2021 IEEE Kansas Power and Energy Conference (KPEC)最新文献_第5页

Parameter Tuning Analysis for Phase Identification Algorithms in Distribution System Model Calibration 配电系统模型标定中相位识别算法的参数整定分析

2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/kpec51835.2021.9446218

Bethany D. Peña, Logan Blakely, M. Reno

引用次数: 2

A Fractional Order Controller Design for a Class of Linear Systems 一类线性系统的分数阶控制器设计

2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/KPEC51835.2021.9446215

G. Gurumurthy, B. M. Krishna.V, Kishore Yadlapati

引用次数: 1

Interactions of Rooftop Solar Photovoltaic Systems with Symmetrical and Unsymmetrical Faults in Distribution Feeders 配电网对称与非对称故障下屋顶太阳能光伏系统的相互作用

2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/KPEC51835.2021.9446264

Haifah B. Sambo, Jonathan Devadason, P. Moses

引用次数: 0

Electromagnetic Transient (EMT) Simulation Algorithm for Evaluation of Photovoltaic (PV) Generation Systems 用于光伏发电系统评估的电磁暂态仿真算法

2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/KPEC51835.2021.9446234

Jongchan Choi, S. Debnath

{"title":"Electromagnetic Transient (EMT) Simulation Algorithm for Evaluation of Photovoltaic (PV) Generation Systems","authors":"Jongchan Choi, S. Debnath","doi":"10.1109/KPEC51835.2021.9446234","DOIUrl":"https://doi.org/10.1109/KPEC51835.2021.9446234","url":null,"abstract":"Use of inverter-based resources facilitating renewable energy resources such as photovoltaic (PV) generation is increasing rapidly with decreasing costs and reduced emissions associated. To accommodate such rapid growth of inverter-based resources like PV systems, electromagnetic transient (EMT) simulation models of both PV systems and grids are required to analyze the interaction of PVs in the grid (like the post-event analysis). In addition, the EMT simulation would help with the planning of future power grid with a large number of PVs as well as other inverter-based distributed generation systems. In this paper, the EMT simulation models of PV systems and grids are developed based on the differential algebraic equations (DAEs) representing their EMT dynamics. Furthermore, advanced simulation algorithms including numerical stiffness-based hybrid discretization, DAE clustering and aggregation, multi-order integration, and matrix splitting approaches are applied to accelerate the EMT simulation. The proposed algorithm was applied to 125 PV inverters within 52-bus medium-voltage (MV) distribution grid.","PeriodicalId":392538,"journal":{"name":"2021 IEEE Kansas Power and Energy Conference (KPEC)","volume":"411 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132167098","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

Design of Resonant DC-DC LLC converter 谐振型DC-DC LLC变换器的设计

2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/KPEC51835.2021.9446206

Kali Naraharisetti, Janamejaya Channegowda, P. Green

引用次数: 0

Solar PV Power Forecasting Using Traditional Methods and Machine Learning Techniques 利用传统方法和机器学习技术进行太阳能光伏发电预测

2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/kpec51835.2021.9446199

A. M. Alam, Nahid-Al-Masood, Iqbal Asif Razee, Mohammad Zunaed

{"title":"Solar PV Power Forecasting Using Traditional Methods and Machine Learning Techniques","authors":"A. M. Alam, Nahid-Al-Masood, Iqbal Asif Razee, Mohammad Zunaed","doi":"10.1109/kpec51835.2021.9446199","DOIUrl":"https://doi.org/10.1109/kpec51835.2021.9446199","url":null,"abstract":"The stability of the power sector has become uncertain due to the unpredictable characteristics of renewable energy sources such as solar photovoltaic (PV) power generation. It endangers the balance of the power system which is very sensitive to any mode of change and results in an ineffectiveness to match power consumption and production. The ultimate goal of harvesting renewable energy is to integrate it into the power grid. So, predicting the total amount of power generation by solar cells has become an important aspect. This study delineates various Convolutional Neural Network (CNN) techniques such as regular CNN, multi-headed CNN, and CNN-LSTM (CNN Long Short-Term Memory) which employs sliding window algorithm and other feature extraction and pre-processing techniques to make accurate predictions. Meteorological parameters such as Solar Irradiance, Air Temperature, Humidity, Wind Direction, and Wind Speed are related to the output of the solar panels. For instance, input parameters were taken for 5 years span and predicted for a particular day and one week. The results were evaluated by comparing them with traditional forecasting techniques such as Autoregressive Moving Average (ARMA) and Multiple Linear Regression (MLR). The efficacy of the result was also evaluated by the Evaluation Metrics such as RMSE, MAE, and MBE. Both traditional and machine learning techniques demonstrate the effectiveness in producing short-term and medium-term forecasting.","PeriodicalId":392538,"journal":{"name":"2021 IEEE Kansas Power and Energy Conference (KPEC)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129477597","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: 7

