Pub Date : 2023-10-30DOI: 10.1177/0309524x231201524
Zahira Seddiki, Tayeb Allaoui, Atallah Smaili
The Hybrid Power Flow Controller (HPFC) has a simple design configuration, where the upgrading of the line functionality and controller can be performed in stages. This paper applies two HPFC configurations to a multi-machine power network. The first HPFC is a combination of two static synchronous series compensators (SSSC) connected in series, and a Static Var compensator (SVC). The second one consists of two shunt Static synchronous compensators (STATCOM) connected through a Thyristor controlled series compensator (TCSC), across a coupling transformer in a common DC link. The HPFC topologies are tested with a multi-machine power network with faults, in the presence of solar and wind energy sources. The overall model is simulated using SimPowerSystems toolbox and the performance of the two HPFC topologies is compared under various operating conditions. The comparison of simulation results shows that the second HPFC gives a better view than the first in analyzing the power system transient stability.
{"title":"Comparative evaluation by two different hybrid power flow controller topologies of transient stability of machine system connected to wind-PV sources","authors":"Zahira Seddiki, Tayeb Allaoui, Atallah Smaili","doi":"10.1177/0309524x231201524","DOIUrl":"https://doi.org/10.1177/0309524x231201524","url":null,"abstract":"The Hybrid Power Flow Controller (HPFC) has a simple design configuration, where the upgrading of the line functionality and controller can be performed in stages. This paper applies two HPFC configurations to a multi-machine power network. The first HPFC is a combination of two static synchronous series compensators (SSSC) connected in series, and a Static Var compensator (SVC). The second one consists of two shunt Static synchronous compensators (STATCOM) connected through a Thyristor controlled series compensator (TCSC), across a coupling transformer in a common DC link. The HPFC topologies are tested with a multi-machine power network with faults, in the presence of solar and wind energy sources. The overall model is simulated using SimPowerSystems toolbox and the performance of the two HPFC topologies is compared under various operating conditions. The comparison of simulation results shows that the second HPFC gives a better view than the first in analyzing the power system transient stability.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136067541","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}
Pub Date : 2023-10-30DOI: 10.1177/0309524x231203686
Shreya Shree Das, Jayendra Kumar
The maintenance of power balance poses significant challenges in renewable combined deregulated power systems due to the unpredictable nature of renewable energy sources. This situation leads to economic instability within the system. However, an energy storage system can help maintain energy supply and control system stability for renewable incorporated thermal power plants. Unlike in regulated markets, energy prices in deregulated markets are not fixed by any government body or particular company. Instead, the Independent System Operator (ISO) serves as the main entity in the electrical market, gathering tenders from Generation Companies (GENCOs), Distribution Companies (DISCOs), and Transmission Companies (TRANSCOs). The market controller regulates energy prices using Nodal Pricing (NP), which provides economic benefits to both GENCOs and DISCOs. However, the unpredictability of renewable sources often results in a decline in system profit due to the production of an imbalance price (CostIMC) caused by a mismatch in contracted power generation from the renewable power plant. To address these issues, this study proposes a novel combined system that utilizes a suitable scheduling technique for the optimum operation of a wind farm-compressed air energy storage (CAES) system to maximize profit and revenue while maintaining grid frequency. The CAES system’s energy level is divided into four different levels, and an optimal strategy has been developed to efficiently utilize the CAES system to maintain grid frequency. This work has been conducted in both regulated and deregulated environments using a modified IEEE 30-bus system. The proposed method has been compared with an existing approach and has yielded better results in all aspects.
