Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183681
Marie-Louise Kloubert
Uncertainties in the electricity grid grow, so the need for alternatives to deterministic load flow approaches come up. The Point Estimate Method (PEM) as an approximate probabilistic load flow method calculates the statistical moments of the output variables using the statistical moments of the input variables. Afterwards, the probability density functions (PDF) and cumulative density functions (CDF) are determined using expansion methods. Due to the combination of different renewable energy sources (RES) at the same grid node, correlated multimodally distributed input variables may result. An enhancement to the two-PEM (2m-PEM) and expansion method in order to consider correlated multimodally distributed input variables is presented. The new method consists of a sensitivity analysis and a modified 2m-PEM to be applicable for large grids with multiple multimodal distributed variables. The proposed algorithm is demonstrated in a test grid and verified through the comparison of the results using Monte Carlo Simulation (MCS) as reference method.
{"title":"Fast Point Estimate Method for Correlated Multimodally Distributed Input Variables","authors":"Marie-Louise Kloubert","doi":"10.1109/PMAPS47429.2020.9183681","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183681","url":null,"abstract":"Uncertainties in the electricity grid grow, so the need for alternatives to deterministic load flow approaches come up. The Point Estimate Method (PEM) as an approximate probabilistic load flow method calculates the statistical moments of the output variables using the statistical moments of the input variables. Afterwards, the probability density functions (PDF) and cumulative density functions (CDF) are determined using expansion methods. Due to the combination of different renewable energy sources (RES) at the same grid node, correlated multimodally distributed input variables may result. An enhancement to the two-PEM (2m-PEM) and expansion method in order to consider correlated multimodally distributed input variables is presented. The new method consists of a sensitivity analysis and a modified 2m-PEM to be applicable for large grids with multiple multimodal distributed variables. The proposed algorithm is demonstrated in a test grid and verified through the comparison of the results using Monte Carlo Simulation (MCS) as reference method.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123581581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183648
Harish Doddi, Deepjyoti Deka, M. Salapaka
This article analyzes statistical learning methods to identify the topology of meshed power distribution grids under partial observability. The learning algorithms use properties of the probability distribution of nodal voltages collected at the observed nodes. Unlike prior work on learning under partial observability, this work does not presume radial structure of the grid, and furthermore does not use injection measurements at any node. To the best of our knowledge, this is the first work for topology recovery in partially observed distribution grids, that uses voltage measurements alone. The developed learning algorithms are validated with non-linear power flow samples generated by Matpower in test grids.
{"title":"Learning partially observed meshed distribution grids","authors":"Harish Doddi, Deepjyoti Deka, M. Salapaka","doi":"10.1109/PMAPS47429.2020.9183648","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183648","url":null,"abstract":"This article analyzes statistical learning methods to identify the topology of meshed power distribution grids under partial observability. The learning algorithms use properties of the probability distribution of nodal voltages collected at the observed nodes. Unlike prior work on learning under partial observability, this work does not presume radial structure of the grid, and furthermore does not use injection measurements at any node. To the best of our knowledge, this is the first work for topology recovery in partially observed distribution grids, that uses voltage measurements alone. The developed learning algorithms are validated with non-linear power flow samples generated by Matpower in test grids.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115667024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183469
Wei Wang, N. Yu
Covered conductors are widely adopted in the medium to low voltage systems to prevent faults and ignitions from events such vegetation contacting with distribution lines and conductors slapping together. However, such events could cause partial discharge in deteriorated insulation system of covered conductors and ultimately lead to failure and ignition. To prevent power outages and wildfires, it is crucial to detect partial discharges of overhead power lines and perform predictive maintenance. In this paper, we develop advanced machine learning algorithms to detect partial discharge by using measurements from high frequency voltage sensors. Our innovative approach synergistically combines the merits of spectrogram feature extraction and deep convolutional neural networks. The proposed algorithms are validated using real-world noisy voltage measurements. Compared to the benchmark, our approach achieves notably better performance. Furthermore, the classification results from the neural networks are interpreted with an occlusion map, which identifies suspicious time intervals when partial discharges occur.
