2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA)最新文献

Sensitivity analysis of a bidirectional wireless charger for EV 电动汽车双向无线充电器的灵敏度分析

2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA) Pub Date : 2016-12-05 DOI: 10.1109/ICRERA.2016.7884506

A. T. Cabrera, J. A. Sanchez, M. Longo, F. Foiadelli

引用次数: 11

Linearized DQ averaged model of modular multilevel converter 模块化多电平变换器的线性化DQ平均模型

2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA) Pub Date : 2016-11-20 DOI: 10.1109/ICRERA.2016.7884458

Yashwant Sinha, A. Nampally

引用次数: 2

Modular multilevel converter modulation using fundamental switching selective harmonic elimination method 采用基波开关选择性谐波消除方法的模块化多电平变换器调制

2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA) Pub Date : 2016-11-20 DOI: 10.1109/ICRERA.2016.7884431

Yashwant Sinha, A. Nampally

引用次数: 18

Averaged model of modular multilevel converter in rotating DQ frame 旋转DQ框架中模块化多电平变换器的平均模型

2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA) Pub Date : 2016-11-20 DOI: 10.1109/ICRERA.2016.7884559

Yashwant Sinha, A. Nampally

引用次数: 2

Maximizing investment value of small-scale PV in a smart grid environment 智能电网环境下小型光伏投资价值最大化

2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA) Pub Date : 2016-11-03 DOI: 10.1109/ICRERA.2016.7884366

J. Every, Li Li, Youguang Guo, D. Dorrell

引用次数: 8

Distribution network reconfiguration in smart grid system using modified particle swarm optimization 基于改进粒子群算法的智能电网配电网重构

2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA) Pub Date : 2016-11-01 DOI: 10.1109/ICRERA.2016.7884556

I. Atteya, H. Ashour, N. Fahmi, D. Strickland

引用次数: 25

Image features for pixel-wise detection of solar photovoltaic arrays in aerial imagery using a random forest classifier 基于随机森林分类器的航空图像中太阳能光伏阵列逐像素检测的图像特征

2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA) Pub Date : 2016-11-01 DOI: 10.1109/ICRERA.2016.7884446

Jordan M. Malof, Kyle Bradbury, L. Collins, R. Newell, Alexander Serrano, Hetian Wu, Sam Keene

{"title":"Image features for pixel-wise detection of solar photovoltaic arrays in aerial imagery using a random forest classifier","authors":"Jordan M. Malof, Kyle Bradbury, L. Collins, R. Newell, Alexander Serrano, Hetian Wu, Sam Keene","doi":"10.1109/ICRERA.2016.7884446","DOIUrl":"https://doi.org/10.1109/ICRERA.2016.7884446","url":null,"abstract":"Power generation from distributed solar photovoltaic (PV) arrays has grown rapidly in recent years. As a result, there is interest in collecting information about the quantity, power capacity, and energy generated by such arrays; and to do so over small geo-spatial regions (e.g., counties, cities, or even smaller regions). Unfortunately, existing sources of such information are dispersed, limited in geospatial resolution, and otherwise incomplete or publically unavailable. As result, we recently proposed a new approach for collecting such distributed PV information that relies on computer algorithms to automatically detect PV arrays in high resolution aerial imagery [1], Here, we build on this work by investigating a detection algorithm based on a Random Forest (RF) classifier, and we consider its detection performance using several different sets of image features. The proposed method is developed and tested using a very large collection of publicly available [2] aerial imagery, covering 112.5 km2 of surface area, with 2,328 manually annotated PV array locations. The results indicate that a combination of local color and texture (using the popular texton feature) features yield the best detection performance.","PeriodicalId":287863,"journal":{"name":"2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116951858","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: 19

Implementation of a conventional DFIG stator flux oriented control scheme using industrial converters 采用工业变换器实现传统DFIG定子磁链定向控制方案

2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA) Pub Date : 2016-11-01 DOI: 10.1109/ICRERA.2016.7884544

N. Sarma, J. Apsley, S. Djurović

引用次数: 11

LCL-Filter design for a battery charger based on buck converter (DCDC converter) 基于降压变换器(DCDC变换器)的电池充电器lcl滤波器设计

2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA) Pub Date : 2016-11-01 DOI: 10.1109/ICRERA.2016.7884408

E. Şanal, P. Dost, C. Sourkounis

引用次数: 7

A deep convolutional neural network and a random forest classifier for solar photovoltaic array detection in aerial imagery 基于深度卷积神经网络和随机森林分类器的航空图像太阳能光伏阵列检测

2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA) Pub Date : 2016-11-01 DOI: 10.1109/ICRERA.2016.7884415

Jordan M. Malof, L. Collins, Kyle Bradbury, R. Newell

{"title":"A deep convolutional neural network and a random forest classifier for solar photovoltaic array detection in aerial imagery","authors":"Jordan M. Malof, L. Collins, Kyle Bradbury, R. Newell","doi":"10.1109/ICRERA.2016.7884415","DOIUrl":"https://doi.org/10.1109/ICRERA.2016.7884415","url":null,"abstract":"Power generation from distributed solar photovoltaic PV arrays has grown rapidly in recent years. As a result, there is interest in collecting information about the quantity, power capacity, and energy generated by such arrays; and to do so over small geo-spatial regions (e.g., counties, cities, or even smaller regions). Unfortunately, existing sources of such information are dispersed, limited in geospatial resolution, and otherwise incomplete or publically unavailable. As result, we recently proposed a new approach for collecting such distributed PV information that relies on computer algorithms to automatically detect PV arrays in high resolution aerial imagery [1], Here we build on this work by investigating two machine learning algorithms for PV array detection: a Random Forest classifier (RF) [2] and a deep convolutional neural network (CNN) [3]. We use the RF algorithm as a benchmark, or baseline, for comparison with a CNN model. The two models are developed and tested using a large collection of publicly available [4] aerial imagery, covering 135 km2, and including over 2,700 manually annotated distributed PV array locations. The results indicate that the CNN substantially improves over the RF. The CNN is capable of excellent performance, detecting nearly 80% of true panels with a precision measure of 72%.","PeriodicalId":287863,"journal":{"name":"2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122299351","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: 38