调戏
发表于 2025-3-21 16:11:09
书目名称Advances in Renewable Energy and Its Grid Integration影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0149569<br><br> <br><br>书目名称Advances in Renewable Energy and Its Grid Integration影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0149569<br><br> <br><br>书目名称Advances in Renewable Energy and Its Grid Integration网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0149569<br><br> <br><br>书目名称Advances in Renewable Energy and Its Grid Integration网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0149569<br><br> <br><br>书目名称Advances in Renewable Energy and Its Grid Integration被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0149569<br><br> <br><br>书目名称Advances in Renewable Energy and Its Grid Integration被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0149569<br><br> <br><br>书目名称Advances in Renewable Energy and Its Grid Integration年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0149569<br><br> <br><br>书目名称Advances in Renewable Energy and Its Grid Integration年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0149569<br><br> <br><br>书目名称Advances in Renewable Energy and Its Grid Integration读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0149569<br><br> <br><br>书目名称Advances in Renewable Energy and Its Grid Integration读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0149569<br><br> <br><br>
攀登
发表于 2025-3-21 22:29:01
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Minutes
发表于 2025-3-22 04:02:45
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
删减
发表于 2025-3-22 07:40:38
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先驱
发表于 2025-3-22 10:02:52
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
观察
发表于 2025-3-22 14:11:44
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
昆虫
发表于 2025-3-22 18:18:29
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
Engaging
发表于 2025-3-23 00:22:05
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
PAGAN
发表于 2025-3-23 03:28:50
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
上腭
发表于 2025-3-23 07:40:49
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