2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)最新文献_第7页

Composite System Reliability Analysis using Deep Learning enhanced by Transfer Learning 基于迁移学习的深度学习复合系统可靠性分析

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183474

Dogan Urgun, C. Singh

引用次数: 7

Transmission Line Overloading Analysis Using Probabilistic Dynamic Line Rating 基于概率动态线路评级的输电线路过载分析

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183550

S. Hajeforosh, M. Bollen

引用次数: 2

Data-driven Assessment of Power System Reliability in Presence of Renewable Energy 可再生能源存在下电力系统可靠性数据驱动评估

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183481

Atri Bera, Anurag Chowdhury, J. Mitra, Saleh S Almasabi, M. Benidris

{"title":"Data-driven Assessment of Power System Reliability in Presence of Renewable Energy","authors":"Atri Bera, Anurag Chowdhury, J. Mitra, Saleh S Almasabi, M. Benidris","doi":"10.1109/PMAPS47429.2020.9183481","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183481","url":null,"abstract":"The penetration of renewable energy sources (RES) and energy storage systems (ESS) in the modern-day power grid is increasing at a fast pace. However, reliability assessment of power systems using traditional methods has become a challenging task due to the interdependencies between RES like wind and solar, ESS, and the load. This paper proposes a new method based on artificial neural networks (ANN), a data-driven technique, for reliability assessment of a power system by estimating the parameters of the ANN. The hourly generation data of the distributed and conventional generators are considered to be the features or the input variables. A recurrent neural network based classification algorithm is trained to determine system responses to changes in system conditions. The data required for training and testing the learning algorithm is generated using sequential Monte Carlo simulation. The IEEE Reliability Test System is utilized for testing and validating the proposed approach. The results indicate that the learning algorithm can model the temporal relevance between different system variables for successful reliability assessment of the system.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"13 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":"128557414","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

Underground AC Circuits in North America: Inventory Attributes and Sustained Outages 北美地下交流电路:库存属性和持续中断

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183424

S. Ekisheva, M. Papic, M. Lauby, B. David Till

{"title":"Underground AC Circuits in North America: Inventory Attributes and Sustained Outages","authors":"S. Ekisheva, M. Papic, M. Lauby, B. David Till","doi":"10.1109/PMAPS47429.2020.9183424","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183424","url":null,"abstract":"This paper presents a first comprehensive statistical study of transmission inventory and outage data for underground AC circuits on the North American scale. The analysis is based on the data collected and submitted by member utilities to the North American Electric Reliability Corporation’s (NERC’s) Transmission Availability Data System (TADS) during the years 2013 to 2018. The statistical approach considers the random nature of automatic outages and connects the outage frequency on an underground circuit and the outage duration with the circuit inventory attributes both numerical and categorical (voltage class, mileage, number of terminals, terrain etc.)A comparative analysis of reliability statistics for underground and overhead transmission circuits is also presented. It shows that automatic outages on the underground circuits are significantly rarer but much longer than on the overhead circuits. The greater durations result in a higher unavailability of the underground transmission lines compared with the overhead ones—on average, an underground AC circuit is unavailable 29 hours a year due to sustained automatic outages versus 6 hours for an overhead ac circuit.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"42 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":"125170084","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

HFNet: Forecasting Real-Time Electricity Price via Novel GRU Architectures HFNet:基于新型GRU架构的实时电价预测

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183697

Haolin Yang, K. Schell

引用次数: 6

Combining Historical Data and Domain Expert Knowledge Using Optimization to Model Electrical Equipment Reliability 结合历史数据和领域专家知识，利用优化建模电气设备可靠性

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183620

A. Côté, O. Blancke, S. Alarie, Amira Dems, D. Komljenovic, D. Messaoudi

引用次数: 3

Use of Transmission Line Outage History for Probabilistic Transmission Risk Assessment 输电线路中断历史在概率输电风险评估中的应用

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183468

Gregg A. Spindler, Susan L. Spindler

引用次数: 1

A review of data-driven and probabilistic algorithms for detection purposes in local power systems 基于数据驱动和概率算法的局部电力系统检测综述

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183634

Sylvie Koziel, P. Hilber, R. Ichise

{"title":"A review of data-driven and probabilistic algorithms for detection purposes in local power systems","authors":"Sylvie Koziel, P. Hilber, R. Ichise","doi":"10.1109/PMAPS47429.2020.9183634","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183634","url":null,"abstract":"Power grid operators use data to guide their asset management decisions. However, as the complexity of collected data increases with time and amount of sensors, it becomes more difficult to extract relevant information. Therefore, methods that perform detection tasks need to be developed, especially in distribution systems, which are impacted by distributed generation and smart appliances. Until now, methods employed in local power systems for detection purposes using data with low sampling rate, have not been reviewed. This paper provides a literature review focused on anomaly detection, fault location, and load disaggregation. We analyze the methods in terms of their type, data requirements and ways they are implemented. Many belong to the machine learning field. We find that some methods are typically combined with others and perform specific tasks, while other methods are more ubiquitous and often used alone. Continued research is needed to identify how to guide the choice of methods, and to investigate combinations of methods that have not been studied yet.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"2 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":"129899737","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

Modeling of Natural Disasters and Extreme Events for Power System Resilience Enhancement and Evaluation Methods 电力系统自然灾害和极端事件建模及弹性增强评估方法

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183679

N. Bhusal, Mukesh Gautam, Michael Abdelmalak, M. Benidris

{"title":"Modeling of Natural Disasters and Extreme Events for Power System Resilience Enhancement and Evaluation Methods","authors":"N. Bhusal, Mukesh Gautam, Michael Abdelmalak, M. Benidris","doi":"10.1109/PMAPS47429.2020.9183679","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183679","url":null,"abstract":"The frequency of disruptive and newly emerging threats (e.g. man-made attacks—cyber and physical attacks; extreme natural events—hurricanes, earthquakes, and floods) has escalated in the last decade. Impacts of these events are very severe ranging from long power outage duration, major power system equipment (e.g. power generation plants, transmission and distribution lines, and substation) destruction, and complete blackout. Accurate modeling of these events is vital as they serve as mathematical tools for the assessment and evaluation of various operations and planning investment strategies to harden power systems against these events. This paper provides a comprehensive and critical review of current practices in modeling of extreme events, system components, and system response for resilience evaluation and enhancement, which is a important stepping stone toward the development of complete, accurate, and computationally attractive modeling techniques. The paper starts with reviewing existing technologies to model the propagation of extreme events and then discusses the approaches used to model impacts of these events on power system components and system response. This paper also discusses the research gaps and associated challenges, and potential solutions to the limitations of the existing modeling approaches.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"34 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":"115797494","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: 11

Composite Power System Reliability Assessment Considering Transmission Line Flexibility 考虑输电线路柔性的综合电力系统可靠性评估

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Pub Date : 2020-08-01 DOI: 10.1109/PMAPS47429.2020.9183505

G. Bolacell, Mauro Augusto da Rosa

引用次数: 3