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Titlebook: Advances in Renewable Energy and Its Grid Integration; Proceedings of ICAER Suryanarayana Doolla,Zakir Hussain Rather,Venkatas Conference p

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發(fā)表于 2025-3-21 16:11:09 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Advances in Renewable Energy and Its Grid Integration
期刊簡稱Proceedings of ICAER
影響因子2023Suryanarayana Doolla,Zakir Hussain Rather,Venkatas
視頻videohttp://file.papertrans.cn/150/149569/149569.mp4
發(fā)行地址Presents research relevant to academics, industry experts, and policymakers.Promotes communication between diverse research areas.Provides a bridge to discuss challenges faced by academics and industr
學(xué)科分類Lecture Notes in Electrical Engineering
圖書封面Titlebook: Advances in Renewable Energy and Its Grid Integration; Proceedings of ICAER Suryanarayana Doolla,Zakir Hussain Rather,Venkatas Conference p
影響因子.This book presents selected papers from the 8th International Conference on Advances in Energy Research (ICAER 2022), providing coverage encompassing advanced conventional energy technology, renewable and non-conventional energy technology, electric mobility, energy storage, energy, environment and society, industry innovations in energy, sector-coupled energy system, and energy education. The contents of this book are of use to researchers from not only scientific background, but also economics and anthropology. It encourages researchers to conduct research on the ways to assess and analyse the acceptance of the novel energy forms among the mass population from a financial and social perspective..
Pindex Conference proceedings 2023
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Real Power Loss Minimisation and Energy Cost Saving with DG and Capacitor Using JAYA Algorithm in Ron of size for DG and capacitor, (ii) Hybrid approach by combining sensitivity-based index VSI to find location of DGs and capacitors and obtaining sizes using JAYA. The obtained results with the two approaches are compared and analysed. Results is obtained for IEEE 34 bus radial distribution system
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Fault Classification of Dry Type Transformer Using Pattern Recognition Neural Network,nition-based method for detecting various DTT failure states is presented. In this suggested technique the real time parameters of the DTT were collected, normalized and then used as an ANN inputs. Four different classes of DTT conditions: healthy, open circuit, short circuit and overload faults wer
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Development of Integrated Test Set for SoC-SoH Estimation of Lithium-Ion Battery,e range. The test set has provision to monitor the battery voltage, charge/discharge current, cell temperature, and transfer of data to a Flash drive or PC using a USB port. A laboratory prototype has been successfully developed and test results are used for SoC estimation using different methods. T
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A Novel Hybrid Islanding Detection Technique in Multi DG Microgrid System,ying loads. In this study, a unique HID method based on VU and rate of change of frequency (ROCOF) is presented. The proposed HID is compared to a hybrid method presently in use that is based on VU and frequency setpoint (FSP). According to simulation results, the suggested approach identifies islan
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Life Cycle Assessment of a Hybrid Solar Based Electric Vehicle Charging Station Using SimaPro,int of the parking station. The system has a renewable energy share of 72.3% in its lifetime of 25?years. The system gave the discounted payback period of 5.15?years so, in the remaining 19.85?years it will bring in the profit to the charging station operator. The environmental performance of the wi
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Potential Assessment for Repowering of Solar Projects in India,trigger penalties, underutilization of evacuated infrastructure and land. Repowering of operational RE projects can address the above-mentioned issues by installing additional solar PV capacity to fill the energy generation gap up to the permissible limit of respective PPA and up to the best possibl
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Machine Learning Based Prediction of Solar Power Plant Performance Under the Impact of Natural Dustodels show the highest accuracy in predicting data for the horizontally placed panels. As a validation, the ANN model offers an accuracy of over 99%, whereas the LSTM model provides with an accuracy of over 92%. These prediction study will help in predicting the performance of the location specific
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