaluation Method ◽  
Failure Modes ◽  
Similarity Theory ◽  
Fem Simulation ◽  
Strength Reduction ◽  
Comprehensive Method ◽  
The Stability ◽  
Bedding Rock Slope

In the construction of high dams, many high rock slope failures occur due to flood discharge atomized rain. Based on the steel frame lifting technique and strength reduction materials, a comprehensive method is proposed in this paper to study the stability of high bedding rock slope subjected to atomized rain. The safety factor expression of the comprehensive method and the evaluation method for deformation instability were established according to the similarity theory of geomechanical model, failure criterion, and mutation theory. Strength reduction materials were developed to simulate the strength reduction of structural planes caused by rainfall infiltration. A typical test was carried out on the high bedding rock slope in the Baihetan Hydropower Station. The results showed that the failure modes of the bedding rock slope were of two types: sliding–fracturing and fracturing–sliding. The first slip block at the exposed place of the structural plane was sliding–fracturing. Other succeeding slip blocks were mainly of the fracturing–sliding type due to the blocking effect of the first slip block. The failure sequence of the slip blocks along the structural planes was graded into multiple levels. The slip blocks along the upper structural planes were formed first. Concrete plugs had effective reinforcement to improve the shear resistance of the structural planes and inhibit rock dislocation. Finite element method (FEM) simulation was also performed to simulate the whole process of slope failure. The FEM simulation results agreed well with the test results. This research provides an improved understanding of the physical behavior and the failure modes of high bedding rock slopes subjected to atomized rain.

Summary of the Stability Chanllenges and Safe Operation of Power Systems in View of Dispatching

2014 ◽  
Vol 960-961 ◽  
pp. 1588-1591
Author(s):  
Xiang Dong Zhao ◽  
Xin Zhao ◽  
Ming Jun Lv ◽  
Jian Guo Liu ◽  
Feng Zhen Liu ◽  
...  
Keyword(s):  
Power Systems ◽  
Power System ◽  
Rapid Development ◽  
Power Grids ◽  
System Stability ◽  
Stable Operation ◽  
Safe Operation ◽  
The Stability ◽  
Continuous Power ◽  
Grid Market

The Internet and the gradual implementation of the continuous power grid market in recent years make the power system more complex under different operating environment. Safe and stable operation of power grids have become increasingly important . With the rapidf development of the grid and constant innovation, safe and stable operation also has a new requirement , because the rapid development of the power system brings more This paper analyzes the causes of blackouts and reviews security of the power system stability problems related to measures on the security and stability of the power system operation .

Religious Regulation in Autocracies

2019 ◽  
Author(s):  
Lawrence C. Reardon
Keyword(s):  
Theoretical Approach ◽  
The State ◽  
Religious Organizations ◽  
Communist Leader ◽  
Power Centers ◽  
The Stability ◽  
Competing Power

Unlike democracies, the stability and longevity of autocracies are solely dependent on the ability of the paramount leader to maintain and wield power effectively. Whether the autocracy is composed of an absolute monarch or a supreme authoritarian, religious, military, fascist, or communist leader, the autocrat strengthens legitimacy by controlling competing power centers within the state. Autocrats are both envious and fearful of organized religion’s ability to mobilize the citizenry. Whether dealing with large religious organizations or organized religious believers, autocrats can choose to implement negative religious regulations to control or eliminate foreign and domestic religious threats, positive religious regulations to co-opt religious powers, or transformative religious regulations to create new organizations that consolidate and maintain autocratic rule. Adopting an interest-based theoretical approach, the autocratic religious regulations of four countries (China, England, Italy, and Japan) are divided into three categories (negative, positive, and transformative religious regulations). Autocrats within the four countries adopted formal regulations to consolidate their hegemonic control over societal forces within and outside the state.

scholarly journals Complete characterization of the stability of cluster synchronization in complex dynamical networks

2016 ◽  
Vol 2 (4) ◽  
pp. e1501737 ◽  
Author(s):  
Francesco Sorrentino ◽  
Louis M. Pecora ◽  
Aaron M. Hagerstrom ◽  
Thomas E. Murphy ◽  
Rajarshi Roy
Keyword(s):  
Group Theory ◽  
Complete Characterization ◽  
Power Grids ◽  
Cluster Synchronization ◽  
General Technique ◽  
Dynamical Networks ◽  
Computational Group Theory ◽  
Node Network ◽  
Engineered Systems ◽  
The Stability

