The Experts below are selected from a list of 21768 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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extracting resilience metrics from distribution utility data using outage and restore process statistics
IEEE Transactions on Power Systems, 2021Co-Authors: Nichellele K Carrington, Ian Dobson, Zhaoyu WangAbstract:Resilience curves track the accumulation and restoration of Outages during an event on an electric distribution grid. We show that a resilience curve generated from utility data can always be decomposed into an outage process and a restore process and that these processes generally overlap in time. We use many events in real utility data to characterize the statistics of these processes, and derive formulas based on these statistics for resilience metrics such as restore duration, customer hours not served, and outage and restore rates. The formulas express the mean value of these metrics as a function of the number of Outages in the event. We also give a formula for the variability of restore duration, which allows us to predict a maximum restore duration with 95\% confidence. Overall, we give a simple and general way to decompose resilience curves into outage and restore processes and then show how to use these processes to extract resilience metrics from standard distribution system data.
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Applying Bayesian estimates of individual transmission line outage rates
2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2020Co-Authors: Kai Zhou, Ian Dobson, Zhaoyu Wang, James R. Cruise, Chris J. I Kill, Louis Wehenkel, Amy WilsonAbstract:Despite the important role transmission line Outages play in power system reliability analysis, it remains a challenge to estimate individual line outage rates accurately enough from limited data. Recent work using a Bayesian hierarchical model shows how to combine together line outage data by exploiting how the lines partially share some common features in order to obtain more accurate estimates of line outage rates. Lower variance estimates from fewer years of data can be obtained. In this paper, we explore what can be achieved with this new Bayesian hierarchical approach using real utility data. In particular, we assess the capability to detect increases in line outage rates over time, quantify the influence of bad weather on outage rates, and discuss the effect of outage rate uncertainty on a simple availability calculation.
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exploring cascading Outages and weather via processing historic data
arXiv: Physics and Society, 2017Co-Authors: Ian Dobson, Benjamin A. Carreras, Zhaoyu Wang, Nichellele K Carrington, Kai Zhou, J M ReynoldsbarredoAbstract:We describe some bulk statistics of historical initial line Outages and the implications for forming contingency lists and understanding which initial Outages are likely to lead to further cascading. We use historical outage data to estimate the effect of weather on cascading via cause codes and via NOAA storm data. Bad weather significantly increases outage rates and interacts with cascading effects, and should be accounted for in cascading models and simulations. We suggest how weather effects can be incorporated into the OPA cascading simulation and validated. There are very good prospects for improving data processing and models for the bulk statistics of historical outage data so that cascading can be better understood and quantified.
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cascading power Outages propagate locally in an influence graph that is not the actual grid topology
Power and Energy Society General Meeting, 2017Co-Authors: Paul D H Hines, Ian Dobson, Pooya RezaeiAbstract:In a cascading power transmission outage, component Outages propagate non-locally; after one component Outages, the next failure may be very distant, both topologically and geographically. As a result, simple models of topological contagion do not accurately represent the propagation of cascades in power systems. However, cascading power Outages do follow patterns, some of which are useful in understanding and reducing blackout risk. This paper describes a method by which the data from many cascading failure simulations can be transformed into a graph-based model of influences that provides actionable information about the many ways that cascades propagate in a particular system. The resulting “influence graph” model is Markovian, in that component outage probabilities depend only on the Outages that occurred in the prior generation. To validate the model we compare the distribution of cascade sizes resulting from n-2 contingencies in a 2896 branch test case to cascade sizes in the influence graph. The two distributions are remarkably similar. In addition, we derive an equation with which one can quickly identify modifications to the proposed system that will substantially reduce cascade propagation. With this equation one can quickly identify critical components that can be improved to substantially reduce the risk of large cascading blackouts.
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obtaining statistics of cascading line Outages spreading in an electric transmission network from standard utility data
IEEE Transactions on Power Systems, 2016Co-Authors: Ian Dobson, Benjamin A. Carreras, D E Newman, J M ReynoldsbarredoAbstract:We show how to use standard transmission line outage historical data to obtain the network topology in such a way that cascades of line Outages can be easily located on the network. Then we obtain statistics quantifying how cascading Outages typically spread on the network. Processing real outage data is fundamental for understanding cascading and for evaluating the validity of the many different models and simulations that have been proposed for cascading in power networks.
Benjamin A. Carreras - One of the best experts on this subject based on the ideXlab platform.
