2021 IEEE Green Technologies Conference (GreenTech)最新文献_第10页

Smart Inverters' Functionalities and their Impacts on Distribution Feeders at High Photovoltaic Penetration 高光伏渗透率下智能逆变器的功能及其对配电网的影响

2021 IEEE Green Technologies Conference (GreenTech) Pub Date : 2020-08-25 DOI: 10.1109/GreenTech48523.2021.00026

T. Olowu, A. Sarwat

{"title":"Smart Inverters' Functionalities and their Impacts on Distribution Feeders at High Photovoltaic Penetration","authors":"T. Olowu, A. Sarwat","doi":"10.1109/GreenTech48523.2021.00026","DOIUrl":"https://doi.org/10.1109/GreenTech48523.2021.00026","url":null,"abstract":"There has been a consistent rise in grid-connected photovoltaic (PV) systems. Several studies have reported the impacts of this increase on the distribution networks such as reverse power flow, voltage fluctuations, possible increase in system losses amongst others. With this rise in PV penetration level, it becomes necessary for grid tied smart inverters (SI) to be allowed to participate in feeder voltage regulation. Till date, no comprehensive technical review and analysis has been done on the impact and interaction of existing and proposed SI settings on the existing legacy voltage control devices. This paper presents a novel technical review and analysis of some voltage regulation settings of SIs and their impacts on distribution feeders including their interaction with legacy devices such as capacitors, on/off Load Tap Changers, their impact on the feeder losses, harmonic effects and economic impacts (expressed as the device cost factor) and circuit impact index (CII). The analysis of these functionalities is done using the standard IEEE 8500 distribution feeder (modeled using OpenDSS and MATLAB), integrated with six PVs (based on real PV parameters and data), and strategically located with actual irradiance and temperature profile from a 1.4MW PV plant located at FIU. These PV locations were carefully selected to allow for detailed impact studies. The results show how the various SI functions impact the reactive power injection and switching of the capacitor banks, the voltage regulator switching, the losses in the feeder, the harmonic and the CII. The Volt-Watt with rise/fall rate-of-change limiting setting showed the least impact in terms of the CII, voltage regulator tapping and capacitor switching but with high amount of losses compared to other SI functions.","PeriodicalId":146759,"journal":{"name":"2021 IEEE Green Technologies Conference (GreenTech)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115235432","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: 4

[Copyright notice] (版权)

2021 IEEE Green Technologies Conference (GreenTech) Pub Date : 1900-01-01 DOI: 10.1109/greentech48523.2021.00003

引用次数: 0

Program Committee GREENTECH 2021 绿色科技2021项目委员会

2021 IEEE Green Technologies Conference (GreenTech) Pub Date : 1900-01-01 DOI: 10.1109/greentech48523.2021.00008

引用次数: 0

Organizing Committee GREENTECH 2021 2021绿色科技组委会

2021 IEEE Green Technologies Conference (GreenTech) Pub Date : 1900-01-01 DOI: 10.1109/greentech48523.2021.00007

引用次数: 0

Message from the Conference General Chair 大会主席致辞

2021 IEEE Green Technologies Conference (GreenTech) Pub Date : 1900-01-01 DOI: 10.1109/acsat.2014.5

{"title":"Message from the Conference General Chair","authors":"","doi":"10.1109/acsat.2014.5","DOIUrl":"https://doi.org/10.1109/acsat.2014.5","url":null,"abstract":"Comparison of various methods of computational intelligence are presented and illustrated with examples. These methods include neural networks, fuzzy systems, and evolutionary computation. The presentation is focused on neural networks, fuzzy systems and neurofuzzy architectures. Various leaning method of neural networks including supervised and unsupervised methods are presented and illustrated with examples. General learning rule as a function of the incoming signals is discussed. Other learning rules such as Hebbian learning, perceptron learning, LMS Least Mean Square learning, delta learning, WTA – Winner Take All learning, and PCA Principal Component Analysis are presented as a derivation of the general learning rule. Architecture specific learning algorithms for cascade correlation networks, Sarajedini and Hecht-Nielsen networks, functional link networks, polynomial networks, counterpropagation networks, RBF-Radial Basis Function networks are described. Dedicated learning algorithms for on chip neural network training are also evaluated. The tutorial focuses on various practical methods such as Quickprop, RPROP, Back Percolation, Delta-bar-Delta and others. Main reasons of convergence difficulties such as local minima or flat spot problems are analyzed. More advance gradient-based methods including pseudo inversion learning, conjugate gradient, Newton and LM LevenbergMarquardt Algorithm are illustrated with examples. Advantages and disadvantages of fuzzy systems will be presented. Detailed comparison of Mamdani and Takagi-Sugeno approaches will be given. Various neuro-fuzzy architectures will be discussed. In the conclusion advantages and disadvantages of neural and fuzzy approaches will be discussed with a reference to their hardware implementation.","PeriodicalId":146759,"journal":{"name":"2021 IEEE Green Technologies Conference (GreenTech)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131697352","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

Sponsors GREENTECH 2021

2021 IEEE Green Technologies Conference (GreenTech) Pub Date : 1900-01-01 DOI: 10.1109/greentech48523.2021.00010

引用次数: 0