{"title":"Profit enhancement and grid frequency control by energy level scheduling of CAES system in wind-connected electrical system","authors":"Shreya Shree Das, Jayendra Kumar","doi":"10.1177/0309524x231203686","DOIUrl":"https://doi.org/10.1177/0309524x231203686","url":null,"abstract":"The maintenance of power balance poses significant challenges in renewable combined deregulated power systems due to the unpredictable nature of renewable energy sources. This situation leads to economic instability within the system. However, an energy storage system can help maintain energy supply and control system stability for renewable incorporated thermal power plants. Unlike in regulated markets, energy prices in deregulated markets are not fixed by any government body or particular company. Instead, the Independent System Operator (ISO) serves as the main entity in the electrical market, gathering tenders from Generation Companies (GENCOs), Distribution Companies (DISCOs), and Transmission Companies (TRANSCOs). The market controller regulates energy prices using Nodal Pricing (NP), which provides economic benefits to both GENCOs and DISCOs. However, the unpredictability of renewable sources often results in a decline in system profit due to the production of an imbalance price (CostIMC) caused by a mismatch in contracted power generation from the renewable power plant. To address these issues, this study proposes a novel combined system that utilizes a suitable scheduling technique for the optimum operation of a wind farm-compressed air energy storage (CAES) system to maximize profit and revenue while maintaining grid frequency. The CAES system’s energy level is divided into four different levels, and an optimal strategy has been developed to efficiently utilize the CAES system to maintain grid frequency. This work has been conducted in both regulated and deregulated environments using a modified IEEE 30-bus system. The proposed method has been compared with an existing approach and has yielded better results in all aspects.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"36 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136104246","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}
Pub Date : 2023-10-30DOI: 10.1177/0309524x231203377
Khaled Sahraoui, Rachid Lalalou, Nadir Boutasseta, Issam Attoui, Nadir Fergani, Mohammed Lamine Frikh
In this paper, a novel multi-objective optimization strategy is proposed for the parallel tuning of six fractional order controllers used in regulation loops of a PMSG based wind energy conversion system connected to the electric grid. The nonlinear nature of the WECS components has made controllers design challenging. To enhance the transient response of system variables, Fractional Order Proportional Integral (FOPI) controllers are considered as they are more suitable for such nonlinear physical systems. The additional parameters introduced by FOPI are normally tuned using a single objective, multi-dimensional particle swarm optimization algorithm. However, the inter-dependence of regulation loops introduces additional complexity that is solved in this work using a multi-objective optimization strategy based on a succession of Single-Objective PSO and Multi-Objective PSO. A simulation study has been conducted in order to demonstrate the higher performance and superior tracking accuracy of the proposed multi-objective optimization strategy in variable wind speed operating conditions.
{"title":"Multi-objective optimization strategy for parallel tuning of multiple fractional order controllers in a PMSG based WECS","authors":"Khaled Sahraoui, Rachid Lalalou, Nadir Boutasseta, Issam Attoui, Nadir Fergani, Mohammed Lamine Frikh","doi":"10.1177/0309524x231203377","DOIUrl":"https://doi.org/10.1177/0309524x231203377","url":null,"abstract":"In this paper, a novel multi-objective optimization strategy is proposed for the parallel tuning of six fractional order controllers used in regulation loops of a PMSG based wind energy conversion system connected to the electric grid. The nonlinear nature of the WECS components has made controllers design challenging. To enhance the transient response of system variables, Fractional Order Proportional Integral (FOPI) controllers are considered as they are more suitable for such nonlinear physical systems. The additional parameters introduced by FOPI are normally tuned using a single objective, multi-dimensional particle swarm optimization algorithm. However, the inter-dependence of regulation loops introduces additional complexity that is solved in this work using a multi-objective optimization strategy based on a succession of Single-Objective PSO and Multi-Objective PSO. A simulation study has been conducted in order to demonstrate the higher performance and superior tracking accuracy of the proposed multi-objective optimization strategy in variable wind speed operating conditions.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136067356","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}
Pub Date : 2023-10-30DOI: 10.1177/0309524x231200582
Mohammed Bakkari, Badre Bossoufi, Ismail El Kafazi, Manale Bouderbala, Mohammed Karim
Morocco has a significant wind energy potential due to its favorable climate proximity to the Atlantic Ocean, and temperature conditions. The governments recognize the importance of transitioning to sustainable energy sources and have taken strategic steps to promote the renewable energy sector, particularly wind energy, to reduce dependence on finite fossil fuels and promote eco-friendly alternatives. Local and international enterprises, along with private investors, have undertaken various wind energy projects in the country. Despite overreliance on conventional resources like coal and gasoline leading to an energy crisis, Morocco sees wind energy as a viable solution due to its increasing accessibility and cost effectiveness. This study comprehensively explores Morocco’s wind energy landscape, defining wind energy and its global and local potential. It highlights challenges and opportunities in wind energy development and outlines strategies to enhance wind resource utilization. By 2021, Morocco achieved a significant milestone by raising the proportion of clean energy in its mix to 37, 6% with wind energy contributing 45% of this. Building on this success, Morocco aims to further increase its renewable energy capacity, targeting 52% of total capacity from renewable source by 2030 according to (IRENA). This showcases Morocco’s commitment to sustainable energy and its progressive approach to creating a greener and more resilient energy future.
