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2021 2021 11th Smart Grid Conference (SGC)  
EVs Charge and Discharge Scheduling in the Smart Grid Considering Machine Learning-based Forecasted Load ...............................................................................................  ...  of Capacitors Suitable for AC Microgrids ....... 192 Cyber Attack Detection in PMU Networks Exploiting the Combination of Machine Learning and State Estimation-Based Methods ..........................  ... 
doi:10.1109/sgc54087.2021.9664149 fatcat:pf4tnzfzezdvlcbqcwrd5h26ta


2022 Chinese Journal of Electrical Engineering  
Regular Papers 1 Review of Grid-forming Inverters in Support of Power System Operation Guanhong Song, Bo Cao and Liuchen Chang Abstract: The penetration of distributed energy resources in electrical  ...  Inverters, as interfaces between distributed energy resources and grids, have become critical assets in modern power systems.  ... 
doi:10.23919/cjee.2022.9751456 fatcat:xb3ofbuadjdnroj2c7rjknsswu

List of Authors

2020 2020 55th International Universities Power Engineering Conference (UPEC)  
Estimates using Machine Learning Methods Frequency Influenceable Grid Emulation for the Analysis of Grid-Forming Inverters Using a Generator Set Congestion Management of the German Transmission Grid through  ...  Sharing Control for Microgrid Applications Thomas Leibfried Distributed AC-DC Optimal Power Flow in the European Transmission Grid with ADMM An Optimal Power Flow Algorithm for the Simulation of Energy  ... 
doi:10.1109/upec49904.2020.9209895 fatcat:4hmddzlorvaurdcojpqrhrnw5a

List of Authors

2021 2021 56th International Universities Power Engineering Conference (UPEC)  
optimization and Machine Learning Approach for Fault Detection Ozgur Karacasu Optimal Overcurrent Relay Coordination in Distribution Networks Pablo Gomez Transient Analysis of Vehicle-to-Grid  ...  of two-area power system Michael Packianather Novel Hybrid Invasive weed optimization and Machine Learning Approach for Fault Detection Michael Suriyah Emulation of Grid-Following Inverters for  ... 
doi:10.1109/upec50034.2021.9548188 fatcat:32pxaqvhajdwdh4jxbyui6ki6i

A Mini-Review on High-Penetration Renewable Integration Into a Smarter Grid

Yang Li, Chunling Wang, Guoqing Li
2020 Frontiers in Energy Research  
In this context, emerging technologies such as smart inverters and machine learning (ML) have provided more regulatory means for better integration of high-penetration renewable energy.  ...  Machine Learning Machine learning (ML) is a powerful technique that analyzes and learns a large amount of existing or generated data for predictions or decisions (Pallonetto et al., 2019) .  ... 
doi:10.3389/fenrg.2020.00084 fatcat:ukf7unrwyrh3lg2scdeair66iu

Machine Learning for Energy Systems Optimization

Insu Kim, Beopsoo Kim, Denis Sidorov
2022 Energies  
This editorial overviews the contents of the Special Issue "Machine Learning for Energy Systems 2021" and review the trends in machine learning (ML) techniques for energy system (ES) optimization [...]  ...  Introduction This editorial overviews the contents of the Special Issue "Machine Learning for Energy Systems 2021" and review the trends in machine learning (ML) techniques for energy system (ES) optimization  ...  considering costs and grid operational constraints), and trends in ML for ESs.  ... 
doi:10.3390/en15114116 fatcat:7y57px26i5dlnmywb453tibclq

ECCE 2020 Index

2020 2020 IEEE Energy Conversion Congress and Exposition (ECCE)  
Inverters in Islanded AC Microgrids [#0126] 4621 Doppa, Janardhan Rao Machine Learning Enabled Fast Multi-Objective Optimization for Electrified Aviation Power System Design [#0819] 6385 Dörfler  ...  of Resilient Distribution Systems with Hybrid Grid-Forming and Grid-Following Inverters [#1577] 3733 Distributed Average Observation in Inverter Dominated Dynamic Microgrids [#1208] 4627 Holistic  ... 
doi:10.1109/ecce44975.2020.9236355 fatcat:sbl4a4gonrf77kylohb3nbmhqa

Towards Distributed Energy Services: Decentralizing Optimal Power Flow with Machine Learning [article]

Roel Dobbe, Oscar Sondermeijer, David Fridovich-Keil, Daniel Arnold, Duncan Callaway, Claire Tomlin
2019 arXiv   pre-print
It provides a framework for Distribution System Operators to efficiently plan and operate the contributions of DERs to achieve Distributed Energy Services in distribution networks.  ...  We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution  ...  ACKNOWLEDGMENTS We thank Michael Sankur for contributions to the code base for this work.  ... 
arXiv:1806.06790v3 fatcat:6sgm2sqnqvclxdplkolt47pxem

