Energy Harvesting and Systems最新文献_第4页

Maximum power point tracking techniques using improved incremental conductance and particle swarm optimizer for solar power generation systems 基于改进增量电导和粒子群优化器的太阳能发电系统最大功率点跟踪技术

Energy Harvesting and Systems Pub Date : 2023-05-10 DOI: 10.1515/ehs-2022-0120

Akwasi Amoh Mensah, Xie Wei, Duku Otuo-Acheampong, Tumbiko Mbuzi

{"title":"Maximum power point tracking techniques using improved incremental conductance and particle swarm optimizer for solar power generation systems","authors":"Akwasi Amoh Mensah, Xie Wei, Duku Otuo-Acheampong, Tumbiko Mbuzi","doi":"10.1515/ehs-2022-0120","DOIUrl":"https://doi.org/10.1515/ehs-2022-0120","url":null,"abstract":"Abstract The generation of power from solar energy by using Photovoltaic (PV) systems to convert the irradiation of the sun into electricity has been adopted over the past years. However, the PV system’s P–V and I–V characteristics become unstable when solar irradiation and temperature change. In this paper, the incremental conductance (INC) has been improved using signals to measure the current and voltage from the PV systems directly which quickly changes with the environmental conditions, and the conventional particle swarm optimization (PSO) is modified so that under multiple shaded peak PV array curves with fast-changing solar irradiance and temperature, more power is extracted at a faster rate without any tracking failure at high-speed tracking of both individual maximum power point (IMPP) and global maximum power point (GMPP) under varying solar irradiance and temperature at a longer distance to enhance the power generated. The individual and global coefficients are also improved to change with multiple shaded peak PV array curves with fast-changing solar irradiance and temperature. DC-DC converter converts DC power from one circuit to another and DC-AC inverter converts DC power to AC power. Simulation was carried out in MATLAB Simulink with different solar irradiance and temperature whereby the conventional INC and PSO were compared with the proposed INC and PSO. An experiment was carried out for a whole day from 8 am to 5 pm to test the validity of the proposed algorithm and compared it with the conventional INC and PSO by using the solar irradiance and temperature received. From both the simulation and experimental results, the proposed INC and PSO performed better by attaining high power and tracking speed with stable output results than the conventional INC and PSO.","PeriodicalId":36885,"journal":{"name":"Energy Harvesting and Systems","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73334709","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: 1

Large-scale green hydrogen production using alkaline water electrolysis based on seasonal solar radiation 基于季节太阳辐射的碱性电解大规模绿色制氢

Energy Harvesting and Systems Pub Date : 2023-05-05 DOI: 10.1515/ehs-2023-0011

Q. Hassan, A. Z. Sameen, H. M. Salman, M. Jaszczur

引用次数: 4

Diffusion induced thermal effect and stress in layered Li(Ni_0.6Mn_0.2Co_0.2)O₂ cathode materials for button lithium-ion battery electrode plates 纽扣式锂离子电池极板层状Li(Ni0.6Mn0.2Co0.2)O2正极材料扩散诱导的热效应和应力

