2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON)最新文献

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2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 2022-10-02 DOI: 10.1109/CCGRID.2005.1558517

引用次数: 0

FogGIS: Fog Computing for geospatial big data analytics FogGIS:用于地理空间大数据分析的雾计算

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 2016-12-10 DOI: 10.1109/UPCON.2016.7894725

Rabindra Kumar Barik, Harishchandra Dubey, A. Samaddar, Rajan D. Gupta, P. Ray

引用次数: 83

A compact two element MIMO antenna system for pattern and polarization diversity 一种用于方向图和极化分集的紧凑双单元MIMO天线系统

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894733

Yashika Sharma, D. Sarkar, K. Saurav, K. V. Srivastava

引用次数: 4

Cylindrical Bragg waveguide based temperature sensors 基于圆柱形布拉格波导的温度传感器

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894630

R. Chourasia, Vivek Singh

引用次数: 3

Grid integration of PV system using Marx multilevel inverter 并网光伏系统采用马克思多级逆变器

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894731

M. K. Lingam, P. Samuel

引用次数: 2

Optimal scheduling of uncertain wind energy and demand response in unit commitment using binary grey wolf optimizer (BGWO) 基于二元灰狼优化器(BGWO)的机组承诺不确定风能和需求响应优化调度

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894677

Srikanth Reddy K, L. Panwar, B. K. Panigrahi, R. Kumar

{"title":"Optimal scheduling of uncertain wind energy and demand response in unit commitment using binary grey wolf optimizer (BGWO)","authors":"Srikanth Reddy K, L. Panwar, B. K. Panigrahi, R. Kumar","doi":"10.1109/UPCON.2016.7894677","DOIUrl":"https://doi.org/10.1109/UPCON.2016.7894677","url":null,"abstract":"The uncertain wind energy handling is a vital aspect of modern power system operational planning with considerable wind penetration. The handling of same is a techno-economic constrained procedure considering its effect on energy and reserve scheduling of whole generation mix. This paper presents a dynamic penalty cost based methodology to solve power system resource scheduling problem with uncertain wind energy, thermal units and responsive loads using binary grey wolf optimizer (BGWO). The proposed methodology considers the effect of uncertain wind energy in terms of total rescheduling cost, total energy balancing cost and total reserve cost. The unit commitment procedure is solved using BGWO and the economic dispatch of committed thermal units alongside the wind energy, responsive load scheduling is solved using Lambda iteration technique. In addition, two different levels of wind uncertainty level are considered to examine the variation techno-economic aspects of proposed methodology. The simulation results are presented and discussed with respect to various performance attributes and the same demonstrate the superior performance of proposed dynamic penalty cost models over existing static cost models.","PeriodicalId":151809,"journal":{"name":"2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON)","volume":"81 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":"126998652","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: 6

Receding horizon based greenhouse air temperature control using grey wolf optimization algorithm 基于灰狼优化算法的后退地平线温室空气温度控制

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894620

R. Singhal, R. Kumar

引用次数: 5

Cut detection using local image descriptor 使用局部图像描述符进行切割检测

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894683

T. Kar, P. Kanungo

引用次数: 2

Long term wind power forecast using adaptive wavelet neural network 基于自适应小波神经网络的风电长期预测

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894735

B. Kanna, S. N. Singh

{"title":"Long term wind power forecast using adaptive wavelet neural network","authors":"B. Kanna, S. N. Singh","doi":"10.1109/UPCON.2016.7894735","DOIUrl":"https://doi.org/10.1109/UPCON.2016.7894735","url":null,"abstract":"With the growing uncertainty due to high wind power penetration, an accurate wind power forecast tool is very much essential for economic and stable operation of the electricity markets. It helps the system operators, to include wind generation into economic scheduling, unit commitment and reserve allocation problems. It also assists the wind power producers to minimize their losses through strategic bidding in the day ahead electricity markets. In this paper the problem of long-term wind power forecast is addressed, considering the numerical weather prediction (NWP) system wind speed and wind direction forecasts as inputs. An adaptive wavelet neural network is proposed for mapping the NWP's wind speed and wind direction forecasts to wind power forecasts. Wind direction inheritantly being a circular variable, for better training and function approximation, a transformed version of wind direction variables are used as inputs. Further, a closest set of patterns based on euclidean distance are chosen for training patterns and block wise training and forecast strategy is employed for carrying wind power forecast. The results show that the significant improvement over persistence method is achieved.","PeriodicalId":151809,"journal":{"name":"2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON)","volume":"40 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":"122446811","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: 20

Analysis of satellite solar power station as base load power plant 卫星太阳能电站作为基载电站的分析

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894714

Deepak Kumar, Kalpana Chaudhary

引用次数: 0