{"title":"Partial Discharge Detection with Convolutional Neural Networks","authors":"Wei Wang, N. Yu","doi":"10.1109/PMAPS47429.2020.9183469","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183469","url":null,"abstract":"Covered conductors are widely adopted in the medium to low voltage systems to prevent faults and ignitions from events such vegetation contacting with distribution lines and conductors slapping together. However, such events could cause partial discharge in deteriorated insulation system of covered conductors and ultimately lead to failure and ignition. To prevent power outages and wildfires, it is crucial to detect partial discharges of overhead power lines and perform predictive maintenance. In this paper, we develop advanced machine learning algorithms to detect partial discharge by using measurements from high frequency voltage sensors. Our innovative approach synergistically combines the merits of spectrogram feature extraction and deep convolutional neural networks. The proposed algorithms are validated using real-world noisy voltage measurements. Compared to the benchmark, our approach achieves notably better performance. Furthermore, the classification results from the neural networks are interpreted with an occlusion map, which identifies suspicious time intervals when partial discharges occur.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124744756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183449
K. Kamps, F. Möhrke, M. Zdrallek, P. Awater, M. Schwan
Due to the growth of distributed generation and the changes of consumption behavior (e. g. induced by electromobility), the need for cost-efficient and reliable smart grid technologies in medium and low-voltage networks increases. A decentralized network automation system is a smart grid technology that relies on comprehensive information and communication technologies. This enables the monitoring of a network state in real time and the subsequent control of active network participants (e. g. distributed generators) in critical situations. When making an investment decision, it is crucial to assess the reliability of this system and to evaluate the impact on distribution network reliability. In order to be able to assess the reliability of these systems, the reliability analysis is enhanced by the specifications of information and communication technologies. In this contribution, the analytical method of minimal cut sets is used for this purpose. As a result, the state probabilities and transition rates of the presented three-state Markov model for decentralized network automation systems are determined. Moreover, the reliability calculation of an electrical power system is enhanced by the functionalities of a decentralized network automation system. This includes power curtailment, fault detection, fault isolation and recovery techniques. The resulting impacts of these enhancements on customer- and distributed-generator-oriented reliability indices are illustrated and discussed for an exemplary medium-voltage network.
{"title":"Reliability of Decentralized Network Automation Systems and Impacts on Distribution Network Reliability","authors":"K. Kamps, F. Möhrke, M. Zdrallek, P. Awater, M. Schwan","doi":"10.1109/PMAPS47429.2020.9183449","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183449","url":null,"abstract":"Due to the growth of distributed generation and the changes of consumption behavior (e. g. induced by electromobility), the need for cost-efficient and reliable smart grid technologies in medium and low-voltage networks increases. A decentralized network automation system is a smart grid technology that relies on comprehensive information and communication technologies. This enables the monitoring of a network state in real time and the subsequent control of active network participants (e. g. distributed generators) in critical situations. When making an investment decision, it is crucial to assess the reliability of this system and to evaluate the impact on distribution network reliability. In order to be able to assess the reliability of these systems, the reliability analysis is enhanced by the specifications of information and communication technologies. In this contribution, the analytical method of minimal cut sets is used for this purpose. As a result, the state probabilities and transition rates of the presented three-state Markov model for decentralized network automation systems are determined. Moreover, the reliability calculation of an electrical power system is enhanced by the functionalities of a decentralized network automation system. This includes power curtailment, fault detection, fault isolation and recovery techniques. The resulting impacts of these enhancements on customer- and distributed-generator-oriented reliability indices are illustrated and discussed for an exemplary medium-voltage network.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126329795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183525
D. Fang, M. Zou, G. Harrison, S. Djokic, M. Ndawula, X. Xu, I. Hernando‐Gil, J. Gunda
This paper evaluates deterministic and probabilistic approaches for assessing hosting capacity (HC) of distribution networks for wind-based distributed generation (DG). The presented methodology considers variations of demands and DG power outputs, as well as dynamic thermal ratings (DTR) of network components. Deterministic approaches are based on a limited number of scenarios with minimum and maximum demands and DTR limits, while probabilistic approaches use simultaneous hourly values of all input parameters. The presented methodology has three stages. First, locational HC (LHC) of individual buses is calculated assuming connection of a single DG unit in the considered network. Afterwards, the LHC results are used to calculate network HC (NHC), assuming that DG units are connected at all network buses. Finally, busto-bus LHC-sensitivity factors are used to determine LHC and NHC for any number of DG units connected at arbitrary network buses.
