2022 IEEE Texas Power and Energy Conference (TPEC)最新文献_第3页

Towards Developing Implementable High Altitude Electromagnetic Pulse E3 Mitigation Strategies for Large-Scale Electric Grids 大型电网高空电磁脉冲E3缓解策略研究

2022 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2022-02-28 DOI: 10.1109/TPEC54980.2022.9750778

T. Overbye, Jonathan Snodgrass, A. Birchfield, M. Stevens

引用次数: 4

Triple Phase Shift Control of Dual Active Bridge Converter using Machine Learning Methods 基于机器学习方法的双有源桥式变换器三相移相控制

2022 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2022-02-28 DOI: 10.1109/TPEC54980.2022.9750772

Bharat Bohara, A. Karbozov, H. Krishnamoorthy

引用次数: 1

Robust Intrusion Detection for Resilience Enhancement of Industrial Control Systems: An Extended State Observer Approach 增强工业控制系统弹性的鲁棒入侵检测:一种扩展状态观测器方法

2022 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2022-02-28 DOI: 10.1109/TPEC54980.2022.9750751

Saif Ahmad, Hafiz Ahmed

引用次数: 7

[Copyright notice] (版权)

2022 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2022-02-28 DOI: 10.1109/tpec54980.2022.9750775

引用次数: 0

A Time-Domain Short Circuit Study for a VSC Based Battery Energy Storage System 基于VSC的电池储能系统时域短路研究

2022 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2022-02-28 DOI: 10.1109/TPEC54980.2022.9750735

A. Abdullah, M. Humayun, D. Mueller, Carl Moeller

引用次数: 1

Hybrid Magnetic Core for Downsizing the Inductor in LLC Converter 用于LLC变换器电感器小型化的混合磁芯

2022 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2022-02-28 DOI: 10.1109/TPEC54980.2022.9750798

Srinu Avala, Naveen Yalla, P. Agarwal

引用次数: 0

Microgrid Protection with Penetration of DERs - A Comprehensive Review 基于DERs的微电网保护综述

2022 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2022-02-28 DOI: 10.1109/TPEC54980.2022.9750716

Jorge Cisneros-Saldana, Smrutirekha Samal, Hemkesh Singh, M. Begovic, S. Samantaray

{"title":"Microgrid Protection with Penetration of DERs - A Comprehensive Review","authors":"Jorge Cisneros-Saldana, Smrutirekha Samal, Hemkesh Singh, M. Begovic, S. Samantaray","doi":"10.1109/TPEC54980.2022.9750716","DOIUrl":"https://doi.org/10.1109/TPEC54980.2022.9750716","url":null,"abstract":"Distributed Energy Resources (DER) early uses as a backup generation has been progressing toward permanent Distributed Generation (DG), along with the development and enhancement of new technologies over small-scale generation. Over last few years, increasing penetration of renewables in the distribution networks at consumer level raises concerns on protection, control, stability and reliability. Considering the DG integration and wide variations in operating conditions of the microgrid, relays experience protection issues at fault current level violating important tripping decision rules. This study reviews the impact of DG penetration as integration means on traditional overcurrent (OC) protection schemes, being the most common and widely used relaying scheme in radial distribution networks. This paper reviews the most representative methods with respect to various challenges uncovered by exhaustive studies and validations and reported in the literature. Further, potential adaptive and intelligent schemes are also discussed for enhancing the performance over traditional protection schemes in microgrids.","PeriodicalId":185211,"journal":{"name":"2022 IEEE Texas Power and Energy Conference (TPEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114307486","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

DCCA Enhanced Forced Oscillation Frequency Detection Using Real-world PMU Data DCCA增强强迫振荡频率检测使用真实世界的PMU数据

2022 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2022-02-28 DOI: 10.1109/TPEC54980.2022.9750846

