Pub Date : 2024-06-10DOI: 10.1177/0309524x241256957
N. Elsonbaty, Mohamed A. Enany, Mahmoud Elymany
This paper investigates a novel control strategy that enables hybrid excitation permanent magnet synchronous generator (HPMSG) to track the optimal extracted power of the modern wind turbine type (NASA-NSF). The proposed control mathematical model is based on two cases of variable speed—Maximum Power Point Tracking (MPPT) and variable speed—Constant Power Point Tracking (CPPT). The later one is specified for wind gust and higher than rated wind speed withstanding operation. The HPMSG generator quantitative performance characteristics are presented and validated through simulation for both steady and dynamics states. Simulation results prove the capability of the generator to operate correctly under load and speed variation over both MPPT and CPPT. The output voltage stays, in both cases, within the much lower limits that imposed by maximum values.
{"title":"Hybrid permanent magnet synchronous generator as an efficient wind energy transducer for modern wind turbines","authors":"N. Elsonbaty, Mohamed A. Enany, Mahmoud Elymany","doi":"10.1177/0309524x241256957","DOIUrl":"https://doi.org/10.1177/0309524x241256957","url":null,"abstract":"This paper investigates a novel control strategy that enables hybrid excitation permanent magnet synchronous generator (HPMSG) to track the optimal extracted power of the modern wind turbine type (NASA-NSF). The proposed control mathematical model is based on two cases of variable speed—Maximum Power Point Tracking (MPPT) and variable speed—Constant Power Point Tracking (CPPT). The later one is specified for wind gust and higher than rated wind speed withstanding operation. The HPMSG generator quantitative performance characteristics are presented and validated through simulation for both steady and dynamics states. Simulation results prove the capability of the generator to operate correctly under load and speed variation over both MPPT and CPPT. The output voltage stays, in both cases, within the much lower limits that imposed by maximum values.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141366293","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 : 2024-06-01DOI: 10.1177/0309524X231212639
El ayache Belagra, Souhil Mouassa, S. Chettih, F. Jurado
One of the most complex and motivating issues in power system is optimal power flow (OPF), which is a constrained optimization problem characterized by non-linearity and non-convexity. From these specifications, researchers competed in the past decades to find optimal solutions to OPF problem while keeping system stability. This paper presents an efficient optimization approach to deal with OPF problem in the hybrid renewable energy systems involving wind turbines, solar photovoltaic and small hydropower plant using optimization method depends on weighted mean of vectors INFO. Total generation cost, active power losses, and combined cost and emission are the principle goal, taking into account both reserve and penalty cost appropriate to over and under estimation respectively in the generation cost model. To evaluate the performance of INFO in solving OPF problem, modified IEEE 30-bus and IEEE 57-bus test systems will be utilized. The obtained results are compared with several algorithms such as Gorilla troop optimizer GTO, artificial ecosystem-based optimization AEO, Barnacles Mating Optimizer BMO for the same test systems keeping the same conditions. Simulation results have indicated the superiority of INFO while respecting all constraints. INFO can minimize total generation cost to 788.9417 $/h for IEEE 30-bus and 5259.2040 $/h for IEEE 57-bus. The results demonstrate clearly that the INFO is a highly efficient algorithm that is an encouraging tool for solving OPF problem. The promising findings highlight the potential of the INFO algorithm to smoothest the integration of RES, and its role in promoting sustainable energy solutions. Furthermore, the one-way analysis of variance (ANOVA) test, a statistical approach, was employed to evaluate the superiority of the proposed algorithm and to highlight a certain level of confidence to our study.