SeFet 2021 TOC

2021 2021 International Conference on Sustainable Energy and Future Electric Transportation (SEFET)  
This presentation will review the current state-of-the-art for vehicle drive machines and highlight the key characteristics that are required.  ...  Electric and hybrid vehicle drives have come a long way in recent years.  ...  and Undervoltage Relay 198 Machine Learning Based Controlled Filtering for Solar PV Variability Reduction with BESS 199 Optimization of Rooftop PV System Deployment for LV Distribution Network 204 Day-Ahead  ... 
doi:10.1109/sefet48154.2021.9375818 fatcat:7qnc2hqd7rayfgycudpkffnhfq

A Review of Low-Voltage Renewable Microgrids: Generation Forecasting and Demand-Side Management Strategies

Miguel Aybar-Mejía, Junior Villanueva, Deyslen Mariano-Hernández, Félix Santos, Angel Molina-Garcia
2021 Electronics  
Recent contributions focused on the application of microgrids in Low-Voltage distribution networks are also analyzed and reviewed in detail.  ...  Under this scenario, the control of distributed generation inverters, demand management systems, renewable resource forecasting, and demand predictions will allow better integration of such microgrid clusters  ...  There are two types of converters in MGs: grid-followers and grid-formers (see Figure 4) ; grid-tied inverters operate as grid-following sources tracking the voltage angle of the grid to control output  ... 
doi:10.3390/electronics10172093 fatcat:76aojzjxxngwxgvo34x4qc2gmu

An overview of grid-edge control with the digital transformation

Tam T. Mai, Phuong H. Nguyen, Quoc-Tuan Tran, Alessia Cagnano, Giovanni De Carne, Yassine Amirat, Anh-Tuan Le, Enrico De Tuglie
2021 Electrical engineering (Berlin. Print)  
This evolution imposes many challenges to the operation of the network, which then calls for new control and operation paradigms.  ...  AbstractDistribution networks are evolving to become more responsive with increasing integration of distributed energy resources (DERs) and digital transformation at the grid edges.  ...  The machine learning methods proposed in [118, 119] employ multi-learning regression to calculate the optimal reactive power outputs of DGs.  ... 
doi:10.1007/s00202-020-01209-x fatcat:xwawbuy43fhsbcy5d26u2yc4sa

Paper title index

2013 2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)  
Generation System under Multi-objective optimal power flow model for power system operation Profit Enhancement Through Optimized Operation of Photovoltaic Unbalanced Grid Voltage Conditions dispatching  ...  Sequential Extreme Learning Machine Impacts of Wind Power Penetration on Risk Constrained Economic Modeling of Photovoltaic Generation Unit for Power System Studies Power Transformer Condition Monitoring  ... 
doi:10.1109/appeec.2013.6837328 fatcat:x32dlqzzufdspolwvlh3ljflke

Preventing cascading failures in microgrids with one-sided support vector machines

Matt Wytock, Srinivasa Salapaka, Murti Salapaka
2014 53rd IEEE Conference on Decision and Control  
In this article we provide a methodology to determine current thresholds and guard times, the time interval when current is allowed to exceed threshold value, for each inverter for fail-safe operation  ...  We employ a machine learning approach to address the problem where we first demonstrate that conventional support vector machine (SVM) methodology does not yield a satisfactory solution.  ...  MACHINE LEARNING MODEL In the following a machine learning approach is taken to learn the functions f 0 , f 1 and f 2 .  ... 
doi:10.1109/cdc.2014.7039892 dblp:conf/cdc/WytockaS14 fatcat:zhnx5ytgqvb4zawqhznita7tui

Table of Contents

2021 IEEE transactions on industry applications  
Jado-Puente 1779 Transportation Systems Committee CAPSA Based Control for Power Quality Correction in PV Array Integrated EVCS Operating in Standalone and Grid Connected Modes . . . . . . . . . . . . .  ...  Kuruvinashetti 1703 A Normalized Adaptive Filter for Enhanced Optimal Operation of Grid-Interfaced PV System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tia.2021.3055604 fatcat:o4qtzamdmnaknckv3zjswcr7wm

Kernel-Based Learning for Smart Inverter Control [article]

Aditie Garg, Mana Jalali, Vassilis Kekatos, Nikolaos Gatsis
2018 arXiv   pre-print
Distribution grids are currently challenged by frequent voltage excursions induced by intermittent solar generation.  ...  Since optimal inverter coordination may be computationally challenging and preset local control rules are subpar, the approach of customized control rules designed in a quasi-static fashion features as  ...  LEARNING INVERTER CONTROL RULES Kernels have served as the foundation for extending machine learning tools to nonlinear mappings.  ... 
arXiv:1807.03769v1 fatcat:jjnyyuedxjflddl5bwyvhwyxzm
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