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exploring cascading Outages and weather via processing historic data
arXiv: Physics and Society, 2017Co-Authors: Ian Dobson, Benjamin A. Carreras, Zhaoyu Wang, Nichellele K Carrington, Kai Zhou, J M ReynoldsbarredoAbstract:We describe some bulk statistics of historical initial line Outages and the implications for forming contingency lists and understanding which initial Outages are likely to lead to further cascading. We use historical outage data to estimate the effect of weather on cascading via cause codes and via NOAA storm data. Bad weather significantly increases outage rates and interacts with cascading effects, and should be accounted for in cascading models and simulations. We suggest how weather effects can be incorporated into the OPA cascading simulation and validated. There are very good prospects for improving data processing and models for the bulk statistics of historical outage data so that cascading can be better understood and quantified.
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obtaining statistics of cascading line Outages spreading in an electric transmission network from standard utility data
IEEE Transactions on Power Systems, 2016Co-Authors: Ian Dobson, Benjamin A. Carreras, D E Newman, J M ReynoldsbarredoAbstract:We show how to use standard transmission line outage historical data to obtain the network topology in such a way that cascades of line Outages can be easily located on the network. Then we obtain statistics quantifying how cascading Outages typically spread on the network. Processing real outage data is fundamental for understanding cascading and for evaluating the validity of the many different models and simulations that have been proposed for cascading in power networks.
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Number and propagation of line Outages in cascading events in electric power transmission systems
2010 48th Annual Allerton Conference on Communication Control and Computing Allerton 2010, 2010Co-Authors: Ian Dobson, Benjamin A. CarrerasAbstract:Large blackouts typically involve the cascading outage of transmission lines. We estimate from observed utility data how transmission line Outages propagate, and obtain parameters of a branching process model of the propagation. We show that the branching process model is consistent with the utility data by using it to estimate the distribution of the total number of lines Outages and showing that this closely matches the distribution of the total number of line Outages observed in the utility data. The branching process model and the measured propagation can then be applied to predict the distribution of total number of Outages for a given number of initial failures. We study how the total number of lines Outages depends on the amounts of propagation as the cascade progresses. The analysis gives a new way to quantify the effect of cascading failure from standard utility data about automatic line Outages.
Sanjoy Das - One of the best experts on this subject based on the ideXlab platform.
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estimating animal related Outages on overhead distribution feeders using boosting
IFAC-PapersOnLine, 2015Co-Authors: Padmavathy Kankanala, Anil Pahwa, Sanjoy DasAbstract:Abstract Faults on overhead distribution feeders have significant impact on the distribution reliability. Literature review on Outages shows that overhead lines are highly susceptible to environmental factors such as weather, trees and animal. Historical analysis of outage data recorded by utility in Kansas showed that the occurrences of animal-caused Outages are dependent on weather conditions and time of the year. This paper proposes models based on neural network combined with boosting algorithms based on AdaBoost to estimate weekly animal-related Outages. Effectiveness of the proposed models is evaluated using actual data for four cities of different sizes in Kansas. Performance of the proposed models are compared with each other and with neural network without boosting, and previously implemented models. The results clearly show that boosting reduces mean square error and mean absolute error, and increases correlation between the estimated Outages and observed Outages. Additionally, AdaBoost+ performs better than AdaBoost.RT with lower errors and higher correlations.
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bayesian network model with monte carlo simulations for analysis of animal related Outages in overhead distribution systems
IEEE Transactions on Power Systems, 2011Co-Authors: Min Gui, Anil Pahwa, Sanjoy DasAbstract:This paper extends previous research on using a Bayesian network model to investigate impacts of time (month) and weather (number of fair weather days in a week) on animal-related Outages in distribution systems. Outage history (Outages in the previous week) is included as an additional input to the model, and inputs and outputs are classified systematically to reduce errors in estimates of outputs. Conditional probability table obtained from the historical data are used to estimate weekly animal-related Outages which is followed by a Monte Carlo simulation to find estimates of mean and confidence limits for monthly animal-related Outages. Comparison of results obtained for four cities of different sizes in Kansas with those obtained using a hybrid wavelet/neural network model shows consistency between the two models. The methodology presented in this paper is simple to implement and useful for the utilities for year-end analysis of the outage data to identify specific reliability-related concerns.
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classification of input and output variables for a bayesian model to analyze animal related Outages in overhead distribution systems
IEEE International Conference on Probabilistic Methods Applied to Power Systems, 2010Co-Authors: Min Gui, Anil Pahwa, Sanjoy DasAbstract:Animals, such as squirrels, cause significant Outages in overhead distribution systems. Models that would accurately estimate Outages caused by animals would be very useful for utilities for year-end analysis of reliability performance of the distribution system. Large increase in Outages caused by animals would require the utility to do further evaluation and take remedial actions. A two-layer Bayesian network model with Month-Type, Level of Fair Weather Days in the week, and Outage Level in the previous week as input and Outage Level in the week is presented in this paper for estimation of weekly animal-related Outages. Results of different approaches for classification of inputs and output are presented, which are then compared to select the best classification of input and output variables for the model.