Abraham Canafe, Yunchuan Liu, Lei Yang, H. Livani

{"title":"DCCA Enhanced Forced Oscillation Frequency Detection Using Real-world PMU Data","authors":"Abraham Canafe, Yunchuan Liu, Lei Yang, H. Livani","doi":"10.1109/TPEC54980.2022.9750846","DOIUrl":"https://doi.org/10.1109/TPEC54980.2022.9750846","url":null,"abstract":"This paper studies forced oscillation frequency detection using real-world Phasor Measurement Unit (PMU) data. The accurate identification of forced oscillations can help operators prevent power system failures and take appropriate remedial actions. To detect forced oscillation frequencies, we first decompose the PMU data into a series of intrinsic mode functions (IMFs) using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) technique, which can effectively de-noise the raw PMU data. Then, we choose the optimal mode for frequency detection by selecting the IMF most strongly correlated with the original signal based on detrended cross correlation analysis (DCCA), as real-world PMU data obtained from oscillation events are often non-stationary. Compared with the cross-correlation coefficient used in the existing studies, the DCCA coefficient can better analyze non-stationary data and thus find a better mode for frequency detection. Using the real-world PMU datasets for oscillation events from the ISO-NE grid, experimental results show that the proposed DCCA enhanced forced oscillation frequency detection can accurately detect the oscillation frequency.","PeriodicalId":185211,"journal":{"name":"2022 IEEE Texas Power and Energy Conference (TPEC)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126970135","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

Reconfiguring Unbalanced Distribution Networks using Reinforcement Learning over Graphs 利用图上的强化学习重新配置不平衡配电网络

2022 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2022-02-28 DOI: 10.1109/TPEC54980.2022.9750805

R. Jacob, Steve Paul, Wenyuan Li, Souma Chowdhury, Y. Gel, J. Zhang

{"title":"Reconfiguring Unbalanced Distribution Networks using Reinforcement Learning over Graphs","authors":"R. Jacob, Steve Paul, Wenyuan Li, Souma Chowdhury, Y. Gel, J. Zhang","doi":"10.1109/TPEC54980.2022.9750805","DOIUrl":"https://doi.org/10.1109/TPEC54980.2022.9750805","url":null,"abstract":"The recent trend in distribution system intelligence necessitates the deployment of real-time, automated, and adaptable decision-making tools. Reconfiguring the distribution network by changing the status of switches can aid in loss minimization during normal operations and resilience enhancement during disruptive events. Traditional methods employed for solving the network reconfiguration problem are model-based and scenario-specific. Besides this, the scalability and computational efficiency also limit the utilization of such techniques for online control, which could be potentially addressed by neural network based models trained with reinforcement learning (RL). To this end, we formulate the reconfiguration problem as a Markov Decision Process where the optimal control policy is learned using the RL approach. Considering the relevance of topology in decision making and the interaction between the generation and demand at different buses, we model the power distribution network along with its state variables as a graph in the learning space. Consequently, we propose an RL over graphs where a Capsule-based graph neural network is used as the policy network. The developed model is validated on the modified IEEE 13 and 34 bus test networks.","PeriodicalId":185211,"journal":{"name":"2022 IEEE Texas Power and Energy Conference (TPEC)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127023873","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: 8

Attention! Is Recycling Artificial Neural Network Effective for Maintaining Renewable Energy Efficiency? 注意!回收人工神经网络对维持可再生能源效率有效吗?

2022 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2022-02-28 DOI: 10.1109/TPEC54980.2022.9750784

Yeonghyeon Park, Myung Jin Kim, Uju Gim

{"title":"Attention! Is Recycling Artificial Neural Network Effective for Maintaining Renewable Energy Efficiency?","authors":"Yeonghyeon Park, Myung Jin Kim, Uju Gim","doi":"10.1109/TPEC54980.2022.9750784","DOIUrl":"https://doi.org/10.1109/TPEC54980.2022.9750784","url":null,"abstract":"Modern society interests on the renewable energy and maintaining the efficiency of them. In the case of using solar energy, recognition and response to defectives as soon as possible is recommended because defects in solar panels reduces energy efficiency. In the same context as the interest in renewable energy, it would be better to use a proper lightweight defective detection model than a high-performance heavy model. In order to reduce the computational load in the training procedure, we define statistical features from the solar panel and use those for defective detection. Also, assuming that the attention mechanism that guides the key information, we recycle the pre-trained convolutional neural network that learned MNIST datset to enhance the feature values. Through the extracted statistical features, we achieve both reducing computational load in the training process and 0.831 for defective detection performance. Also, the detection performance is improved to 0.845 via recycling the pre-trained attention mechanism. The above means our approach additionally contributes renewable energy and sustainability via statistical feature extraction and recycling pre-trained neural network.","PeriodicalId":185211,"journal":{"name":"2022 IEEE Texas Power and Energy Conference (TPEC)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127182188","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