{"title":"Optimal power flow calculation in hybrid power system involving solar, wind, and hydropower plant using weighted mean of vectors algorithm","authors":"El ayache Belagra, Souhil Mouassa, S. Chettih, F. Jurado","doi":"10.1177/0309524X231212639","DOIUrl":"https://doi.org/10.1177/0309524X231212639","url":null,"abstract":"One of the most complex and motivating issues in power system is optimal power flow (OPF), which is a constrained optimization problem characterized by non-linearity and non-convexity. From these specifications, researchers competed in the past decades to find optimal solutions to OPF problem while keeping system stability. This paper presents an efficient optimization approach to deal with OPF problem in the hybrid renewable energy systems involving wind turbines, solar photovoltaic and small hydropower plant using optimization method depends on weighted mean of vectors INFO. Total generation cost, active power losses, and combined cost and emission are the principle goal, taking into account both reserve and penalty cost appropriate to over and under estimation respectively in the generation cost model. To evaluate the performance of INFO in solving OPF problem, modified IEEE 30-bus and IEEE 57-bus test systems will be utilized. The obtained results are compared with several algorithms such as Gorilla troop optimizer GTO, artificial ecosystem-based optimization AEO, Barnacles Mating Optimizer BMO for the same test systems keeping the same conditions. Simulation results have indicated the superiority of INFO while respecting all constraints. INFO can minimize total generation cost to 788.9417 $/h for IEEE 30-bus and 5259.2040 $/h for IEEE 57-bus. The results demonstrate clearly that the INFO is a highly efficient algorithm that is an encouraging tool for solving OPF problem. The promising findings highlight the potential of the INFO algorithm to smoothest the integration of RES, and its role in promoting sustainable energy solutions. Furthermore, the one-way analysis of variance (ANOVA) test, a statistical approach, was employed to evaluate the superiority of the proposed algorithm and to highlight a certain level of confidence to our study.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141409129","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 : 2024-05-13DOI: 10.1177/0309524x241240496
Xin Guan, Mingyang Li, Wei Wu, Yuqi Xie, Yongpeng Sun
Considering the physical characteristics of wind turbine wing icing, icing synthesis rate, and icing type, we selected the icing type and surface roughness of ice-coated blades as sensitive parameters. The focus of our research was on the equivalent particle roughness height correction model, and we numerically analyzed the two icing processes (frost ice and clear ice) on wind turbine blade surfaces by combining FENSAP-ICE and FLUENT analysis tools. We predicted the ice type on blade surfaces using a multi-time step method and analyzed how variations in icing shape and ice surface roughness affect the aerodynamic performance of blades during frost ice formation or clear ice formation. Our results indicate that differences in blade surface roughness and heat flux lead to disparities in both ice formation rate and shape between frost ice and clear ice. Clear ice has a greater impact on aerodynamics compared to frost ice, while frost ice is significantly influenced by the roughness of its icy surface. These findings can serve as valuable references for wind power operators and manufacturers seeking solutions to issues related to blade surface icing under extremely cold conditions.
{"title":"Research on formation mechanism and output effect of wind turbine ice-covered blades","authors":"Xin Guan, Mingyang Li, Wei Wu, Yuqi Xie, Yongpeng Sun","doi":"10.1177/0309524x241240496","DOIUrl":"https://doi.org/10.1177/0309524x241240496","url":null,"abstract":"Considering the physical characteristics of wind turbine wing icing, icing synthesis rate, and icing type, we selected the icing type and surface roughness of ice-coated blades as sensitive parameters. The focus of our research was on the equivalent particle roughness height correction model, and we numerically analyzed the two icing processes (frost ice and clear ice) on wind turbine blade surfaces by combining FENSAP-ICE and FLUENT analysis tools. We predicted the ice type on blade surfaces using a multi-time step method and analyzed how variations in icing shape and ice surface roughness affect the aerodynamic performance of blades during frost ice formation or clear ice formation. Our results indicate that differences in blade surface roughness and heat flux lead to disparities in both ice formation rate and shape between frost ice and clear ice. Clear ice has a greater impact on aerodynamics compared to frost ice, while frost ice is significantly influenced by the roughness of its icy surface. These findings can serve as valuable references for wind power operators and manufacturers seeking solutions to issues related to blade surface icing under extremely cold conditions.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140984835","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 : 2024-05-13DOI: 10.1177/0309524x241247231
Mahdi Hermassi, Saber Krim, Youssef Kraiem, Mohamed Ali Hajjaji, Mohamed Faouzi Mimouni, A. Mtibaa
This paper proposes a novel method using a machine learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) to optimize Maximum Power Point Tracking (MPPT) in variable-speed Wind Turbines (WT). The ANFIS algorithm, blending artificial neural networks and fuzzy logic, addresses issues with traditional wind speed sensors, such as cost, imprecision, and susceptibility to adverse weather conditions. An initial offline-trained ANFIS is suggested to understand turbine power characteristics, and subsequently estimate varying wind speed, addressing strong nonlinearity due to WT aerodynamics and wind speed fluctuations. A second ANFIS efficiently tracks the maximum power point, overcoming limitations of linear controllers. Implemented in Matlab/Simulink for a 3.5 kW WT, the approach demonstrates effectiveness, precision, and faster response time in wind speed estimation and accurate MPPT compared to alternatives. A notable advantage is its independence from instantaneous wind speed measurement, providing a cost-effective solution for wind energy systems.