{"title":"Deterministic and Probabilistic Assessment of Distribution Network Hosting Capacity for Wind-Based Renewable Generation","authors":"D. Fang, M. Zou, G. Harrison, S. Djokic, M. Ndawula, X. Xu, I. Hernando‐Gil, J. Gunda","doi":"10.1109/PMAPS47429.2020.9183525","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183525","url":null,"abstract":"This paper evaluates deterministic and probabilistic approaches for assessing hosting capacity (HC) of distribution networks for wind-based distributed generation (DG). The presented methodology considers variations of demands and DG power outputs, as well as dynamic thermal ratings (DTR) of network components. Deterministic approaches are based on a limited number of scenarios with minimum and maximum demands and DTR limits, while probabilistic approaches use simultaneous hourly values of all input parameters. The presented methodology has three stages. First, locational HC (LHC) of individual buses is calculated assuming connection of a single DG unit in the considered network. Afterwards, the LHC results are used to calculate network HC (NHC), assuming that DG units are connected at all network buses. Finally, busto-bus LHC-sensitivity factors are used to determine LHC and NHC for any number of DG units connected at arbitrary network buses.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126210609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183536
Milad Kabirifar, M. Fotuhi‐Firuzabad, M. Moeini‐Aghtaie, Niloofar Pourghaderi, M. Lehtonen
In this paper the reliability of distribution network is modeled in joint multistage expansion planning of distribution network assets and distributed generations (DGs). The imposed costs due to network reliability weakness are considerable in the distribution level. Therefore in the proposed model distribution network operator (DNO) considers the costs associated with load interruptions in the planning problem. In this regard, reliability evaluation of the network is modeled in the joint multistage distribution network expansion planning (MDNEP) problem in an integrated manner while the network topology is unknown until the planning problem is not solved. In the proposed joint MDNEP problem the investment plan of network assets including feeders, substations and transformers as well as DGs are jointly obtained. Involving the reliability costs in the joint MDNEP problem is based on linearized mathematical model for calculating reliability index of expected energy not served (EENS). Therefore the proposed model is formulated in the form of mixed integer linear programming (MILP) which can be efficiently solved using off-the-shelf software.
{"title":"Reliability based Joint Distribution Network and Distributed Generation Expansion Planning","authors":"Milad Kabirifar, M. Fotuhi‐Firuzabad, M. Moeini‐Aghtaie, Niloofar Pourghaderi, M. Lehtonen","doi":"10.1109/PMAPS47429.2020.9183536","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183536","url":null,"abstract":"In this paper the reliability of distribution network is modeled in joint multistage expansion planning of distribution network assets and distributed generations (DGs). The imposed costs due to network reliability weakness are considerable in the distribution level. Therefore in the proposed model distribution network operator (DNO) considers the costs associated with load interruptions in the planning problem. In this regard, reliability evaluation of the network is modeled in the joint multistage distribution network expansion planning (MDNEP) problem in an integrated manner while the network topology is unknown until the planning problem is not solved. In the proposed joint MDNEP problem the investment plan of network assets including feeders, substations and transformers as well as DGs are jointly obtained. Involving the reliability costs in the joint MDNEP problem is based on linearized mathematical model for calculating reliability index of expected energy not served (EENS). Therefore the proposed model is formulated in the form of mixed integer linear programming (MILP) which can be efficiently solved using off-the-shelf software.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125799006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183474
Dogan Urgun, C. Singh
This paper proposes a new algorithm for evaluation of power systems reliability based on Artificial Intelligence. This algorithm proposes an efficient technique to gather training samples and training Convolutional Neural Networks (CNN) for computing power system reliability indices considering changes in system parameters. It is shown that the computational efficiency gained by machine learning can be increased even further by reducing the time required for collecting training samples and applying transfer learning. Three different modifications of IEEE Reliability Test System (IEEE-RTS) are used to show the performance of proposed method during changes in system. The results of case studies show that CNNs together with the proposed algorithm provide a good classification accuracy while reducing computation time.
{"title":"Composite System Reliability Analysis using Deep Learning enhanced by Transfer Learning","authors":"Dogan Urgun, C. Singh","doi":"10.1109/PMAPS47429.2020.9183474","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183474","url":null,"abstract":"This paper proposes a new algorithm for evaluation of power systems reliability based on Artificial Intelligence. This algorithm proposes an efficient technique to gather training samples and training Convolutional Neural Networks (CNN) for computing power system reliability indices considering changes in system parameters. It is shown that the computational efficiency gained by machine learning can be increased even further by reducing the time required for collecting training samples and applying transfer learning. Three different modifications of IEEE Reliability Test System (IEEE-RTS) are used to show the performance of proposed method during changes in system. The results of case studies show that CNNs together with the proposed algorithm provide a good classification accuracy while reducing computation time.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129496782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183670
Gonca Gürses-Tran, Hendrik Flamme, A. Monti
Short-term load forecasting is typically used by electricity market participants to optimize their trading decisions and by system operators to ensure reliable grid operation. In particular, it allows the latter to foresee potential power imbalances and other critical grid states and thereafter, to enforce appropriate mitigation actions. Especially, forecasting critical grid states such as congestions, plays an essential role in this context. This paper proposes a recurrent neural network that is trained to forecast day-ahead time-series and prediction intervals for residual loads. Moreover, a comprehensive overview on probabilistic evaluation metrics is given. The ignorance score and the quantile score are used during the training whereas other metrics are for evaluation as they facilitate comparability between the different forecasting approaches with the naive baselines. The proposed deep learning model can be both specified as a parametric or as a non-parametric model and delivers reliable forecasts for day-ahead purposes.
