2019 International Conference on Energy Management for Green Environment (UEMGREEN)最新文献_第2页

IEEE Uemgreen 2019

2019 International Conference on Energy Management for Green Environment (UEMGREEN) Pub Date : 2019-09-01 DOI: 10.1109/uemgreen46813.2019.9221376

引用次数: 0

Implementation of a high accuracy ac current sensing scheme using hall-sensor 采用霍尔传感器实现了一种高精度交流电流传感方案

2019 International Conference on Energy Management for Green Environment (UEMGREEN) Pub Date : 2019-09-01 DOI: 10.1109/UEMGREEN46813.2019.9221592

A. Datta, K. Raj, R. Sarker

引用次数: 1

A Supervised Hybrid Algorithm based DSTATCOM to Cater to Dynamic Load Changes 一种适应动态负载变化的DSTATCOM监督混合算法

2019 International Conference on Energy Management for Green Environment (UEMGREEN) Pub Date : 2019-09-01 DOI: 10.1109/UEMGREEN46813.2019.9221544

Epsita Das, Aakash Bhattacharjee, Sukalyan Roy, Biswarup Ganguly, A. Banerji, S. Biswas

{"title":"A Supervised Hybrid Algorithm based DSTATCOM to Cater to Dynamic Load Changes","authors":"Epsita Das, Aakash Bhattacharjee, Sukalyan Roy, Biswarup Ganguly, A. Banerji, S. Biswas","doi":"10.1109/UEMGREEN46813.2019.9221544","DOIUrl":"https://doi.org/10.1109/UEMGREEN46813.2019.9221544","url":null,"abstract":"Renewable energies like Photo Voltaic (PV)/ solar power are abundantly available and waiting to be harnessed. Being weak system renewable energy based power systems require reactive power generation close to load to unburden the source. Study reveals fixed tuned Proportional & Integral (PI) controller based DSTATCOM may not be able to provide satisfactory voltage regulation with wide load changes. DSTATCOM generally requires tuning of PI controllers by utility engineers during installation. This process is mostly trial and error approach. It is necessary to re-tune the DSTATCOM controller when there is change in operating condition. To ensure automatic control action irrespective of load conditions, soft-computing technique is implemented in the DSTATCOM. A supervised hybrid algorithm named Neuro- Fuzzy controller is adopted to ensure automatic adaptation of the controller parameters during changing load conditions. The paper presents a Synchronous Reference Frame theory (SRF) based DSTATCOM on MATLAB platform and uses a Neuro- Fuzzy inference system to get a better response in terms of dynamic voltage profile and Total Harmonic Distortion (THD) as compared to simple PI Controller.","PeriodicalId":199125,"journal":{"name":"2019 International Conference on Energy Management for Green Environment (UEMGREEN)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122768705","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: 2

Modeling and control of a PEM fuel cell performance using Artificial Neural Networks to maximize the real time efficiency 利用人工神经网络对PEM燃料电池性能进行建模和控制，以实现实时效率最大化

2019 International Conference on Energy Management for Green Environment (UEMGREEN) Pub Date : 2019-09-01 DOI: 10.1109/UEMGREEN46813.2019.9221428

Sankhadeep Ghosh, A. Routh, M. Rahaman, A. Ghosh

{"title":"Modeling and control of a PEM fuel cell performance using Artificial Neural Networks to maximize the real time efficiency","authors":"Sankhadeep Ghosh, A. Routh, M. Rahaman, A. Ghosh","doi":"10.1109/UEMGREEN46813.2019.9221428","DOIUrl":"https://doi.org/10.1109/UEMGREEN46813.2019.9221428","url":null,"abstract":"In recent years, the proton exchange membrane (PEM) fuel cell is regarded as the best choice in the next generation automobile power source owing to its high fuel conversion efficiency, low noise, almost zero emissions, and low operating temperature. The working condition of PEM fuel cell depends upon several environmental parameters including the flow rate of fuel and oxidant, cell temperature, catalyst activity, and cell fittings. Mostly the data driven techniques are used to predict the voltage and power losses from a fuel cell in particular time. So instead of using a whole analytical model of fuel cell it is better to use Artificial Neural Network (ANN) model due to some of the parameters are very difficult to measure with respect to time. In this present work, it is investigated to develop a PEM fuel cell model using ANN technique. The experimental test on a real time fuel cell has been carried out to validate the ANN model. The different set of operating data is investigated with changing the environmental parameter. The ANN model is applied to emulate real operating conditions such as temperature, hydrogen consumption. After analysis the results it can be concluded that this presented model have good accuracy. Moreover, ANN learning methodology can be implemented to improve the PEM fuel cell stack efficiency. The model is implemented to determine the I-V performance of a single cell PEM fuel cell at different operating settings. The model could obtain the optimized values for the input variables corresponding to the value of objective function. Results showed a consistency between experimental data and the data made by the model. Therefore, it is indicated that the developed model is an effective method, which can predict the performance of fuel cell with high accuracy.","PeriodicalId":199125,"journal":{"name":"2019 International Conference on Energy Management for Green Environment (UEMGREEN)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125967320","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: 2

Uemgreen 2019 Paper List Uemgreen 2019论文清单

2019 International Conference on Energy Management for Green Environment (UEMGREEN) Pub Date : 2019-09-01 DOI: 10.1109/uemgreen46813.2019.9221507

引用次数: 0

Proceedings Front Page 会议记录首页

2019 International Conference on Energy Management for Green Environment (UEMGREEN) Pub Date : 2019-09-01 DOI: 10.1109/uemgreen46813.2019.9221499

引用次数: 0