A Multilevel-Modular 10 kV Silicon Carbide MOSFET Module using Custom High-Voltage Isolated Gate Driver, Coupling, and Snubber Circuits 一个多电平模块化10千伏碳化硅MOSFET模块，使用定制的高压隔离栅驱动，耦合和缓冲电路

2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/KPEC51835.2021.9446200

A. N. M. Wasekul Azad, Sourov Roy, Faisal Khan, A. Caruso

{"title":"A Multilevel-Modular 10 kV Silicon Carbide MOSFET Module using Custom High-Voltage Isolated Gate Driver, Coupling, and Snubber Circuits","authors":"A. N. M. Wasekul Azad, Sourov Roy, Faisal Khan, A. Caruso","doi":"10.1109/KPEC51835.2021.9446200","DOIUrl":"https://doi.org/10.1109/KPEC51835.2021.9446200","url":null,"abstract":"This paper presents a multilevel-modular high voltage (HV) switch architecture comprised of series-connected SiC MOSFETs and a voltage balancing method that achieves <1.1% voltage mismatch under steady-state and switching modes. An individual module consists of four series-connected 1.7-kV rated SiC MOSFETs, yielding a breakdown voltage of 6.8-kV, and driven by a single custom 10-kV isolated gate driver. One gate driver per module and 46 passive components (e.g., coupling capacitors, resistors, etc.) per module lead to low-cost fabrication and simpler operation of this HV switch. The unique modularity feature of the proposed switch enables voltage scalability to the limit of the weakest passive link. Any single or a combination of multiple passive components (e.g., capacitors, diodes, resistors, etc.) can form the weakest passive link in a module. Further, the high volumetric power density realized by the physical layout and modular stacking is within 80% of a fully custom/integral design. The working principle of the HV switch during switching transients and steady-state are demonstrated and compared in simulation and from measurements. Simulation and experimental results demonstrate excellent voltage balancing as supported by a minimal voltage imbalance of <80-V among the individual MOSFETs in the HV switch at a supply voltage of 6-kV and a switching frequency of 15-kHz.","PeriodicalId":392538,"journal":{"name":"2021 IEEE Kansas Power and Energy Conference (KPEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131336913","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

Short-Term Forecast Analysis on Wind Power Generation Data 风力发电数据短期预测分析

2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/KPEC51835.2021.9446247

A. S. Nair, P. Ranganathan, C. Finley, N. Kaabouch

引用次数: 0

Voltage Control by Transmitter-Side Measurements for On-board Wireless EV Battery Charger 车载无线电动汽车电池充电器的发射端测量电压控制

2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/KPEC51835.2021.9446245

F. Sadeque, Fariba Fateh

{"title":"Voltage Control by Transmitter-Side Measurements for On-board Wireless EV Battery Charger","authors":"F. Sadeque, Fariba Fateh","doi":"10.1109/KPEC51835.2021.9446245","DOIUrl":"https://doi.org/10.1109/KPEC51835.2021.9446245","url":null,"abstract":"Wireless charging for electric vehicles (EVs) requires a high precision control scheme to maintain desired charging voltage across the battery. The receiver-end voltage of an on-board charger can vary dramatically with the change in the mutual inductance developed in between the transmitter-end coil and the receiver-end coil. The receiver-end coil is set inside the electric vehicle, and therefore, accurate alignment between the transmitter-receiver coils is crucial for efficient charging. In this paper, strategy for a receiver-end charging voltage control for a wireless EV battery charger by measuring the transmitter-side voltage and current parameters has been proposed. Mathematical models to estimate the mutual inductance and the output voltages have been developed. The proposed strategy is able to estimate the mutual inductance between the two coils by utilizing the transmitter-side measurements and estimate the output voltage at the receiver end. The estimated voltage is then utilized in a closed-loop controller at the transmitter-side to control the output voltage at the desired level. The major contribution of this paper lies in the achievement of accurate voltage control of wireless EV charger, without physically accessing the measurement parameters inside the vehicle and thus ensuring efficient wireless charging, which is straight forward, does not rely on the sensors from the electric vehicle, and easy to implement. The efficacy of the proposed strategy is verified through simulation results in MATLAB/Simulink.","PeriodicalId":392538,"journal":{"name":"2021 IEEE Kansas Power and Energy Conference (KPEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130833237","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

An Effective Ensemble Learning approach-Based Grid Stability Assessment and Classification 基于集成学习方法的电网稳定性评估与分类

2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/kpec51835.2021.9446197

M. Massaoudi, H. Abu-Rub, S. Refaat, I. Chihi, F. Oueslati

引用次数: 8