{"title":"Wind speed estimation and maximum power point tracking using neuro-fuzzy systems for variable-speed wind generator","authors":"Mahdi Hermassi, Saber Krim, Youssef Kraiem, Mohamed Ali Hajjaji, Mohamed Faouzi Mimouni, A. Mtibaa","doi":"10.1177/0309524x241247231","DOIUrl":"https://doi.org/10.1177/0309524x241247231","url":null,"abstract":"This paper proposes a novel method using a machine learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) to optimize Maximum Power Point Tracking (MPPT) in variable-speed Wind Turbines (WT). The ANFIS algorithm, blending artificial neural networks and fuzzy logic, addresses issues with traditional wind speed sensors, such as cost, imprecision, and susceptibility to adverse weather conditions. An initial offline-trained ANFIS is suggested to understand turbine power characteristics, and subsequently estimate varying wind speed, addressing strong nonlinearity due to WT aerodynamics and wind speed fluctuations. A second ANFIS efficiently tracks the maximum power point, overcoming limitations of linear controllers. Implemented in Matlab/Simulink for a 3.5 kW WT, the approach demonstrates effectiveness, precision, and faster response time in wind speed estimation and accurate MPPT compared to alternatives. A notable advantage is its independence from instantaneous wind speed measurement, providing a cost-effective solution for wind energy systems.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140985275","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 : 2024-04-24DOI: 10.1177/0309524x241241795
B. Mahdad
In this study, a new variant, the fast pelican optimizer (FPO), is proposed to improve the performance of the radial distribution network (RDEN). The proposed variant is characterized by creating a dynamic interaction between two phases, exploration and exploitation, during the search process. The modifications introduced within the standard algorithm allow the proposed new variant, namely FPO, to be fast and adaptive to efficiently solve various complex optimization problems. In the first stage, the proposed FPO is newly adapted and applied to solve the optimal locations of various types of distributed generation based renewable sources and multi shunt compensators, namely the DSTATCOM devices based FACTS technology, and in the second stage, the proposed FPO is applied to optimize the active power of DG units in coordination with the reactive power of multi DSTATCOM. Three objective functions, such as the total power losses, the total voltage deviation, and the margin stability, are optimized individually and in coordination to enhance the performances of the practical radial distribution network (RDEN) 33-bus. A deep comparative study in terms of solution quality and convergence accuracy based statistical analysis is elaborated to demonstrate the competitive aspect of the proposed FPO.
{"title":"A fast multi-objective pelican optimizer for optimal coordination of DGs and DSTATCOM considering risk penetration level of wind","authors":"B. Mahdad","doi":"10.1177/0309524x241241795","DOIUrl":"https://doi.org/10.1177/0309524x241241795","url":null,"abstract":"In this study, a new variant, the fast pelican optimizer (FPO), is proposed to improve the performance of the radial distribution network (RDEN). The proposed variant is characterized by creating a dynamic interaction between two phases, exploration and exploitation, during the search process. The modifications introduced within the standard algorithm allow the proposed new variant, namely FPO, to be fast and adaptive to efficiently solve various complex optimization problems. In the first stage, the proposed FPO is newly adapted and applied to solve the optimal locations of various types of distributed generation based renewable sources and multi shunt compensators, namely the DSTATCOM devices based FACTS technology, and in the second stage, the proposed FPO is applied to optimize the active power of DG units in coordination with the reactive power of multi DSTATCOM. Three objective functions, such as the total power losses, the total voltage deviation, and the margin stability, are optimized individually and in coordination to enhance the performances of the practical radial distribution network (RDEN) 33-bus. A deep comparative study in terms of solution quality and convergence accuracy based statistical analysis is elaborated to demonstrate the competitive aspect of the proposed FPO.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140664490","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 : 2024-04-22DOI: 10.1177/0309524x241247230
Mohammad Kazeminejad, Mozhdeh Karamifard, Ali Sheibani