Steven M. Quiring - One of the best experts on this subject based on the ideXlab platform.
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predicting thunderstorm induced power Outages to support utility restoration
IEEE Transactions on Power Systems, 2019Co-Authors: Elnaz Kabir, Seth D Guikema, Steven M. QuiringAbstract:Strong thunderstorms have substantial impacts on power systems, posing risks and inconveniences due to power Outages. Developing models predicting the Outages before a storm is a high priority to support restoration planning. However, most power outage data are zero-inflated, which results in some challenges in predictive modeling such as bias and inaccuracy. Power Outages are also stochastic and there always exists irreducible variability in outage predictions. The goal is to develop models to overcome the challenges caused by zero-inflation and to accurately estimate power Outages in terms of probability distributions to better address inherent stochasticity and uncertainty in predictions. This paper proposes a novel approach integrating mixture models with resampling and cost-sensitive learning for predicting the probability distribution for the number of Outages. Validating the models using power outage data, we demonstrate that our approach offers more accurate point and probabilistic predictions compared to traditional approaches, better supporting utility restoration planning.
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predicting hurricane power Outages to support storm response planning
IEEE Access, 2014Co-Authors: Seth D Guikema, Roshanak Nateghi, Steven M. Quiring, Andrea Staid, Allison C Reilly, Michael GaoAbstract:Hurricanes regularly cause widespread and prolonged power Outages along the U.S. coastline. These power Outages have significant impacts on other infrastructure dependent on electric power and on the population living in the impacted area. Efficient and effective emergency response planning within power utilities, other utilities dependent on electric power, private companies, and local, state, and federal government agencies benefit from accurate estimates of the extent and spatial distribution of power Outages in advance of an approaching hurricane. A number of models have been developed for predicting power Outages in advance of a hurricane, but these have been specific to a given utility service area, limiting their use to support wider emergency response planning. In this paper, we describe the development of a hurricane power outage prediction model applicable along the full U.S. coastline using only publicly available data, we demonstrate the use of the model for Hurricane Sandy, and we use the model to estimate what the impacts of a number of historic storms, including Typhoon Haiyan, would be on current U.S. energy infrastructure.
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Power Outage Estimation for Tropical Cyclones: Improved Accuracy with Simpler Models
Risk Analysis, 2014Co-Authors: Roshanak Nateghi, Seth D Guikema, Steven M. QuiringAbstract:In this article, we discuss an outage-forecasting model that we have developed. This model uses very few input variables to estimate hurricane-induced Outages prior to landfall with great predictive accuracy. We also show the results for a series of simpler models that use only publicly available data and can still estimate Outages with reasonable accuracy. The intended users of these models are emergency response planners within power utilities and related government agencies. We developed our models based on the method of random forest, using data from a power distribution system serving two states in the Gulf Coast region of the United States. We also show that estimates of system reliability based on wind speed alone are not sufficient for adequately capturing the reliability of system components. We demonstrate that a multivariate approach can produce more accurate power outage predictions.
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estimating the spatial distribution of power Outages during hurricanes in the gulf coast region
Reliability Engineering & System Safety, 2009Co-Authors: Seth D Guikema, David V Rosowsky, Steven M. Quiring, Rachel A DavidsonAbstract:Hurricanes have caused severe damage to the electric power system throughout the Gulf coast region of the US, and electric power is critical to post-hurricane disaster response as well as to long-term recovery for impacted areas. Managing power outage risk and preparing for post-storm recovery efforts requires accurate methods for estimating the number and location of power Outages. This paper builds on past work on statistical power outage estimation models to develop, test, and demonstrate a statistical power outage risk estimation model for the Gulf Coast region of the US. Previous work used binary hurricane-indicator variables representing particular hurricanes in order to achieve a good fit to the past data. To use these models for predicting power Outages during future hurricanes, one must implicitly assume that an approaching hurricane is similar to the average of the past hurricanes. The model developed in this paper replaces these indicator variables with physically measurable variables, enabling future predictions to be based on only well-understood characteristics of hurricanes. The models were developed using data about power Outages during nine hurricanes in three states served by a large, investor-owned utility company in the Gulf Coast region.
Kai Sun - One of the best experts on this subject based on the ideXlab platform.
