2020 IEEE Power & Energy Society General Meeting (PESGM)最新文献_第5页

Hydro-Québec’s new approach for asset management hydro - quamesbec的资产管理新方法

2020 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2020-08-02 DOI: 10.1109/PESGM41954.2020.9281936

A. Delavari, J. Prévost, M. Gaha, Amira Dems

引用次数: 2

A Decomposition and Coordination Approach for Large Sub-hourly Unit Commitment 大型次小时单位承诺的分解与协调方法

2020 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2020-08-02 DOI: 10.1109/PESGM41954.2020.9282076

Jianghua Wu, P. Luh, Yonghong Chen, B. Yan, Mikhail A. Bragin

引用次数: 1

Deep Learning Ensemble Based New Approach for Very Short-Term Wind Power Forecasting 基于深度学习集成的极短期风电预测新方法

2020 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2020-08-02 DOI: 10.1109/PESGM41954.2020.9281473

Dan A. Rosa de Jesús, P. Mandal, Yuan-Kang Wu, T. Senjyu

{"title":"Deep Learning Ensemble Based New Approach for Very Short-Term Wind Power Forecasting","authors":"Dan A. Rosa de Jesús, P. Mandal, Yuan-Kang Wu, T. Senjyu","doi":"10.1109/PESGM41954.2020.9281473","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281473","url":null,"abstract":"This paper presents a new prediction approach based on deep learning ensemble for very short-term (10-minuteahead) wind power forecasting for a look-ahead period of 1h, 3h, and 6h. The proposed deep learning ensemble approach combines several individual and hybrid deep learning models, such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Hybrid Deep Neural Network (HDNN), with the formation of four different ensembles, in particular HDNN+CNN,HDNN+LSTM, CNN+LSTM, and HDNN+CNN+LSTM. The proposed approach considers the historical data of wind speed as major input through ensemble averaging in order to produce the final wind power prediction. The major advantage of the proposed ensemble learning is that they make the best use of predictions from multiple deep learning models and their capability to effectively “cancel out” the individual errors, which in turn help enhance the final prediction accuracy. The simulation on actual data, acquired from the real wind farm in Texas, demonstrates the effectiveness of the presented approach to produce a higher degree of very short-term wind power forecast accuracy for multiple seasons of the year in comparison to other soft computing as well as to individual deep learning models.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132083002","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

Modeling of Protective Relays for Transient Stability Analysis 保护继电器暂态稳定分析的建模

2020 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2020-08-02 DOI: 10.1109/PESGM41954.2020.9281555

Muyang Liu, M. Murad, Junru Chen, F. Milano

引用次数: 0

A Comparative Study of Surrogate Based Learning Methods in Solving Power Flow Problem 基于代理学习方法求解潮流问题的比较研究

2020 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2020-08-02 DOI: 10.1109/PESGM41954.2020.9281640

O. Ceylan, G. Taşkın, S. Paudyal

引用次数: 1

Cost Sharing Mechanism for Reactive Power Management Amidst P2P Energy Sharing P2P能源共享中无功管理的成本分担机制

2020 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2020-08-02 DOI: 10.1109/PESGM41954.2020.9281554

Yuxuan Gu, Jianxiao Wang, Kedi Zheng, Qixin Chen, Wenyuan Tang, Jianing Liu, Kaiwen Zeng

引用次数: 1

Quantum-Secure Networked Microgrids 量子安全网络微电网

2020 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2020-08-02 DOI: 10.1109/PESGM41954.2020.9281884

Zefan Tang, Yanyuan Qin, Zimin Jiang, Walter O. Krawec, Peng Zhang

引用次数: 8

Sizing of Movable Energy Resources for Service Restoration and Reliability Enhancement 面向业务恢复和可靠性提升的可移动能源规模

2020 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2020-08-02 DOI: 10.1109/PESGM41954.2020.9281558