Energy Harvesting and Systems Pub Date : 2023-05-01 DOI: 10.1515/ehs-2022-0095

Lipeng Xu, Chongwang Tian, Chunjiang Bao, Fei Zhou, Jinsheng Zhao

{"title":"Diffusion induced thermal effect and stress in layered Li(Ni0.6Mn0.2Co0.2)O2 cathode materials for button lithium-ion battery electrode plates","authors":"Lipeng Xu, Chongwang Tian, Chunjiang Bao, Fei Zhou, Jinsheng Zhao","doi":"10.1515/ehs-2022-0095","DOIUrl":"https://doi.org/10.1515/ehs-2022-0095","url":null,"abstract":"Abstract This paper develops a coupling model of the relationship between chemical reaction, temperature and stress/strain for Li (Ni 0.6 Mn 0.2 Co 0.2 ) O 2 cathode materials. With the process of reaction, the concentration of electrolyte salt changes rapidly at the beginning of diffusion and tends to dynamic equilibrium. The concentration of electrolyte LiPF 6 in electrode materials diffuses from bottom to top with the process of lithium intercalation. In the process of Li-ion intercalation, the temperature rise of porous electrode materials increases sharply at first, then decreases and then increases slowly. The rate of temperature rise in the cathode material increases with the temperature decreases. The volume of electrode material deformed with the expansion along the X -axis and the radial bending along the Y -axis. And the law of stress variation with time is consistent with the temperature-time curve. By the stress-strain distribution nephogram, it is found that the position where the maximum stress is located at the edge of the upper surface, and which is most vulnerable to failure.","PeriodicalId":36885,"journal":{"name":"Energy Harvesting and Systems","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136048803","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: 0

A novel SGD-DLSTM-based efficient model for solar power generation forecasting system 基于sgd - dlstm的太阳能发电预测系统高效模型

Energy Harvesting and Systems Pub Date : 2023-04-28 DOI: 10.1515/ehs-2022-0129

Surender Rangaraju, A. Bhaumik, Phu Le Vo

{"title":"A novel SGD-DLSTM-based efficient model for solar power generation forecasting system","authors":"Surender Rangaraju, A. Bhaumik, Phu Le Vo","doi":"10.1515/ehs-2022-0129","DOIUrl":"https://doi.org/10.1515/ehs-2022-0129","url":null,"abstract":"Abstract Globally, Solar Power (SP) is generated by employing Photovoltaic (PV) systems. Accurate forecasting of PV power is a critical issue in ensuring secure operation along with economic incorporation of PV in smart grids. For providing an accurate forecasting model, various prevailing methodologies have been developed even then, there requires a huge enhancement. Thus, for Solar Power Generation (SPG) forecasting with deviation analysis, a novel Strengthen Gaussian Distribution-centric Deep Long Short Term Memory (SGD-DLSTM) methodology has been proposed here. Firstly, the PV modelling is formulated. After that, as of the PV, the data is gathered; likewise, for the deviation analysis, the historical data is gathered. Next, the pre-processing is performed; this stage undergoes two steps namely the Missing Value (MV) imputation and the scaling process. Afterwards, the features pertinent to the weather condition along with SP are extracted. After that, by utilizing the Intensive Exploitation-centric Shell Game Optimizer (IESGO) algorithm, the significant features are selected as of the features extracted. Then, the SPG is predicted by inputting the selected features into the SGD-DLSTM classifier. Next, by computing the Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE) measures, the predicted outcome’s deviation is assessed. In the experimental evaluation, by means of these measures, the proposed system’s performance is contrasted with the conventional techniques. Therefore, from the experimental assessment, it was established that the proposed model exhibits better performance than the prevailing research works. When analogized to the prevailing methodologies, a better accuracy of 97.25% was attained by the proposed system.","PeriodicalId":36885,"journal":{"name":"Energy Harvesting and Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79286570","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: 0

Industrial gas turbine performance prediction and improvement – a case study 工业燃气轮机性能预测与改进——一个案例研究

Energy Harvesting and Systems Pub Date : 2023-04-27 DOI: 10.1515/ehs-2022-0094

H. Mzad, Fethi Bennour

{"title":"Industrial gas turbine performance prediction and improvement – a case study","authors":"H. Mzad, Fethi Bennour","doi":"10.1515/ehs-2022-0094","DOIUrl":"https://doi.org/10.1515/ehs-2022-0094","url":null,"abstract":"Abstract The gas turbines (GTs), model M3142R/GE MS 3002, equipping the natural gas compression and crude oil pumping stations of SONATRACH’s pipeline transport of hydrocarbons activity are robust despite their dilapidation and state of service, but mediocre in terms of energy efficiency and power output. According to the manufacturer, for temperatures ranging from 15 to 47 °C, the efficiency of these machines drops from 26.78 to 25.03 % and their power drops from 11.29 to 8.9191 MW. It should be noted, however, that the number of these turbines exceeds 80 units and that their operation dates from 1974. The intention of this paper is to improve the gas turbine performance, mainly the efficiency and shaft power, by an evaporative cooling process of the compressor intake air. Besides, it is proposed to lower the upper limit power in periods of high temperatures to reduce gas consumption. To achieve these objectives, a mathematical model was implemented under Matlab R16, which reproduced the real behavior of these machines. It follows that this simulation made it possible to highlight relevant gains in terms of power and efficiency of the order of 1.361 MW and 3.4 % at a temperature of 47 °C.","PeriodicalId":36885,"journal":{"name":"Energy Harvesting and Systems","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88817910","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: 0