{"title":"A review of wind energy potential in Morocco: New challenges and perspectives","authors":"Mohammed Bakkari, Badre Bossoufi, Ismail El Kafazi, Manale Bouderbala, Mohammed Karim","doi":"10.1177/0309524x231200582","DOIUrl":"https://doi.org/10.1177/0309524x231200582","url":null,"abstract":"Morocco has a significant wind energy potential due to its favorable climate proximity to the Atlantic Ocean, and temperature conditions. The governments recognize the importance of transitioning to sustainable energy sources and have taken strategic steps to promote the renewable energy sector, particularly wind energy, to reduce dependence on finite fossil fuels and promote eco-friendly alternatives. Local and international enterprises, along with private investors, have undertaken various wind energy projects in the country. Despite overreliance on conventional resources like coal and gasoline leading to an energy crisis, Morocco sees wind energy as a viable solution due to its increasing accessibility and cost effectiveness. This study comprehensively explores Morocco’s wind energy landscape, defining wind energy and its global and local potential. It highlights challenges and opportunities in wind energy development and outlines strategies to enhance wind resource utilization. By 2021, Morocco achieved a significant milestone by raising the proportion of clean energy in its mix to 37, 6% with wind energy contributing 45% of this. Building on this success, Morocco aims to further increase its renewable energy capacity, targeting 52% of total capacity from renewable source by 2030 according to (IRENA). This showcases Morocco’s commitment to sustainable energy and its progressive approach to creating a greener and more resilient energy future.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"115 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136067461","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}
Pub Date : 2023-10-03DOI: 10.1177/0309524x231200237
Lahcène Noureddine, Marouane Hadjadj, Habib Chaouki Ben Djoudi, Ahmed Hafaifa
This work investigates the prospect of rotor broken bar defect diagnosis in squirrel cage induction generator-based wind turbine using fuzzy logic system (FLS) of the stator currents. The generator current signal is analyzed through the power spectral density (PSD) to diagnose the magnitudes and frequencies associated with various defects. These magnitudes and frequency components are used to apply the system of Fuzzy logic by simulation software.
{"title":"Condition monitoring through DWT-PSD-FLS approach of faulty wind generators","authors":"Lahcène Noureddine, Marouane Hadjadj, Habib Chaouki Ben Djoudi, Ahmed Hafaifa","doi":"10.1177/0309524x231200237","DOIUrl":"https://doi.org/10.1177/0309524x231200237","url":null,"abstract":"This work investigates the prospect of rotor broken bar defect diagnosis in squirrel cage induction generator-based wind turbine using fuzzy logic system (FLS) of the stator currents. The generator current signal is analyzed through the power spectral density (PSD) to diagnose the magnitudes and frequencies associated with various defects. These magnitudes and frequency components are used to apply the system of Fuzzy logic by simulation software.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135740200","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}
Pub Date : 2023-09-07DOI: 10.1177/0309524x231194639
Mariuxi Segarra-Fernández, Johnny Fabian Loor, Sourojeet Chakraborty, Dany De Cecchis, Alexander Espinoza, D. Galatro
Wind energy systems show tremendous potential toward the reduction of greenhouse gas (GHG) emissions; however, the rate of generation of this mode of clean energy remains predominantly intermittent, since it is produced by constantly changing natural drivers, such as wind availability and wind velocity. In this work, a novel framework is proposed which combines a modular process simulator, and a Python environment, to calibrate the operation, and perform a sensitivity analysis of a compressed air energy storage system (CAES) system. Six operational variables are identified via various Monte-Carlo simulations, and a SOBOL analysis of the results highlight three key variables that significantly influence the two primary outputs of a CAES system: the LCOE and the exergy destroyed. Our results successfully identify two novel design metrics that can inform D-CAES design and optimization, for future simulation and experimental works targeted toward wind energy capture and storage.
