The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Ian Dobson - One of the best experts on this subject based on the ideXlab platform.
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benchmarking and validation of Cascading Failure analysis tools
IEEE Transactions on Power Systems, 2016Co-Authors: Janusz Bialek, Ian Dobson, Eduardo Cotillasanchez, E Ciapessoni, D Cirio, Chris Dent, Pierre Henneaux, Paul Hines, Jorge L Jardim, Stephen S MillerAbstract:Cascading Failure in electric power systems is a complicated problem for which a variety of models, software tools, and analytical tools have been proposed but are difficult to verify. Benchmarking and validation are necessary to understand how closely a particular modeling method corresponds to reality, what engineering conclusions may be drawn from a particular tool, and what improvements need to be made to the tool in order to reach valid conclusions. The community needs to develop the test cases tailored to Cascading that are central to practical benchmarking and validation. In this paper, the IEEE PES working group on Cascading Failure reviews and synthesizes how benchmarking and validation can be done for Cascading Failure analysis, summarizes and reviews the Cascading test cases that are available to the international community, and makes recommendations for improving the state of the art.
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dual graph and random chemistry methods for Cascading Failure analysis
Hawaii International Conference on System Sciences, 2013Co-Authors: Paul D H Hines, Ian Dobson, Eduardo Cotillasanchez, Margaret J EppsteinAbstract:This paper describes two new approaches to Cascading Failure analysis in power systems that can combine large amounts of data about Cascading blackouts to produce information about the ways that cascades may propagate. In the first, we evaluate methods for representing Cascading Failure information in the form of a graph. We refer to these graphs as “dual graphs” because the vertices are the transmission lines (the physical links), rather than the more conventional approach of representing power system buses as vertices. Examples of these ideas using the IEEE 30 bus system indicate that the “dual graph” methods can provide useful insight into how cascades propagate. In the second part of the paper we describe a random chemistry algorithm that can search through the enormous space of possible combinations of potential component outages to efficiently find large collections of the most dangerous combinations. This method was applied to a power grid with 2896 transmission branches, and provides insight into component outages that are notably more likely than others to trigger a Cascading Failure. In the conclusions we discuss potential uses of these methods for power systems planning and operations.
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approximating a loading dependent Cascading Failure model with a branching process
IEEE Transactions on Reliability, 2010Co-Authors: Janghoon Kim, Ian DobsonAbstract:We quantify the closeness of the approximation between two high-level probabilistic models of Cascading Failure. In one model called CASCADE, failing components successively load the unfailed components, whereas the other model is based on a Galton-Watson branching process. Both models are generic, idealized models of Cascading Failure of a large, but finite number of components. For suitable parameters, the distributions of the total number of Failures from the branching process and CASCADE models are close enough to make the branching process a useful approximation.
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testing branching process estimators of Cascading Failure with data from a simulation of transmission line outages
Risk Analysis, 2010Co-Authors: Ian Dobson, Janghoon Kim, K R WierzbickiAbstract:We suggest a statistical estimator to quantify the propagation of Cascading transmission line Failures in large blackouts of electric power systems. We use a Galton-Watson branching process model of Cascading Failure and the standard Harris estimator of the mean propagation modified to work when the process saturates at a maximum number of components. If the mean number of initial Failures and the mean propagation are estimated, then the branching process model predicts the distribution of the total number of Failures. We initially test this prediction on Failure data generated by a simulation of Cascading transmission line outages on two standard test systems. We discuss the effectiveness of the estimator in terms of how many cascades need to be simulated to predict the distribution of the total number of line outages accurately.
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using transmission line outage data to estimate Cascading Failure propagation in an electric power system
IEEE Transactions on Circuits and Systems Ii-express Briefs, 2008Co-Authors: Hui Ren, Ian DobsonAbstract:We study Cascading transmission line outages recorded over nine years in an electric power system with approximately 200 lines. The average amount of propagation of the line outages is estimated from the data. The distribution of the total number of line outages is predicted from the propagation and the initial outages using a Galton-Watson branching process model of Cascading Failure.
Margaret J Eppstein - One of the best experts on this subject based on the ideXlab platform.