Optimized power flow management based on Harris Hawks optimization for an islanded DC microgrid 基于Harris Hawks优化的孤岛直流微电网潮流优化管理

Energy Harvesting and Systems Pub Date : 2023-04-14 DOI: 10.1515/ehs-2022-0153

Harin M. Mohan, Santanu Kumar Dash

{"title":"Optimized power flow management based on Harris Hawks optimization for an islanded DC microgrid","authors":"Harin M. Mohan, Santanu Kumar Dash","doi":"10.1515/ehs-2022-0153","DOIUrl":"https://doi.org/10.1515/ehs-2022-0153","url":null,"abstract":"Abstract This article presents an energy management system (EMS) in a DC microgrid (MG) operating in an islanded mode to control the power flow in the distribution network. The microgrid system considered in this research consists of distributed generation sources like a solar photovoltaic system, a fuel cell energy system, and an energy storage system controlled by an optimized energy management system. As the distributed energy sources used are primarily renewable, unpredictable weather conditions may cause irregular energy generation. These variations impact the power flow in the DC bus, making it challenging to maintain a supply and demand balance. Therefore, an intelligent energy management system using the Harris Hawks Optimization (HHO) is implemented to enhance the microgrid’s performance and efficiency. The HHO algorithm is based on the hunting nature of the Harris Hawks, and the EMS is developed to maintain the optimal power flow and to handle the constraints. The performance of the presented system is analyzed with the particle swarm optimization (PSO) based Proportional Integral (PI) controller in different operating scenarios to validate the effectiveness of the DC microgrid system.","PeriodicalId":36885,"journal":{"name":"Energy Harvesting and Systems","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90675864","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: 1

Evaluation of performances in DI Diesel engine with different split injection timings 直喷式柴油机不同分喷正时的性能评价

Energy Harvesting and Systems Pub Date : 2023-04-06 DOI: 10.1515/ehs-2023-0010

G. Balamurugan, S. Gowthaman

引用次数: 0

Power flow control and power oscillation damping in a 2-machine system using SSSC during faults 基于SSSC的双机系统故障时的潮流控制与功率振荡阻尼

Energy Harvesting and Systems Pub Date : 2023-04-03 DOI: 10.1515/ehs-2022-0105

Kartikey Sharma, A. A. Nimje, Shanker D. Godwal

{"title":"Power flow control and power oscillation damping in a 2-machine system using SSSC during faults","authors":"Kartikey Sharma, A. A. Nimje, Shanker D. Godwal","doi":"10.1515/ehs-2022-0105","DOIUrl":"https://doi.org/10.1515/ehs-2022-0105","url":null,"abstract":"Abstract With the rapidly growing population, energy demand is increasing. The power supply to consumers must be free from distortions. By injecting voltage in quadrature with line current and varying the magnitude, the SSSC offers series compensation to the line. The injected voltage, which offers the effect of inserting an inductive or else capacitive reactance in series with the transmission line, is in quadrature with the line current. Using MATLAB/Simulink software, a phasor model of a 2-machine device with SSSC integration and POD as a subsidiary controller is simulated in this paper to evaluate efficient power flow regulation. The simulation has been used to study the time domain behavior of SSSC under normal and faulty conditions. The SSSC is implemented for correcting the voltage and analyzing power responses during a low voltage fault in the power system, whereas in normal conditions, the power system’s voltage stability for maintaining steady acceptable voltages at every bus is analyzed. It has been revealed that POD controller assists SSSC by supplying the reference voltage signal to damp out the low frequency power oscillations. The objective of this study is to reduce the crest outreach and clearing time during the fault thus improving the transient stability.","PeriodicalId":36885,"journal":{"name":"Energy Harvesting and Systems","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89001075","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: 0