{"title":"Integrated simulation-based calibration and sensitivity analysis of a compressed air energy storage system","authors":"Mariuxi Segarra-Fernández, Johnny Fabian Loor, Sourojeet Chakraborty, Dany De Cecchis, Alexander Espinoza, D. Galatro","doi":"10.1177/0309524x231194639","DOIUrl":"https://doi.org/10.1177/0309524x231194639","url":null,"abstract":"Wind energy systems show tremendous potential toward the reduction of greenhouse gas (GHG) emissions; however, the rate of generation of this mode of clean energy remains predominantly intermittent, since it is produced by constantly changing natural drivers, such as wind availability and wind velocity. In this work, a novel framework is proposed which combines a modular process simulator, and a Python environment, to calibrate the operation, and perform a sensitivity analysis of a compressed air energy storage system (CAES) system. Six operational variables are identified via various Monte-Carlo simulations, and a SOBOL analysis of the results highlight three key variables that significantly influence the two primary outputs of a CAES system: the LCOE and the exergy destroyed. Our results successfully identify two novel design metrics that can inform D-CAES design and optimization, for future simulation and experimental works targeted toward wind energy capture and storage.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"49 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86576160","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}
Pub Date : 2023-09-07DOI: 10.1177/0309524x231191056
Imen Chikha, Y. Bouzidi, N. Tazi, Samir Baklouti, R. Idir
Over the past two decades, the wind turbine industry has grown rapidly. As a result, thousands of tons of composite materials from these end-of-life (EoL) wind turbine blades (WTBs) are discarded every year. Due to their complex structure, which consists of a thermoset matrix with glass (GF) and/or carbon (CF) fibers, their recovery is a challenge and remains limited. The objective of this study is to compare several recycling techniques for composite materials using landfill as a baseline scenario. Several aspects can influence the performance of GF and CF recovery, but one of the most important is the efficiency of recycling technologies in terms of the recovered GF/CF fiber rate. To evaluate this amount of fiber annually, a material flow analysis (MFA) was performed based on the punctual years of 2030, 2040, and 2050. A correlation with other aspects was established and based on maturity level, technical, economic, and environmental aspects. Afterward, recommendations on short and medium/long term circularity objectives were drafted on the most suitable technologies for WTBs circularity.
{"title":"Potential recovery of glass and carbon fibers from wind turbine blades through different valorization techniques","authors":"Imen Chikha, Y. Bouzidi, N. Tazi, Samir Baklouti, R. Idir","doi":"10.1177/0309524x231191056","DOIUrl":"https://doi.org/10.1177/0309524x231191056","url":null,"abstract":"Over the past two decades, the wind turbine industry has grown rapidly. As a result, thousands of tons of composite materials from these end-of-life (EoL) wind turbine blades (WTBs) are discarded every year. Due to their complex structure, which consists of a thermoset matrix with glass (GF) and/or carbon (CF) fibers, their recovery is a challenge and remains limited. The objective of this study is to compare several recycling techniques for composite materials using landfill as a baseline scenario. Several aspects can influence the performance of GF and CF recovery, but one of the most important is the efficiency of recycling technologies in terms of the recovered GF/CF fiber rate. To evaluate this amount of fiber annually, a material flow analysis (MFA) was performed based on the punctual years of 2030, 2040, and 2050. A correlation with other aspects was established and based on maturity level, technical, economic, and environmental aspects. Afterward, recommendations on short and medium/long term circularity objectives were drafted on the most suitable technologies for WTBs circularity.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"2 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87120890","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}
Pub Date : 2023-08-21DOI: 10.1177/0309524x231191163
G. Marulanda, J. Cifuentes, Antonio Bello, J. Reneses