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management of cascading outage risk based on risk gradient and markovian tree search
IEEE Transactions on Power Systems, 2018Co-Authors: Rui Yao, Kai Sun, Feng Liu, Shengwei MeiAbstract:Since cascading Outages are major threats to power systems, it is important to reduce the risk of potential cascading Outages. In this paper, a risk management method of cascading Outages based on Markovian tree search is proposed. With the tree expansion on the cascading outage risk, risk gradient is computed efficiently by a forward–backward tree search scheme with good convergence, and it is then employed in an optimization model to minimize control cost while effectively reducing the cascading outage risk. To overcome the limitation with linearization in computing risk gradient, an iterative risk management (IRM) approach is further developed. Tests on the RTS-96 3-area system verify the accuracy of the computed risk gradient and its effectiveness for risk reduction. Time performance of the proposed IRM approach is tested on the RTS-96 system, a 410-bus U.S.–Canada northeast system, and a 1354-bus mid-European system, and demonstrates its potentials for decision support on practical power systems online or on hourly basis.
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simulation of cascading Outages using a power flow model considering frequency
IEEE Access, 2018Co-Authors: Kai Sun, Rui YaoAbstract:Many steady-state power-flow-based models for cascading outage simulation have not considered frequency, which, however, is an important indicator of generation-load imbalance. This paper proposes a novel steady-state approach for simulating cascading Outages. The approach employs a power-flow-based model that considers static power-frequency characteristics of both generators and loads. Thus, the frequency deviation due to active power imbalance can be calculated under cascading Outages. Furthermore, a new ac optimal power-flow model considering frequency deviation is proposed to simulate the remedial control when system collapse happens as indicated by the divergence of power flows. Case studies first benchmark the steady-state frequency calculated by the power-flow-based model with time-domain simulation results on a two-area power system, and then test the proposed approach for simulation of cascading Outages on the IEEE 39-bus system and an NPCC 48-machine 140-bus power system. The test results are compared with a traditional frequency-independent approach using the conventional power flow and ac optimal power-flow models, and verify that by capturing frequency variations under cascading Outages, the proposed approach can more accurately simulate the mechanism of outage propagation.
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a multi timescale quasi dynamic model for simulation of cascading Outages
Power and Energy Society General Meeting, 2017Co-Authors: Rui Yao, Shaowei Huang, Kai Sun, Feng Liu, Xuemin Zhang, Shengwei MeiAbstract:Many blackouts in electric power grids throughout the world are caused by cascading Outages, which often involve complex processes in various timescales. The multi-timescale nature of cascading Outages makes conventional quasi-static simulation methods inaccurate in characterizing actual evolution of Outages. This paper proposes a multi-timescale cascading outage model using a quasi-dynamic simulation method. The model establishes a framework for simulating interactions among dynamics in quite different timescales. It realizes simulation of cascading Outages with representation of time evolution, so it overcomes ambiguity of time in conventional cascading outage models and hence has better practicality. Moreover, the model considers dynamics, e.g. load variation and generator excitation protection which affect voltage and reactive power profiles. Also, an improved re-dispatch model based on sensitivity is proposed. These improvements facilitate better simulation for a realistic power system. Also, dynamic simulation can be flexibly incorporated into the simulation of short-term processes in this model as needed. Case studies with the proposed multi-timescale model on the IEEE 30-bus system discuss the role of generator protection in cascading outage evolution, and analyze stage characteristics in Outages. The multi-timescale model is also demonstrated on a reduced 410-bus US-Canada northeast power grid. Moreover, impacts from dispatchers' involvements are analyzed.
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estimating the propagation of interdependent cascading Outages with multi type branching processes
IEEE Transactions on Power Systems, 2017Co-Authors: Kai SunAbstract:In this paper, the multitype branching process is applied to describe the statistics and interdependencies of line Outages, the load shed, and isolated buses. The offspring mean matrix of the multitype branching process is estimated by the Expectation Maximization (EM) algorithm and can quantify the extent of outage propagation. The joint distribution of two types of Outages is estimated by the multitype branching process via the Lagrange-Good inversion. The proposed model is tested with data generated by the AC OPA cascading simulations on the IEEE 118-bus system. The largest eigenvalues of the offspring mean matrix indicate that the system is closer to criticality when considering the interdependence of different types of Outages. Compared with empirically estimating the joint distribution of the total Outages, good estimate is obtained by using the multitype branching process with a much smaller number of cascades, thus greatly improving the efficiency. It is shown that the multitype branching process can effectively predict the distribution of the load shed and isolated buses and their conditional largest possible total Outages even when there are no data of them.