Impact of using a predictive neural network of multi-term zenith angle function on energy management of solar-harvesting sensor nodes 多项天顶角函数预测神经网络对太阳能采集传感器节点能量管理的影响

Energy Harvesting and Systems Pub Date : 2023-03-30 DOI: 10.2139/ssrn.4144365

M. Al-Omary, R. Aljarrah, Aiman Albatayneh, Dua'a Alshabi, Khaled Alzaareer

{"title":"Impact of using a predictive neural network of multi-term zenith angle function on energy management of solar-harvesting sensor nodes","authors":"M. Al-Omary, R. Aljarrah, Aiman Albatayneh, Dua'a Alshabi, Khaled Alzaareer","doi":"10.2139/ssrn.4144365","DOIUrl":"https://doi.org/10.2139/ssrn.4144365","url":null,"abstract":"Abstract Using the Neural Networks to predict solar harvestable energy would contribute to prolonging the duration of the effective operation and thus less consumption in solar-harvesting sensor nodes. The NNs with higher prediction accuracy have the longest effective operation. Till now, the NNs that use the zenith angle function as input have been utilized with only two terms. This paper shows the advantages of using a multi-term zenith angle function on the energy management in the nodes. To this end, this paper considers two, three, and four terms for the function of the zenith angle. The results showed that the case of four terms has the lowest prediction mistakes on average (0.83%) compared to (2.13% and 1.75%) for the cases of two and three terms, respectively. This is followed by a reduction in energy consumption in favor of four terms case. For one month simulation period with hourly prediction, the sensor node worked at the higher consumption mode (M2) in the case of four terms 4 hours less than three terms and 7 hours less than two terms case. Thus, increasing the number of terms in the zenith angle function leads to higher accuracy and less energy consumption.","PeriodicalId":36885,"journal":{"name":"Energy Harvesting and Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72530738","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: 3

Impact of using a predictive neural network of multi-term zenith angle function on energy management of solar-harvesting sensor nodes 多项天顶角函数预测神经网络对太阳能采集传感器节点能量管理的影响

Energy Harvesting and Systems Pub Date : 2023-03-30 DOI: 10.1515/ehs-2022-0141

Murad Al-Omary, Rafat Aljarrah, Aiman Albatayneh, Dua’a Alshabi, Khaled Alzaareer

{"title":"Impact of using a predictive neural network of multi-term zenith angle function on energy management of solar-harvesting sensor nodes","authors":"Murad Al-Omary, Rafat Aljarrah, Aiman Albatayneh, Dua’a Alshabi, Khaled Alzaareer","doi":"10.1515/ehs-2022-0141","DOIUrl":"https://doi.org/10.1515/ehs-2022-0141","url":null,"abstract":"Abstract Using the Neural Networks to predict solar harvestable energy would contribute to prolonging the duration of the effective operation and thus less consumption in solar-harvesting sensor nodes. The NNs with higher prediction accuracy have the longest effective operation. Till now, the NNs that use the zenith angle function as input have been utilized with only two terms. This paper shows the advantages of using a multi-term zenith angle function on the energy management in the nodes. To this end, this paper considers two, three, and four terms for the function of the zenith angle. The results showed that the case of four terms has the lowest prediction mistakes on average (0.83%) compared to (2.13% and 1.75%) for the cases of two and three terms, respectively. This is followed by a reduction in energy consumption in favor of four terms case. For one month simulation period with hourly prediction, the sensor node worked at the higher consumption mode (M2) in the case of four terms 4 hours less than three terms and 7 hours less than two terms case. Thus, increasing the number of terms in the zenith angle function leads to higher accuracy and less energy consumption.","PeriodicalId":36885,"journal":{"name":"Energy Harvesting and Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135419650","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: 0