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estimating Cascading Failure risk with random chemistry
IEEE Transactions on Power Systems, 2015Co-Authors: Pooya Rezaei, Paul Hines, Margaret J EppsteinAbstract:The potential for Cascading Failure in power systems adds substantially to overall reliability risk. Monte Carlo sampling can be used with a power system model to estimate this impact, but doing so is computationally expensive. This paper presents a new approach to estimating the risk of large Cascading blackouts triggered by multiple contingencies. The method uses a search algorithm (Random Chemistry) to identify blackout-causing contingencies, and then combines the results with outage probabilities to estimate overall risk. Comparing this approach with Monte Carlo sampling for two test cases (the IEEE RTS-96 and a 2383-bus model of the Polish system) illustrates that the new approach is at least two orders of magnitude faster than Monte Carlo, without introducing measurable bias. Moreover, the approach enables one to compute the sensitivity of overall blackout risk to individual component-Failure probabilities in the initiating contingency, allowing one to quickly identify low-cost strategies for reducing risk. By computing the sensitivity of risk to individual initial outage probabilities for the Polish system, we found that reducing three line-outage probabilities by 50% would reduce Cascading Failure risk by 33%. Finally, we used the method to estimate changes in risk as a function of load. Surprisingly, this calculation illustrates that risk can sometimes decrease as load increases.
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estimating Cascading Failure risk with random chemistry
Power and Energy Society General Meeting, 2015Co-Authors: Pooya Rezaei, Paul Hines, Margaret J EppsteinAbstract:The potential for Cascading Failure in power systems adds substantially to overall reliability risk. Monte Carlo sampling can be used with a power system model to estimate this impact, but doing so is computationally expensive. This paper presents a new approach to estimating the risk of large Cascading blackouts triggered by multiple contingencies. The method uses a search algorithm (Random Chemistry) to identify blackout-causing contingencies, and then combines the results with outage probabilities to estimate overall risk. Comparing this approach with Monte Carlo sampling for two test cases (the IEEE RTS-96 and a 2383 bus model of the Polish grid) suggests that the new approach is at least two orders of magnitude faster than Monte Carlo, without introducing measurable bias. Moreover, the approach enables one to compute the sensitivity of overall blackout risk to individual component-Failure probabilities in the initiating contingency, allowing one to quickly identify low-cost strategies for reducing risk. By computing the sensitivity of risk to individual initial outage probabilities for the Polish system, we found that reducing three line-outage probabilities by 50% would reduce Cascading Failure risk by 33%. Finally, we used the method to estimate changes in risk as a function of load. Surprisingly, this calculation suggests that risk can sometimes decrease as load increases.
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a random chemistry algorithm for identifying collections of multiple contingencies that initiate Cascading Failure
Power and Energy Society General Meeting, 2013Co-Authors: Margaret J Eppstein, Paul HinesAbstract:Summary form only given. This paper describes a stochastic “Random Chemistry” (RC) algorithm to identify multiple (n-k) contingencies that initiate large Cascading Failures in a simulated power system. The method requires only O(log(n)) simulations per contingency identified, which is orders of magnitude faster than random search of this combinatorial space. We applied the method to a model of Cascading Failure in a power network with n=2896 branches and identify 148,243 unique, minimal n-k branch contingencies (2<;=k<;=5) that cause large cascades, many of which would be missed by using pre-contingency flows, linearized line outage distribution factors, or performance indices as screening factors. Within each n-k collection, the frequency with which individual branches appear follows a power-law (or nearly so) distribution, indicating that a relatively small number of components contribute disproportionately to system vulnerability. The paper discusses various ways that RC generated collections of dangerous contingencies could be used in power systems planning and operations.
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dual graph and random chemistry methods for Cascading Failure analysis
Hawaii International Conference on System Sciences, 2013Co-Authors: Paul D H Hines, Ian Dobson, Eduardo Cotillasanchez, Margaret J EppsteinAbstract:This paper describes two new approaches to Cascading Failure analysis in power systems that can combine large amounts of data about Cascading blackouts to produce information about the ways that cascades may propagate. In the first, we evaluate methods for representing Cascading Failure information in the form of a graph. We refer to these graphs as “dual graphs” because the vertices are the transmission lines (the physical links), rather than the more conventional approach of representing power system buses as vertices. Examples of these ideas using the IEEE 30 bus system indicate that the “dual graph” methods can provide useful insight into how cascades propagate. In the second part of the paper we describe a random chemistry algorithm that can search through the enormous space of possible combinations of potential component outages to efficiently find large collections of the most dangerous combinations. This method was applied to a power grid with 2896 transmission branches, and provides insight into component outages that are notably more likely than others to trigger a Cascading Failure. In the conclusions we discuss potential uses of these methods for power systems planning and operations.
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a random chemistry algorithm for identifying collections of multiple contingencies that initiate Cascading Failure
IEEE Transactions on Power Systems, 2012Co-Authors: Margaret J Eppstein, Paul HinesAbstract:This paper describes a stochastic “Random Chemistry” (RC) algorithm to identify large collections of multiple (n-k) contingencies that initiate large Cascading Failures in a simulated power system. The method requires only O(log (n)) simulations per contingency identified, which is orders of magnitude faster than random search of this combinatorial space. We applied the method to a model of Cascading Failure in a power network with n=2896 branches and identify 148243 unique, minimal n-k branch contingencies (2 ≤ k ≤ 5) that cause large cascades, many of which would be missed by using pre-contingency flows, linearized line outage distribution factors, or performance indices as screening factors. Within each n-k collection, the frequency with which individual branches appear follows a power-law (or nearly so) distribution, indicating that a relatively small number of components contribute disproportionately to system vulnerability. The paper discusses various ways that RC generated collections of dangerous contingencies could be used in power systems planning and operations.