Wind power plants have gained prominence in recent decades owing to their positive environmental and economic impact. However, the unpredictability of wind resources poses significant challenges to the secure and stable operation of the power grid. To address this challenge, numerous computational and statistical methods have been proposed in the literature to forecast short-term wind power generation. However, the demand for more accurate and reliable methodologies to tackle this problem remains. In this context, this paper proposes a new hybrid framework that combines a statistical pre-processing stage with an attention-based deep learning approach to overcome the shortcomings of existing forecasting strategies in accurately predicting multi-seasonal wind power time series. The proposed ensemble model involves a data transformation stage that normalizes the data distribution, along with modeling and removing multiple seasonal patterns from the historical time-series. Considering these results, the proposed model further incorporates an LSTM Recurrent Neural Network (RNN) model with an attention mechanism, for each month of the year, to better capture the relevant temporal dependencies in the input residuals sequence. The model was trained and evaluated on hourly wind power data obtained from the Spanish electricity market, spanning the period from 2008 to 2019. Experimental results show that the proposed model outperforms well-established DL-based models, achieving lower error metrics. These findings have potential applications in energy trading, grid planning, and renewable energy management.
{"title":"A hybrid model based on LSTM neural networks with attention mechanism for short-term wind power forecasting","authors":"G. Marulanda, J. Cifuentes, Antonio Bello, J. Reneses","doi":"10.1177/0309524x231191163","DOIUrl":"https://doi.org/10.1177/0309524x231191163","url":null,"abstract":"Wind power plants have gained prominence in recent decades owing to their positive environmental and economic impact. However, the unpredictability of wind resources poses significant challenges to the secure and stable operation of the power grid. To address this challenge, numerous computational and statistical methods have been proposed in the literature to forecast short-term wind power generation. However, the demand for more accurate and reliable methodologies to tackle this problem remains. In this context, this paper proposes a new hybrid framework that combines a statistical pre-processing stage with an attention-based deep learning approach to overcome the shortcomings of existing forecasting strategies in accurately predicting multi-seasonal wind power time series. The proposed ensemble model involves a data transformation stage that normalizes the data distribution, along with modeling and removing multiple seasonal patterns from the historical time-series. Considering these results, the proposed model further incorporates an LSTM Recurrent Neural Network (RNN) model with an attention mechanism, for each month of the year, to better capture the relevant temporal dependencies in the input residuals sequence. The model was trained and evaluated on hourly wind power data obtained from the Spanish electricity market, spanning the period from 2008 to 2019. Experimental results show that the proposed model outperforms well-established DL-based models, achieving lower error metrics. These findings have potential applications in energy trading, grid planning, and renewable energy management.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"64 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83827551","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}
Pub Date : 2023-08-21DOI: 10.1177/0309524x231188951
X. Tong
Due to the uncertainty and randomness of large-scale wind and light, the output power of the power grid has great fluctuations. If it is directly connected to the grid, it will affect the main grid. In addition, when the grid switches between on-grid/off-grid operation modes, there will be power shortages, shocks and oscillations. The scientific and reasonable configuration of energy storage system capacity big data can reduce the load power shortage rate, improve the utilization rate of renewable energy, and ensure the reliable operation of the power grid. For this reason, the key technology of large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization is studied. A large-scale wind-solar hybrid grid energy storage structure is proposed, and the working characteristics of photovoltaic power generation and wind power generation are analyzed, and the probability model of photovoltaic power generation, wind power generation and load, as well as the charging and discharging model of battery and super capacitor are established accordingly. On this basis, the optimization objective function is set, the constraints are determined, and the large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization model is constructed. And the PSO algorithm is used to solve the model to realize the big data configuration optimization of large-scale wind-solar hybrid grid energy storage capacity. The research results show that the proposed method of large-scale wind-solar hybrid grid energy storage system has good power supply reliability and economy, and can effectively improve the utilization rate of renewable energy.
