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.
{"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}
Q. Hassan, A. Z. Sameen, H. M. Salman, M. Jaszczur
Abstract The research study provides a techno-economic analysis for the green hydrogen generation based solar radiation data for both the single and hybrid alkaline water electrolyzer and energy storage system systems. In addition, a carbon footprint study is conducted to estimate the developed system carbon dioxide emissions. The optimal size of the alkaline water electrolyzer and energy storage system is determined by a genetic algorithm that takes into account a carbon tax on carbon emissions. Based on itemized cost estimating findings, unit hydrogen production costs for a single system and a hybrid system were $6.88/kg and $8.32/kg respectively. Furthermore, capital cost it has been found as a key element in determining the optimal scale of the alkaline water electrolyzer and energy storage system, which are essential for minimizing the unit hydrogen production cost. Lastly, an effort to minimize the capital cost of producing green hydrogen is required when the rising trend of the carbon dioxide tax is taken into account.
{"title":"Large-scale green hydrogen production using alkaline water electrolysis based on seasonal solar radiation","authors":"Q. Hassan, A. Z. Sameen, H. M. Salman, M. Jaszczur","doi":"10.1515/ehs-2023-0011","DOIUrl":"https://doi.org/10.1515/ehs-2023-0011","url":null,"abstract":"Abstract The research study provides a techno-economic analysis for the green hydrogen generation based solar radiation data for both the single and hybrid alkaline water electrolyzer and energy storage system systems. In addition, a carbon footprint study is conducted to estimate the developed system carbon dioxide emissions. The optimal size of the alkaline water electrolyzer and energy storage system is determined by a genetic algorithm that takes into account a carbon tax on carbon emissions. Based on itemized cost estimating findings, unit hydrogen production costs for a single system and a hybrid system were $6.88/kg and $8.32/kg respectively. Furthermore, capital cost it has been found as a key element in determining the optimal scale of the alkaline water electrolyzer and energy storage system, which are essential for minimizing the unit hydrogen production cost. Lastly, an effort to minimize the capital cost of producing green hydrogen is required when the rising trend of the carbon dioxide tax is taken into account.","PeriodicalId":36885,"journal":{"name":"Energy Harvesting and Systems","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89663830","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}
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.
摘要建立了Li (Ni 0.6 Mn 0.2 Co 0.2) o2正极材料的化学反应、温度和应力/应变关系的耦合模型。随着反应的进行，电解质盐浓度在扩散开始时变化迅速，趋于动态平衡。电极材料中电解质lipf6浓度随锂嵌入过程自下而上扩散。在锂离子嵌入过程中，多孔电极材料的温升先急剧上升，然后下降，再缓慢上升。阴极材料的升温速率随温度的降低而增大。电极材料体积随X轴向的膨胀和Y轴向的径向弯曲而发生变形。应力随时间的变化规律与温度-时间曲线一致。通过应力-应变分布云图发现，最大应力位置位于上表面边缘，最容易发生破坏。
{"title":"Diffusion induced thermal effect and stress in layered Li(Ni<sub>0.6</sub>Mn<sub>0.2</sub>Co<sub>0.2</sub>)O<sub>2</sub> 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}
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.
{"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}
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.
SONATRACH公司烃类管道输送天然气压缩站和原油泵站所装备的M3142R/GE MS 3002型燃气轮机(GTs)虽然破旧且处于使用状态，但性能稳定，但能效和输出功率一般。根据制造商的说法，在15到47 °C的温度范围内，这些机器的效率从26.78下降到25.03 %，功率从11.29下降到8.9191 MW。但是，应当指出，这些涡轮机的数量超过80台，它们的运行时间从1974年开始。本文的目的是通过压缩机进气的蒸发冷却过程来提高燃气轮机的性能，主要是效率和轴功率。此外，还建议在高温时降低上限功率，以减少燃气消耗。为了实现这些目标，在Matlab R16下实现了一个数学模型，该模型再现了这些机器的真实行为。由此可见，在47 °C的温度下，该模拟可以突出显示功率和效率方面的相关增益，分别为1.361 MW和3.4 %。
{"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}
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.
{"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}
Abstract An injection mechanism which is split injection was found to reduce emissions in Diesel engines. In this mechanism, split injection proportion and split injection timing was varied and analyzed to reduce engine emissions. Injection proportion was varied at 25% of the pilot and 75% of the fuel as main injection and timing as 54° ATDC (after top dead center) and 40° ATDC for split injection. Since a homogeneous mixture occurs in this pilot injection, combustion is becoming complete for Diesel Engine. Hence, BTE was increased by 1.5% for timing 40° ATDC and 12° BTDC (before top dead center) and reduced by 1.4% for timing 54° ATDC and 12° BTDC. The reduction in BTE for 54° ATDC is because the increase in timing increases cooling effect of air and combustion rating was reduced. Also, combustion takes place at low temperature itself due to homogeneous mixture. So, NOx emission was also reduced by 8.4% and 18.6% for 40° ATDC and 54° ATDC injection timing respectively. The other emissions like HC and CO were also observed to be reduced upto 35% and 11% respectively due to increase in homogeneous mixture in Diesel Engine.
{"title":"Evaluation of performances in DI Diesel engine with different split injection timings","authors":"G. Balamurugan, S. Gowthaman","doi":"10.1515/ehs-2023-0010","DOIUrl":"https://doi.org/10.1515/ehs-2023-0010","url":null,"abstract":"Abstract An injection mechanism which is split injection was found to reduce emissions in Diesel engines. In this mechanism, split injection proportion and split injection timing was varied and analyzed to reduce engine emissions. Injection proportion was varied at 25% of the pilot and 75% of the fuel as main injection and timing as 54° ATDC (after top dead center) and 40° ATDC for split injection. Since a homogeneous mixture occurs in this pilot injection, combustion is becoming complete for Diesel Engine. Hence, BTE was increased by 1.5% for timing 40° ATDC and 12° BTDC (before top dead center) and reduced by 1.4% for timing 54° ATDC and 12° BTDC. The reduction in BTE for 54° ATDC is because the increase in timing increases cooling effect of air and combustion rating was reduced. Also, combustion takes place at low temperature itself due to homogeneous mixture. So, NOx emission was also reduced by 8.4% and 18.6% for 40° ATDC and 54° ATDC injection timing respectively. The other emissions like HC and CO were also observed to be reduced upto 35% and 11% respectively due to increase in homogeneous mixture in Diesel Engine.","PeriodicalId":36885,"journal":{"name":"Energy Harvesting and Systems","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78645913","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}
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.
{"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}
M. Al-Omary, R. Aljarrah, Aiman Albatayneh, Dua'a Alshabi, Khaled Alzaareer
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.
{"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}
Murad Al-Omary, Rafat Aljarrah, Aiman Albatayneh, Dua’a Alshabi, Khaled Alzaareer
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.
{"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}
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