{"title":"Probabilistic Load Forecasting for Day-Ahead Congestion Mitigation","authors":"Gonca Gürses-Tran, Hendrik Flamme, A. Monti","doi":"10.1109/PMAPS47429.2020.9183670","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183670","url":null,"abstract":"Short-term load forecasting is typically used by electricity market participants to optimize their trading decisions and by system operators to ensure reliable grid operation. In particular, it allows the latter to foresee potential power imbalances and other critical grid states and thereafter, to enforce appropriate mitigation actions. Especially, forecasting critical grid states such as congestions, plays an essential role in this context. This paper proposes a recurrent neural network that is trained to forecast day-ahead time-series and prediction intervals for residual loads. Moreover, a comprehensive overview on probabilistic evaluation metrics is given. The ignorance score and the quantile score are used during the training whereas other metrics are for evaluation as they facilitate comparability between the different forecasting approaches with the naive baselines. The proposed deep learning model can be both specified as a parametric or as a non-parametric model and delivers reliable forecasts for day-ahead purposes.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128043757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183415
Michael Abdelmalak, M. Benidris
This paper proposes a Generalized Polynomial Chaos (gPC)-based approach to determine sizes of Virtual Synchronous Generator (VSG) units to enhance the dynamic performance of power systems. With the high integration of renewable energy sources, distributed generators, and energy storage units, the overall system inertial level has reduced. VSGs have the potential to compensate for the reduced inertia and enhance stability margins of electric power systems. On the other hand, determining the minimum sizes of VSGs units under several system uncertainties is challenging and requires advanced stochastic approaches. Monte Carlo simulation and Perturbation techniques have been used for a long time to quantify impacts of stochastic variables on power systems. These approaches are computationally involved especially for large systems. The gPC-based method provides a faster and efficient method to quantify uncertainties in various power system problems where the behavior of random variables is represented as a series of orthogonal polynomials that can be easily evaluated. In the proposed approach, the time domain simulation approach for multi-machine systems is integrated with the gPC to estimate the sizes of VSG units under various failure conditions. The proposed method is demonstrated on the reduced WECC-9 bus system. The results are compared with Monte Carlo simulation to validate the accuracy and efficiency of gPC.
{"title":"A Polynomial Chaos-based Approach to Sizing of Virtual Synchronous Generators","authors":"Michael Abdelmalak, M. Benidris","doi":"10.1109/PMAPS47429.2020.9183415","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183415","url":null,"abstract":"This paper proposes a Generalized Polynomial Chaos (gPC)-based approach to determine sizes of Virtual Synchronous Generator (VSG) units to enhance the dynamic performance of power systems. With the high integration of renewable energy sources, distributed generators, and energy storage units, the overall system inertial level has reduced. VSGs have the potential to compensate for the reduced inertia and enhance stability margins of electric power systems. On the other hand, determining the minimum sizes of VSGs units under several system uncertainties is challenging and requires advanced stochastic approaches. Monte Carlo simulation and Perturbation techniques have been used for a long time to quantify impacts of stochastic variables on power systems. These approaches are computationally involved especially for large systems. The gPC-based method provides a faster and efficient method to quantify uncertainties in various power system problems where the behavior of random variables is represented as a series of orthogonal polynomials that can be easily evaluated. In the proposed approach, the time domain simulation approach for multi-machine systems is integrated with the gPC to estimate the sizes of VSG units under various failure conditions. The proposed method is demonstrated on the reduced WECC-9 bus system. The results are compared with Monte Carlo simulation to validate the accuracy and efficiency of gPC.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134449318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183492
Kai Zhou, I. Dobson, Zhaoyu Wang
It is challenging to simulate the cascading line outages that can follow initial damage to the electric power transmission system from extreme events. Instead of model-based simulation, we propose using a Markovian influence graph driven by historical utility data to sample the cascades. The sampling method encompasses the rare, large cascades that contribute greatly to the blackout risk. This suggested new approach contributes a high-level simulation of cascading line outages that is driven by standard utility data.
{"title":"Can the Markovian influence graph simulate cascading resilience from historical outage data?","authors":"Kai Zhou, I. Dobson, Zhaoyu Wang","doi":"10.1109/PMAPS47429.2020.9183492","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183492","url":null,"abstract":"It is challenging to simulate the cascading line outages that can follow initial damage to the electric power transmission system from extreme events. Instead of model-based simulation, we propose using a Markovian influence graph driven by historical utility data to sample the cascades. The sampling method encompasses the rare, large cascades that contribute greatly to the blackout risk. This suggested new approach contributes a high-level simulation of cascading line outages that is driven by standard utility data.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132640428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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