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Register a new userTopology Estimation Following Islanding and its Impact on Preventive Control of Cascading Failure
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Knowledge of power grids topology during cascading failure is an essential element of centralized blackout prevention control, given that multiple islands are typically formed, as a cascade progresses. Moreover, academic research on interdependency between cyber and physical layers of the grid indicate that power failure during a cascade may lead to outages in communication networks, which progressively reduce the observable areas. These challenge the current literature on line outage detection, which assumes that the grid remains as a single connected component. We propose a new approach to eliminate that assumption. Following an islanding event, first the buses forming that connected components are identified and then further line outages within the individual islands are detected. In addition to the power system measurements, observable breaker statuses are integrated as constraints in our topology identification algorithm. The impact of error propagation on the estimation process as reliance on previous estimates keeps growing during cascade is also studied. Finally, the estimated admittance matrix is used in preventive control of cascading failure, creating a closed-loop system. The impact of such an interlinked estimation and control on that total load served is studied for the first time. Simulations in IEEE-118 bus system and 2,383-bus Polish network demonstrate the effectiveness of our approach.
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This paper explores whether graph embedding methods can be used as a tool for analysing the robustness of power-grids within the framework of network science. The paper focuses on the strain elevation tension spring embedding (SETSe) algorithm and compares it to node2vec and Deep Graph Infomax, and the measures mean edge capacity and line load. These five methods are tested on how well they can predict the collapse point of the giant component of a network under random attack. The analysis uses seven power-grid networks, ranging from 14 to 2000 nodes. In total, 3456 load profiles are created for each network by loading the edges of the network to have a range of tolerances and concentrating network capacity into fewer edges. One hundred random attack sequences are generated for each load profile, and the mean number of attacks required for the giant component to collapse for each profile is recorded. The relationship between the embedding values for each load profile and the mean collapse point is then compared across all five methods. It is found that only SETSe and line load perform well as proxies for robustness with $R^2 = 0.89$ for both measures. When tested on a time series normal operating conditions line load performs exceptionally well ($R=0.99$). However, the SETSe algorithm provides valuable qualitative insight into the state of the power-grid by leveraging its method local smoothing and global weighting of node features to provide an interpretable geographical embedding. This paper shows that graph representation algorithms can be used to analyse network properties such as robustness to cascading failure attacks, even when the network is embedded at node level.
Cascading failure models are typically used to capture the phenomenon where failures possibly trigger further failures in succession, causing knock-on effects. In many networks this ultimately leads to a disintegrated network where the failure propagation continues independently across the various components. In order to gain insight in the impact of network splitting on cascading failure processes, we extend a well-established cascading failure model for which the number of failures obeys a power-law distribution. We assume that a single line failure immediately splits the network in two components, and examine its effect on the power-law exponent. The results provide valuable qualitative insights that are crucial first steps towards understanding more complex network splitting scenarios.
Research into cascading failures in power-transmission networks requires detailed data on the capacity of individual transmission lines. However, these data are often unavailable to researchers. As a result, line limits are often modelled by assuming they are proportional to some average load. Little research exists, however, to support this assumption as being realistic. In this paper, we analyse the proportional-loading (PL) approach and compare it to two linear models that use voltage and initial power flow as variables. In conducting this modelling, we test the ability of artificial line limits to model true line limits, the damage done during an attack and the order in which edges are lost. we also test how accurately these methods rank the relative performance of different attack strategies. We find that the linear models are the top-performing method or close to the top in all tests. In comparison, the tolerance value that produces the best PL limits changes depending on the test. The PL approach was a particularly poor fit when the line tolerance was less than two, which is the most commonly used value range in cascading-failure research. We also find indications that the accuracy of modelling line limits does not indicate how well a model will represent grid collapse. In addition, we find evidence that the networks topology can be used to estimate the systems true mean loading. The findings of this paper provide an understanding of the weaknesses of the PL approach and offer an alternative method of line-limit modelling.
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Khodakhast Nasirian (Electrical Engineering Department
, Marvdasht Azadn University
, Marvdasht
2020
A microgrid is a new concept that has changed the power systems dramatically. It is a combination of Distributed Generation Resources (DGR) like Biomass, PV systems, Wind energy, Fuel cell, Diesel Generator, and so on with different types of loads (residential or commercial). Microgrids can work in two modes: autonomous and Interconnected. In the Islanding situation, the loads will be supported by DGR and without connecting to upstream utility grids. Controlling power electronic Interfaces between sources and loads has been an important task between the researchers. Several different strategies have been presented by researchers. Droop control strategy is one of the which has its pros and cons. In this paper, the conventional droop strategy has been explained in detail and formulated. The Simulation results are taken from MATLAB/SIMULINK to show the capability of the control strategy.
Swarm robotic systems have foreseeable applications in the near future. Recently, there has been an increasing amount of literature that employs mean-field partial differential equations (PDEs) to model the time-evolution of the probability density of swarm robotic systems and uses mean-field feedback to design stable control laws that act on individuals such that their density converges to a target profile. However, it remains largely unexplored considering problems of how to estimate the mean-field density, how the density estimation algorithms affect the control performance, and whether the estimation performance in turn depends on the control algorithms. In this work, we focus on studying the interplay of these algorithms. Specially, we propose new mean-field control laws which use the real-time density and its gradient as feedback, and prove that they are globally input-to-state stable (ISS) to estimation errors. Then, we design filtering algorithms to obtain estimates of the density and its gradient, and prove that these estimates are convergent assuming the control laws are known. Finally, we show that the feedback interconnection of these estimation and control algorithms is still globally ISS, which is attributed to the bilinearity of the mean-field PDE system. An agent-based simulation is included to verify the stability of these algorithms and their feedback interconnection.
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