This study proposed a method for optimizing a radial distribution network by integrating wind turbine allocation, considering fluctuating load demands, through the use of a hybrid Grey Wolf Optimizer-Genetic Algorithm (HGWOGA). This approach aims to decrease the network’s energy loss costs. By incorporating genetic algorithm techniques, the method enhances the Grey Wolf Optimizer’s efficiency, speeding up convergence and avoiding local optima. The strategy determines the network’s open lines and the placement and capacity of wind turbines, adhering to radiality and operational constraints. It categorizes load levels into residential, commercial, and industrial, providing a comprehensive analysis of energy losses and their cost implications under various scenarios, including constant and dynamic loads. The study suggests that managing time-varying demand offers a more accurate depiction of network challenges, enabling effective reconfiguration throughout different demand phases. Moreover, HGWOGA demonstrates its ability to find the global optimum efficiently, even with reduced population sizes—a feat not achievable with the Grey Wolf Optimizer alone. Comparative analyses reveal HGWOGA’s effectiveness in curbing network energy loss costs better than previous methodologies. By simultaneously applying network reconfiguration and wind turbine allocation, as opposed to merely reconfiguring the network, this approach notably reduces power loss, diminishes the cost of losses, and enhances the voltage profile. This synergistic strategy leverages the dynamic allocation of wind turbines within the network, optimizing energy flow and distribution efficiency, thereby offering a substantial improvement over conventional network reconfiguration methods.
{"title":"Reconfiguration of distribution network-based wind energy resource allocation considering time-varying load using hybrid optimization method","authors":"Mohammad Kazeminejad, Mozhdeh Karamifard, Ali Sheibani","doi":"10.1177/0309524x241247230","DOIUrl":"https://doi.org/10.1177/0309524x241247230","url":null,"abstract":"This study proposed a method for optimizing a radial distribution network by integrating wind turbine allocation, considering fluctuating load demands, through the use of a hybrid Grey Wolf Optimizer-Genetic Algorithm (HGWOGA). This approach aims to decrease the network’s energy loss costs. By incorporating genetic algorithm techniques, the method enhances the Grey Wolf Optimizer’s efficiency, speeding up convergence and avoiding local optima. The strategy determines the network’s open lines and the placement and capacity of wind turbines, adhering to radiality and operational constraints. It categorizes load levels into residential, commercial, and industrial, providing a comprehensive analysis of energy losses and their cost implications under various scenarios, including constant and dynamic loads. The study suggests that managing time-varying demand offers a more accurate depiction of network challenges, enabling effective reconfiguration throughout different demand phases. Moreover, HGWOGA demonstrates its ability to find the global optimum efficiently, even with reduced population sizes—a feat not achievable with the Grey Wolf Optimizer alone. Comparative analyses reveal HGWOGA’s effectiveness in curbing network energy loss costs better than previous methodologies. By simultaneously applying network reconfiguration and wind turbine allocation, as opposed to merely reconfiguring the network, this approach notably reduces power loss, diminishes the cost of losses, and enhances the voltage profile. This synergistic strategy leverages the dynamic allocation of wind turbines within the network, optimizing energy flow and distribution efficiency, thereby offering a substantial improvement over conventional network reconfiguration methods.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140677984","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 : 2024-04-10DOI: 10.1177/0309524x241240982
Bisma Hamid, S. J. Iqbal, Ikhlaq Hussain
This study aims to ameliorate the contribution capability of doubly-fed induction generator (DFIG) to participate in standalone microgrid operation. The islanded microgrid consists of a solar photovoltaic array for solar energy conversion and battery energy storage in addition to DFIG-based wind energy conversion system. Using a simplified control approach, the study describes multi-mode operation of a DFIG-based AC/DC microgrid using a stator-side solid-state transition switch (SSTS). Using SSTS operation, the DFIG stator can be seamlessly disconnected and reconnected from the point of common coupling without interrupting power to the loads in the microgrid. Additionally, non-ideal AC loads can be handled efficiently without the need for computationally exhaustive approaches to enhance stator voltages and currents.