Paul Hines - One of the best experts on this subject based on the ideXlab platform.
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benchmarking and validation of Cascading Failure analysis tools
IEEE Transactions on Power Systems, 2016Co-Authors: Janusz Bialek, Ian Dobson, Eduardo Cotillasanchez, E Ciapessoni, D Cirio, Chris Dent, Pierre Henneaux, Paul Hines, Jorge L Jardim, Stephen S MillerAbstract:Cascading Failure in electric power systems is a complicated problem for which a variety of models, software tools, and analytical tools have been proposed but are difficult to verify. Benchmarking and validation are necessary to understand how closely a particular modeling method corresponds to reality, what engineering conclusions may be drawn from a particular tool, and what improvements need to be made to the tool in order to reach valid conclusions. The community needs to develop the test cases tailored to Cascading that are central to practical benchmarking and validation. In this paper, the IEEE PES working group on Cascading Failure reviews and synthesizes how benchmarking and validation can be done for Cascading Failure analysis, summarizes and reviews the Cascading test cases that are available to the international community, and makes recommendations for improving the state of the art.
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estimating Cascading Failure risk with random chemistry
IEEE Transactions on Power Systems, 2015Co-Authors: Pooya Rezaei, Paul Hines, Margaret J EppsteinAbstract:The potential for Cascading Failure in power systems adds substantially to overall reliability risk. Monte Carlo sampling can be used with a power system model to estimate this impact, but doing so is computationally expensive. This paper presents a new approach to estimating the risk of large Cascading blackouts triggered by multiple contingencies. The method uses a search algorithm (Random Chemistry) to identify blackout-causing contingencies, and then combines the results with outage probabilities to estimate overall risk. Comparing this approach with Monte Carlo sampling for two test cases (the IEEE RTS-96 and a 2383-bus model of the Polish system) illustrates that the new approach is at least two orders of magnitude faster than Monte Carlo, without introducing measurable bias. Moreover, the approach enables one to compute the sensitivity of overall blackout risk to individual component-Failure probabilities in the initiating contingency, allowing one to quickly identify low-cost strategies for reducing risk. By computing the sensitivity of risk to individual initial outage probabilities for the Polish system, we found that reducing three line-outage probabilities by 50% would reduce Cascading Failure risk by 33%. Finally, we used the method to estimate changes in risk as a function of load. Surprisingly, this calculation illustrates that risk can sometimes decrease as load increases.
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estimating Cascading Failure risk with random chemistry
Power and Energy Society General Meeting, 2015Co-Authors: Pooya Rezaei, Paul Hines, Margaret J EppsteinAbstract:The potential for Cascading Failure in power systems adds substantially to overall reliability risk. Monte Carlo sampling can be used with a power system model to estimate this impact, but doing so is computationally expensive. This paper presents a new approach to estimating the risk of large Cascading blackouts triggered by multiple contingencies. The method uses a search algorithm (Random Chemistry) to identify blackout-causing contingencies, and then combines the results with outage probabilities to estimate overall risk. Comparing this approach with Monte Carlo sampling for two test cases (the IEEE RTS-96 and a 2383 bus model of the Polish grid) suggests that the new approach is at least two orders of magnitude faster than Monte Carlo, without introducing measurable bias. Moreover, the approach enables one to compute the sensitivity of overall blackout risk to individual component-Failure probabilities in the initiating contingency, allowing one to quickly identify low-cost strategies for reducing risk. By computing the sensitivity of risk to individual initial outage probabilities for the Polish system, we found that reducing three line-outage probabilities by 50% would reduce Cascading Failure risk by 33%. Finally, we used the method to estimate changes in risk as a function of load. Surprisingly, this calculation suggests that risk can sometimes decrease as load increases.
