发布求助
文献互助 智能选刊 最新文献

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)最新文献

筛选
英文 中文
Fast Point Estimate Method for Correlated Multimodally Distributed Input Variables 相关多模态分布输入变量的快速点估计方法
2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183681
Marie-Louise Kloubert
{"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":"23 1","pages":"0"},"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}
引用次数: 2
Learning partially observed meshed distribution grids 学习部分观察的网状配电网
2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183648
Harish Doddi, Deepjyoti Deka, M. Salapaka
{"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":"94 1","pages":"0"},"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}
引用次数: 2
Partial Discharge Detection with Convolutional Neural Networks 基于卷积神经网络的局部放电检测
2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183469
Wei Wang, N. Yu
{"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":"6 1","pages":"0"},"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}
引用次数: 4
Reliability of Decentralized Network Automation Systems and Impacts on Distribution Network Reliability 分散式电网自动化系统可靠性及其对配电网可靠性的影响
2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183449
K. Kamps, F. Möhrke, M. Zdrallek, P. Awater, M. Schwan
{"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":"1 1","pages":"0"},"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}
引用次数: 1
Deterministic and Probabilistic Assessment of Distribution Network Hosting Capacity for Wind-Based Renewable Generation 风电可再生能源发电配电网承载能力的确定性与概率评估
2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183525
D. Fang, M. Zou, G. Harrison, S. Djokic, M. Ndawula, X. Xu, I. Hernando‐Gil, J. Gunda
{"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":"28 1","pages":"0"},"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}
引用次数: 6
Reliability based Joint Distribution Network and Distributed Generation Expansion Planning 基于可靠性的联合配电网与分布式发电扩展规划
2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183536
Milad Kabirifar, M. Fotuhi‐Firuzabad, M. Moeini‐Aghtaie, Niloofar Pourghaderi, M. Lehtonen
{"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":"24 1","pages":"0"},"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}
引用次数: 1
Probabilistic Load Forecasting for Day-Ahead Congestion Mitigation 日前拥堵缓解的概率负荷预测
2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183670
Gonca Gürses-Tran, Hendrik Flamme, A. Monti
{"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":"12 1","pages":"0"},"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}
引用次数: 5
A Polynomial Chaos-based Approach to Sizing of Virtual Synchronous Generators 一种基于多项式混沌的虚拟同步发电机定径方法
2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183415
Michael Abdelmalak, M. Benidris
{"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":"114 1","pages":"0"},"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}
引用次数: 1
Can the Markovian influence graph simulate cascading resilience from historical outage data? 马尔可夫影响图能从历史停电数据中模拟级联弹性吗?
2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183492
Kai Zhou, I. Dobson, Zhaoyu Wang
{"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":"5 1","pages":"0"},"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}
引用次数: 5
System Reliability Risk Model and Its Application to Station Breaker Replacement 系统可靠性风险模型及其在车站断路器更换中的应用
2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183702
Dange Huang, B. Bagen
{"title":"System Reliability Risk Model and Its Application to Station Breaker Replacement","authors":"Dange Huang, B. Bagen","doi":"10.1109/PMAPS47429.2020.9183702","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183702","url":null,"abstract":"Utilities are facing many challenges in planning and operating the power systems. The use of probabilistic planning approach is a beneficial supplement to the existing system planning and operation process. A series of tools has been developed in Manitoba Hydro to provide inputs to high level decision making process including capital project justification, enhancement of transmission asset management, prioritization of transmission asset investment. The applications of the basic concept that has been used in the risk assessment tools are illustrated through the assessment of a potential investment project involving the replacement of a number of breakers in a practical power system. Particularly the evaluation of the breaker replacement project considers the operational constraints, which is an important aspect that needs to be modelled in practical power system reliability assessment.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131872342","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}
引用次数: 2
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信