{"title":"Control of DFIG-based microgrid and seamless transition from stator connected and disconnected modes","authors":"Bisma Hamid, S. J. Iqbal, Ikhlaq Hussain","doi":"10.1177/0309524x241240982","DOIUrl":"https://doi.org/10.1177/0309524x241240982","url":null,"abstract":"This study aims to ameliorate the contribution capability of doubly-fed induction generator (DFIG) to participate in standalone microgrid operation. The islanded microgrid consists of a solar photovoltaic array for solar energy conversion and battery energy storage in addition to DFIG-based wind energy conversion system. Using a simplified control approach, the study describes multi-mode operation of a DFIG-based AC/DC microgrid using a stator-side solid-state transition switch (SSTS). Using SSTS operation, the DFIG stator can be seamlessly disconnected and reconnected from the point of common coupling without interrupting power to the loads in the microgrid. Additionally, non-ideal AC loads can be handled efficiently without the need for computationally exhaustive approaches to enhance stator voltages and currents.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140717929","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 : 2024-04-10DOI: 10.1177/0309524x241238244
Aswin Anil Bindu, K. S. Thampatty
Repowering wind farms entails upgrading or replacing old turbines with more efficient, capable, and profitable ones. This technique has the potential to boost energy production, improve grid integration, and lower operational costs. Repowering also makes use of the most recent breakthroughs in wind energy technology, ensuring that wind farms stay economically viable and contribute to the growth of renewable energy. The majority of the wind farms that are located in India were constructed in early 2000, and their capacity ratings range from 200 kW to 800 kW. The lifespans of these wind farms have finally come to an end. Repowering wind farms is a viable alternative in these regions due to the significant wind capacity that exists there. In order to find the wind potential in the site wind resource assessment is needed. This paper proposes a repowering scheme for the existing wind farm located in Kayathar, Tamil Nadu. The reduction in power loss due to the wake effect in the existing wind farm is analyzed using WAsP software and repowering scheme is proposed to increase the Annual Energy Production (AEP), Capacity Utilisation Factor (CUF). The Wind Atlas Analysis and Application Program (WAsP) is utilized in order to carry out the site’s wind resource evaluation. After the wind resource assessment, individual turbine wake loss is identified, and the viability of repowering the wind farm by raising the hub height of high wake-affected turbines was investigated. Another repowering study is also carried out by installing high-capacity turbines in place of the existing turbines.
{"title":"Repowering feasibility of Indian wind energy sector: A case study","authors":"Aswin Anil Bindu, K. S. Thampatty","doi":"10.1177/0309524x241238244","DOIUrl":"https://doi.org/10.1177/0309524x241238244","url":null,"abstract":"Repowering wind farms entails upgrading or replacing old turbines with more efficient, capable, and profitable ones. This technique has the potential to boost energy production, improve grid integration, and lower operational costs. Repowering also makes use of the most recent breakthroughs in wind energy technology, ensuring that wind farms stay economically viable and contribute to the growth of renewable energy. The majority of the wind farms that are located in India were constructed in early 2000, and their capacity ratings range from 200 kW to 800 kW. The lifespans of these wind farms have finally come to an end. Repowering wind farms is a viable alternative in these regions due to the significant wind capacity that exists there. In order to find the wind potential in the site wind resource assessment is needed. This paper proposes a repowering scheme for the existing wind farm located in Kayathar, Tamil Nadu. The reduction in power loss due to the wake effect in the existing wind farm is analyzed using WAsP software and repowering scheme is proposed to increase the Annual Energy Production (AEP), Capacity Utilisation Factor (CUF). The Wind Atlas Analysis and Application Program (WAsP) is utilized in order to carry out the site’s wind resource evaluation. After the wind resource assessment, individual turbine wake loss is identified, and the viability of repowering the wind farm by raising the hub height of high wake-affected turbines was investigated. Another repowering study is also carried out by installing high-capacity turbines in place of the existing turbines.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140719900","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 : 2024-04-07DOI: 10.1177/0309524x241237734
S. Tounsi
This work deals with optimal systemic design of wind energy generator based on an Integrated Optimal Design (IOD) methodology of a full passive wind turbine system. The originality of the study resides in the use of DC model of wind turbine the make the integration of this model to Genetics Algorithms method possible. The optimization problem concerns the association of the DC model of the wind turbine with a developed Genetics Algorithms to optimize conjointly the global power system mass and total power system energy losses with several constraints. The generator is designed by analytical method validated by finite element method.