Synchronization is an important and prevalent phenomenon in natural and engineered systems. In many dynamical networks, the coupling is balanced or adjusted to admit global synchronization, a condition called Laplacian coupling. Many networks exhibit incomplete synchronization, where two or more clusters of synchronization persist, and computational group theory has recently proved to be valuable in discovering these cluster states based on the topology of the network. In the important case of Laplacian coupling, additional synchronization patterns can exist that would not be predicted from the group theory analysis alone. Understanding how and when clusters form, merge, and persist is essential for understanding collective dynamics, synchronization, and failure mechanisms of complex networks such as electric power grids, distributed control networks, and autonomous swarming vehicles. We describe a method to find and analyze all of the possible cluster synchronization patterns in a Laplacian-coupled network, by applying methods of computational group theory to dynamically equivalent networks. We present a general technique to evaluate the stability of each of the dynamically valid cluster synchronization patterns. Our results are validated in an optoelectronic experiment on a five-node network that confirms the synchronization patterns predicted by the theory.

scholarly journals A Review of Key Performance Indicators for Building Flexibility Quantification to Support the Clean Energy Transition

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5676
Author(s):  
Girolama Airò Farulla ◽  
Giovanni Tumminia ◽  
Francesco Sergi ◽  
Davide Aloisio ◽  
Maurizio Cellura ◽  
...  
Keyword(s):  
Performance Indicators ◽  
Renewable Energy Sources ◽  
Energy Transition ◽  
Clean Energy ◽  
Key Performance Indicators ◽  
Electric Power System ◽  
Power Grids ◽  
Current Status ◽  
Future Perspective ◽  
The Stability

The transition to a sustainable society and a carbon-neutral economy by 2050 requires extensive deployment of renewable energy sources that, due to the aleatority and non-programmability of most of them, may seriously affect the stability of existing power grids. In this context, buildings are increasingly being seen as a potential source of energy flexibility for the power grid. In literature, key performance indicators, allowing different aspects of the load management, are used to investigate buildings’ energy flexibility. The paper reviews existing indicators developed in the context of theoretical, experimental and numerical studies on flexible buildings, outlining the current status and the potential future perspective. Moreover, the paper briefly reviews the range of grid services that flexible buildings can provide to support the reliability of the electric power system which is potentially challenged by the increasing interconnection of distributed variable renewable generation.

scholarly journals Development of a system for control of electric energy parameters in the distribution network

2021 ◽  
Vol 7 (2) ◽  
pp. 106-118
Author(s):  
Ivan M. Kazymov ◽  
Boris S. Kompaneets ◽  
Oleg N. Drobyazko
Keyword(s):  
Electric Energy ◽  
Electric Power Industry ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Power Grids ◽  
Effective Control ◽  
Electrical Networks ◽  
Stage Of Development ◽  
Effective System ◽  
The Creation

Background: The creation and distribution of technical means and complexes aimed at building effective control systems for electrical networks using information that can be collected by modern metering devices, as well as organizing work in an automated mode, is an urgent task at the present stage of development of the electric power industry in Russia and in the world. Aim: The research presented in this article is aimed at creating an effective system for monitoring the parameters of electrical energy in distribution networks of low and medium voltage levels. Methods: The study was carried out using the theoretical foundations and basic laws of electrical engineering, as well as methods of computer modeling and CAD. Results: A description of the developed system is given, the applicability of its use is graphically shown and substantiated in writing, the possibilities and prospects of application are indicated, and recommendations for practical application are given. Conclusion: The results obtained can be used by power grid companies to analyze the state and efficiency of power grids, and may also be of interest to researchers working on the creation of digital twins of power grids.

scholarly journals Predictive Analytics for Maintaining Power System Stability in Smart Energy Communities

2021 ◽  
Author(s):  
Aida Mehdipour Pirbazari
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Energy Supply ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
System Stability ◽  
Energy Forecasting ◽  
The Stability ◽  
Energy Prosumers

Digitalization and decentralization of energy supply have introduced several challenges to emerging power grids known as smart grids. One of the significant challenges, on the demand side, is preserving the stability of the power systems due to locally distributed energy sources such as micro-power generation and storage units among energy prosumers at the household and community levels. In this context, energy prosumers are defined as energy consumers who also generate, store and trade energy. Accurate predictions of energy supply and electric demand of prosuemrs can address the stability issues at local levels. This study aims to develop appropriate forecasting frameworks for such environments to preserve power stability. Building on existing work on energy forecasting at low-aggregated levels, it asks: What factors influence most on consumption and generation patterns of residential customers as energy prosumers. It also investigates how the accuracy of forecasting models at the household and community levels can be improved. Based on a review of the literature on energy forecasting and per- forming empirical study on real datasets, the forecasting frameworks were developed focusing on short-term prediction horizons. These frameworks are built upon predictive analytics including data col- lection, data analysis, data preprocessing, and predictive machine learning algorithms based on statistical learning, artificial neural networks and deep learning. Analysis of experimental results demonstrated that load observa- tions from previous hours (lagged loads) along with air temperature and time variables highly affects the households’ consumption and generation behaviour. The results also indicate that the prediction accuracy of adopted machine learning techniques can be improved by feeding them with highly influential variables and appliance-level data as well as by combining multiple learning algorithms ranging from conventional to deep neural networks. Further research is needed to investigate online approaches that could strengthen the effectiveness of forecasting in time-sensitive energy environments.

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