N. Bhusal, Mukesh Gautam, M. Benidris

{"title":"Sizing of Movable Energy Resources for Service Restoration and Reliability Enhancement","authors":"N. Bhusal, Mukesh Gautam, M. Benidris","doi":"10.1109/PESGM41954.2020.9281558","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281558","url":null,"abstract":"The frequency of extreme events (e.g., hurricanes, earthquakes, and floods) and man-made attacks (cyber and physical attacks) has increased dramatically in recent years. These events have severely impacted power systems ranging from long outage times to major equipment (e.g., substations, transmission lines, power plants, and distribution system) destruction. Distribution system failures and outages are major contributors to power supply interruptions. Network reconfiguration and movable energy resources (MERs) can play a vital role in supplying loads during and after contingencies. This paper proposes a two-stage strategy to determine the minimum sizes of MERs with network reconfiguration for distribution service restoration and supplying local and isolated loads. Sequential Monte Carlo simulations are used to model the outages of distribution system components. After a contingency, the first stage determines the network reconfiguration based on the spanning tree search algorithm. In the second stage, if some system loads cannot be fed by network reconfiguration, MERs are deployed and the optimal routes to reach isolated areas are determined based on the Dijkstra’s shortest path algorithm (DSPA). The traveling time obtained from the DSPA is incorporated with the proposed sequential Monte Carlo simulation-based approach to determine the sizes of MERs. The proposed method is applied on several distribution systems including the IEEE-13 and IEEE-123 node test feeders. The results show that network reconfiguration can reduce the required sizes of MERs to supply the isolated areas.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130172785","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: 10

Transient Stability Analysis and Enhancement of Renewable Energy Conversion System During LVRT LVRT可再生能源转换系统暂态稳定性分析及增强

2020 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2020-08-02 DOI: 10.1109/PESGM41954.2020.9282122

Xiuqiang He, H. Geng, Ruiqi Li, B. Pal

{"title":"Transient Stability Analysis and Enhancement of Renewable Energy Conversion System During LVRT","authors":"Xiuqiang He, H. Geng, Ruiqi Li, B. Pal","doi":"10.1109/PESGM41954.2020.9282122","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9282122","url":null,"abstract":"Grid-connected renewable energy conversion systems (RECSs) are usually required by grid codes to possess the low voltage ride through (LVRT) and reactive power support capabilities so as to cope with grid voltage sags. During LVRT, RECS’s terminal voltage becomes sensitive and changeable with its output current, which brings a great challenge for the RECS to resynchronize with the grid by means of phase-locked loops (PLLs). This paper indicates that loss of synchronism (LOS) of PLLs is responsible for the transient instability of grid-connected RECSs during LVRT, and the LOS is essentially due to the transient interaction between the PLL and the weak terminal voltage. For achieving a quantitative analysis, an equivalent swing equation model is developed to describe the transient interaction. Based on the model, the transient instability mechanism of RECSs during LVRT is clarified. Furthermore, a transient stability enhancement method is proposed to avoid the possibility of transient instability. Simulations performed on the New England 39-bus test system verify the effectiveness of the method.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134370261","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: 0

Physics-based Early Detection Algorithm for Temperature Monitoring Systems in Electrical Equipment 基于物理的电气设备温度监测系统早期检测算法

2020 IEEE Power & Energy Society General Meeting (PESGM) Pub Date : 2020-08-02 DOI: 10.1109/PESGM41954.2020.9281411

S. Purushothaman

{"title":"Physics-based Early Detection Algorithm for Temperature Monitoring Systems in Electrical Equipment","authors":"S. Purushothaman","doi":"10.1109/PESGM41954.2020.9281411","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281411","url":null,"abstract":"Temperature monitoring of electrical equipment is useful to detect deficient conditions like loose connections that cause overheating and could lead to failures. The monitoring systems typically implement fixed threshold-based logic and provide warnings or alarms. However, the industry is starting to leverage additional variables like load (current through equipment), ambient conditions, etc., and implementing pattern recognition or artificial intelligence-based techniques to identify deficiencies at an early stage. The implementation of these advanced techniques generally requires dedicated computational resources and software. This paper presents a simple analytical physics-based model that can be used to provide an early anomaly detection capability for current-carrying conductors in electrical equipment. The analytical model is developed as a second order equation that can be easily included in a monitoring system platform without the need for additional computational resources and external software. This paper includes the theory and simulation results from a finite element model to validate the analytical model. The finite element model was set up in COMSOL to simulate the heat transfer in a current-carrying conductor and validate the proposed analytical physics-based model.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131513485","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: 0