{"title":"Research on key technologies of large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization","authors":"X. Tong","doi":"10.1177/0309524x231188951","DOIUrl":"https://doi.org/10.1177/0309524x231188951","url":null,"abstract":"Due to the uncertainty and randomness of large-scale wind and light, the output power of the power grid has great fluctuations. If it is directly connected to the grid, it will affect the main grid. In addition, when the grid switches between on-grid/off-grid operation modes, there will be power shortages, shocks and oscillations. The scientific and reasonable configuration of energy storage system capacity big data can reduce the load power shortage rate, improve the utilization rate of renewable energy, and ensure the reliable operation of the power grid. For this reason, the key technology of large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization is studied. A large-scale wind-solar hybrid grid energy storage structure is proposed, and the working characteristics of photovoltaic power generation and wind power generation are analyzed, and the probability model of photovoltaic power generation, wind power generation and load, as well as the charging and discharging model of battery and super capacitor are established accordingly. On this basis, the optimization objective function is set, the constraints are determined, and the large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization model is constructed. And the PSO algorithm is used to solve the model to realize the big data configuration optimization of large-scale wind-solar hybrid grid energy storage capacity. The research results show that the proposed method of large-scale wind-solar hybrid grid energy storage system has good power supply reliability and economy, and can effectively improve the utilization rate of renewable energy.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"8 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82709377","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}
Pub Date : 2023-08-10DOI: 10.1177/0309524x231188696
Jiten Parmar, Jeff Pieper
Various types of control methods are utilized in wind turbines to obtain the optimal amount of power from wind. The turbine dynamics are required in said methods, and the wind speed is a critical component of the analysis. However, the stochastic nature of wind means that wind speed sensor signals are noisy. This paper proposes the utilization of a radial basis function neural network (RBFNN) based filter to process the signal, by training the network with a simulated wind signal. The network is differentiated from a traditional filter in that the number of neurons and the “learning rate” of the network dictate the properties of the filtered signal. The information flow in the network consists of the signal to be processed as the input, the which is then used as an argument in a radial basis function (which determines the “distance” of each value in the input from a particular preset point), and then it multiplied by a weight. The learning rate is obtained from a novel equation that is proposed in the paper. The results showed that the proposed scheme has versatility in terms of noise removal and signal smoothing, and if required, can viably match performance with a Butterworth filter. Furthermore, live training and adaptability also serve as advantages over a classic filter. Three “modes” of processing the signal are determined based on choosing certain ranges of values for parameters which comprise the RBFNN (number of neurons used and learning rate), and the control designer can choose which one to implement based on performance requirements.
{"title":"A radial basis function neural network approach to filtering stochastic wind speed data","authors":"Jiten Parmar, Jeff Pieper","doi":"10.1177/0309524x231188696","DOIUrl":"https://doi.org/10.1177/0309524x231188696","url":null,"abstract":"Various types of control methods are utilized in wind turbines to obtain the optimal amount of power from wind. The turbine dynamics are required in said methods, and the wind speed is a critical component of the analysis. However, the stochastic nature of wind means that wind speed sensor signals are noisy. This paper proposes the utilization of a radial basis function neural network (RBFNN) based filter to process the signal, by training the network with a simulated wind signal. The network is differentiated from a traditional filter in that the number of neurons and the “learning rate” of the network dictate the properties of the filtered signal. The information flow in the network consists of the signal to be processed as the input, the which is then used as an argument in a radial basis function (which determines the “distance” of each value in the input from a particular preset point), and then it multiplied by a weight. The learning rate is obtained from a novel equation that is proposed in the paper. The results showed that the proposed scheme has versatility in terms of noise removal and signal smoothing, and if required, can viably match performance with a Butterworth filter. Furthermore, live training and adaptability also serve as advantages over a classic filter. Three “modes” of processing the signal are determined based on choosing certain ranges of values for parameters which comprise the RBFNN (number of neurons used and learning rate), and the control designer can choose which one to implement based on performance requirements.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135494290","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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