{"title":"Systemic optimal design of wind energy generator based on combined analytical-finite element method using genetic algorithms","authors":"S. Tounsi","doi":"10.1177/0309524x241237734","DOIUrl":"https://doi.org/10.1177/0309524x241237734","url":null,"abstract":"This work deals with optimal systemic design of wind energy generator based on an Integrated Optimal Design (IOD) methodology of a full passive wind turbine system. The originality of the study resides in the use of DC model of wind turbine the make the integration of this model to Genetics Algorithms method possible. The optimization problem concerns the association of the DC model of the wind turbine with a developed Genetics Algorithms to optimize conjointly the global power system mass and total power system energy losses with several constraints. The generator is designed by analytical method validated by finite element method.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140733140","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 : 2024-04-06DOI: 10.1177/0309524x241237964
Ying Han, Chi Zhang, Kun Li
Accurate wind speed prediction is of essential importance for the stability and safe operation of power systems. Given the complexity of wind speed sequence, this paper proposed a new two-stage decomposition and integrated hybrid model to improve the accuracy of wind speed prediction. A two-stage decomposition method combining robust local mean decomposition (RLMD), sample entropy (SE) and variational modal decomposition (VMD) was used to decompose the wind speed signal in the data preprocessing stage. Firstly, the wind speed signal was decomposed into various components by RLMD, and the complexity of each component was calculated using the SE to classify them into random, detail component and trend component. Then, a secondary decomposition of the random component with the highest SE was performed using the VMD. In the prediction stage, two different prediction models were used for prediction depending on the smoothness of each component. Stochastic configuration networks (SCN) was used to predict the detail and trend components with relatively smoothness. Echo state network (ESN) was used to predict the components of the secondary decomposition. Finally, the actual wind speed data were compared by different prediction models, which illustrated that the prediction method proposed in this paper had good prediction accuracy and generalizability.
准确的风速预测对电力系统的稳定和安全运行至关重要。鉴于风速序列的复杂性，本文提出了一种新的两阶段分解和集成混合模型，以提高风速预测的准确性。在数据预处理阶段，采用鲁棒局部均值分解（RLMD）、样本熵（SE）和变模分解（VMD）相结合的两阶段分解方法对风速信号进行分解。首先，用 RLMD 将风速信号分解为各种分量，然后用 SE 计算各分量的复杂度，将其分为随机分量、细节分量和趋势分量。然后，利用 VMD 对 SE 值最高的随机分量进行二次分解。在预测阶段，根据每个分量的平滑度，使用了两种不同的预测模型进行预测。随机配置网络（SCN）用于预测相对平滑的细节和趋势成分。回声状态网络（ESN）用于预测二次分解的分量。最后，通过不同预测模型对实际风速数据进行比较，说明本文提出的预测方法具有良好的预测精度和普适性。
{"title":"A new two-stage decomposition and integrated hybrid model for short-term wind speed prediction","authors":"Ying Han, Chi Zhang, Kun Li","doi":"10.1177/0309524x241237964","DOIUrl":"https://doi.org/10.1177/0309524x241237964","url":null,"abstract":"Accurate wind speed prediction is of essential importance for the stability and safe operation of power systems. Given the complexity of wind speed sequence, this paper proposed a new two-stage decomposition and integrated hybrid model to improve the accuracy of wind speed prediction. A two-stage decomposition method combining robust local mean decomposition (RLMD), sample entropy (SE) and variational modal decomposition (VMD) was used to decompose the wind speed signal in the data preprocessing stage. Firstly, the wind speed signal was decomposed into various components by RLMD, and the complexity of each component was calculated using the SE to classify them into random, detail component and trend component. Then, a secondary decomposition of the random component with the highest SE was performed using the VMD. In the prediction stage, two different prediction models were used for prediction depending on the smoothness of each component. Stochastic configuration networks (SCN) was used to predict the detail and trend components with relatively smoothness. Echo state network (ESN) was used to predict the components of the secondary decomposition. Finally, the actual wind speed data were compared by different prediction models, which illustrated that the prediction method proposed in this paper had good prediction accuracy and generalizability.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140734649","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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