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a random chemistry algorithm for identifying collections of multiple contingencies that initiate Cascading Failure
Power and Energy Society General Meeting, 2013Co-Authors: Margaret J Eppstein, Paul HinesAbstract:Summary form only given. This paper describes a stochastic “Random Chemistry” (RC) algorithm to identify multiple (n-k) contingencies that initiate large Cascading Failures in a simulated power system. The method requires only O(log(n)) simulations per contingency identified, which is orders of magnitude faster than random search of this combinatorial space. We applied the method to a model of Cascading Failure in a power network with n=2896 branches and identify 148,243 unique, minimal n-k branch contingencies (2<;=k<;=5) that cause large cascades, many of which would be missed by using pre-contingency flows, linearized line outage distribution factors, or performance indices as screening factors. Within each n-k collection, the frequency with which individual branches appear follows a power-law (or nearly so) distribution, indicating that a relatively small number of components contribute disproportionately to system vulnerability. The paper discusses various ways that RC generated collections of dangerous contingencies could be used in power systems planning and operations.
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a random chemistry algorithm for identifying collections of multiple contingencies that initiate Cascading Failure
IEEE Transactions on Power Systems, 2012Co-Authors: Margaret J Eppstein, Paul HinesAbstract:This paper describes a stochastic “Random Chemistry” (RC) algorithm to identify large collections of multiple (n-k) contingencies that initiate large Cascading Failures in a simulated power system. The method requires only O(log (n)) simulations per contingency identified, which is orders of magnitude faster than random search of this combinatorial space. We applied the method to a model of Cascading Failure in a power network with n=2896 branches and identify 148243 unique, minimal n-k branch contingencies (2 ≤ k ≤ 5) that cause large cascades, many of which would be missed by using pre-contingency flows, linearized line outage distribution factors, or performance indices as screening factors. Within each n-k collection, the frequency with which individual branches appear follows a power-law (or nearly so) distribution, indicating that a relatively small number of components contribute disproportionately to system vulnerability. The paper discusses various ways that RC generated collections of dangerous contingencies could be used in power systems planning and operations.
Wenhu Tang - One of the best experts on this subject based on the ideXlab platform.
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Vulnerability Assessment for Power Transmission Lines under Typhoon Weather Based on a Cascading Failure State Transition Diagram
Energies, 2020Co-Authors: Guo Jun, Tao Feng, Zelin Cai, Xianglong Lian, Wenhu TangAbstract:The analysis of the fault propagation path of transmission lines and the method of identification of vulnerable lines during typhoon weather conditions is of great significance. In this context, this paper introduces the Failure probability model of transmission lines under such conditions by considering both wind speed and the load of the lines. The Monte Carlo simulation (MCS) and the DC model based on OPA are applied to simulate the Failure of transmission lines. The Cascading Failure state transition diagram (CFSTD) is proposed based on the Failure chains and the criticality ranking of nodes in CFSTD by the average weight coefficient (AWC) for identifying vulnerable lines of the power grid under such conditions. A new weight in CFSTD is proposed to describe the vulnerability of each line and a new resilience index is used to assess the impacts of a typhoon on the system. The proposed method is demonstrated by using the modified IEEE 118-bus test system. Results show that the method proposed in this paper can simulate the fault propagation path, and identify the critical components of power grid under a typhoon.
Bin Ran - One of the best experts on this subject based on the ideXlab platform.
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analysis of Cascading Failure induced by load fluctuation and robust station capacity assignment for metros
Transportmetrica, 2021Co-Authors: Yi Shen, Gang Ren, Bin RanAbstract:In this paper, we analyze metro Cascading Failure induced by load fluctuation and present a robust station capacity assignment method. A Cascading Failure model is proposed by considering system in...
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Cascading Failure analysis and robustness optimization of metro networks based on coupled map lattices a case study of nanjing china
Transportation, 2021Co-Authors: Yi Shen, Gang Ren, Bin RanAbstract:Cascading Failure in metro networks is a dynamic chain process induced by the interaction of passenger flow and network topology. In this paper, a bi-directional coupled map lattice model is proposed to study the Cascading Failure of metro networks. The model considers the two-way traffic problem, and the results are closer to those of actual metro networks than the previous one-way coupling models. A $$\eta$$ -based flow redistribution method is proposed, and different passenger flow redistribution strategies after station Failure can be achieved by changing the flow redistribution coefficient $$\eta$$ from 0 to 1. Moreover, the robustness of metro networks can be optimized by searching for the optimal $$\eta$$ that can maximize the critical perturbation leading to global network Failure. We study the actual case of Nanjing metro. The analysis results show that the network is more vulnerable to intentional attacks than to random Failures, and global network Failure is triggered more easily on the largest strength station than on the stations with the largest betweenness and largest degree. The influence of coupling strengths on the critical perturbation is also investigated. The results show that larger coupling strengths correspond to smaller critical perturbations, but a change in the coupling strengths has a small impact on the optimal $$\eta$$ . Under the given traffic data, the optimal $$\eta$$ for Nanjing metro is approximately in the range (0.3, 0.4). This study provides a reference for developing strategies for dynamic safety evaluation and emergency management of passenger flow in metro networks.