The paper presents the design and the implementation of a direct predictive control of a variable speed wind energy conversion system. The conversion chain uses a Doubly Fed Induction Generator DFIG whereas the control method is based on a Finite States Model Predictive Control FS-MPC. The proposed control method selects the optimal switching state of the two levels back to back power converter that minimizes the cost function, where this optimal voltage vector is applied on the output of the power converter in next sampling time. The proposed predictive control strategy uses only one sample time prediction and it is intuitive since it is very simple for implementation. In order to adjust the measured rotor currents to track their references, the error between orthogonal rotor current components predictions to their computed values used to select the optimal vector and applied on the power converter in rotor side CSR in next sampling time. On other side, based on the error between the active and reactive power prediction and their references of the electrical grid, the predictive algorithm control of the gird side converter CSG kept the Dc-link voltage constant and guarantee that the whole system functioning with unity power factor. The experimental results confirm the advantages of using this structure for wind energy conversion system and the effectiveness of the proposed control strategy.
{"title":"Experimental Validation of Direct Predictive Control of Variable Speed Wind Energy Conversion System Based on DFIG","authors":"S. Chikha, K. Barra, A. Reama","doi":"10.3311/ppee.18874","DOIUrl":"https://doi.org/10.3311/ppee.18874","url":null,"abstract":"The paper presents the design and the implementation of a direct predictive control of a variable speed wind energy conversion system. The conversion chain uses a Doubly Fed Induction Generator DFIG whereas the control method is based on a Finite States Model Predictive Control FS-MPC. The proposed control method selects the optimal switching state of the two levels back to back power converter that minimizes the cost function, where this optimal voltage vector is applied on the output of the power converter in next sampling time. The proposed predictive control strategy uses only one sample time prediction and it is intuitive since it is very simple for implementation. In order to adjust the measured rotor currents to track their references, the error between orthogonal rotor current components predictions to their computed values used to select the optimal vector and applied on the power converter in rotor side CSR in next sampling time. On other side, based on the error between the active and reactive power prediction and their references of the electrical grid, the predictive algorithm control of the gird side converter CSG kept the Dc-link voltage constant and guarantee that the whole system functioning with unity power factor. The experimental results confirm the advantages of using this structure for wind energy conversion system and the effectiveness of the proposed control strategy.","PeriodicalId":37664,"journal":{"name":"Periodica polytechnica Electrical engineering and computer science","volume":"6 1","pages":"174-190"},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80465138","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}
Stencil printing is one of the key steps in reflow soldering technology, and by the spread of ultra-fine-pitch components, analysis of this process is essential. The process of stencil printing has been investigated by a machine learning technique utilizing the ensemble method of boosted decision trees. The phenomenon of overfitting, which can alter the prediction error of boosted decision trees has also been analyzed in detail. The training data set was acquired experimentally by performing stencil printing using different printing speeds (from 20 to 120 mm/s) and various types of solder pastes with different particle sizes (particle size range 25–45 µm, 20–38 µm, 15–25 µm) and different stencil aperture sizes, characterized by their area ratio (from 0.35 to 1.7). The overfitting phenomenon was addressed by training by using incomplete data sets, which means that a subset of data corresponding to a particular input parameter value was excluded from the training. Four cases were investigated with incomplete data sets, by excluding the corresponding data subsets for: area ratios of 0.75 and 1.3, and printing speeds of 70 mm/s and 85 mm/s. It was found that the prediction error at input parameter values that have been excluded from the training can be lowered by eliminating the overfitting; though, the decrease in the prediction error depends on the rate of change in the output parameter in the vicinity of the respective input parameter value.
{"title":"Analyzing the Overfitting of Boosted Decision Trees for the Modelling of Stencil Printing","authors":"P. Martinek, O. Krammer","doi":"10.3311/ppee.19274","DOIUrl":"https://doi.org/10.3311/ppee.19274","url":null,"abstract":"Stencil printing is one of the key steps in reflow soldering technology, and by the spread of ultra-fine-pitch components, analysis of this process is essential. The process of stencil printing has been investigated by a machine learning technique utilizing the ensemble method of boosted decision trees. The phenomenon of overfitting, which can alter the prediction error of boosted decision trees has also been analyzed in detail. The training data set was acquired experimentally by performing stencil printing using different printing speeds (from 20 to 120 mm/s) and various types of solder pastes with different particle sizes (particle size range 25–45 µm, 20–38 µm, 15–25 µm) and different stencil aperture sizes, characterized by their area ratio (from 0.35 to 1.7). The overfitting phenomenon was addressed by training by using incomplete data sets, which means that a subset of data corresponding to a particular input parameter value was excluded from the training. Four cases were investigated with incomplete data sets, by excluding the corresponding data subsets for: area ratios of 0.75 and 1.3, and printing speeds of 70 mm/s and 85 mm/s. It was found that the prediction error at input parameter values that have been excluded from the training can be lowered by eliminating the overfitting; though, the decrease in the prediction error depends on the rate of change in the output parameter in the vicinity of the respective input parameter value.","PeriodicalId":37664,"journal":{"name":"Periodica polytechnica Electrical engineering and computer science","volume":"2 1","pages":"132-138"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80241358","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}
A zero-inertia micro-grid is a power system consisting of multiple renewable energy power sources and energy storage systems without the presence of conventional synchronous generators. In such a system, a large variation of the load or source sides during the islanded mode of operation extremely degrades the micro-grid's voltage and frequency stability. This study presents a virtual inertia-based predictive control strategy for a small-scale zero-inertia multiple distributed generators (DGs) micro-grid. In islanded mode, Voltage Model Predictive Control (VMPC) was implemented to control and maintain the voltage and frequency of the micro-grid. However, instabilities in frequency and voltage may rise at the Point of Common Coupling (PCC) due to large variations at both source and load sides. Therefore, the proposed virtual inertia loop calculates the amount of active power to be delivered or absorbed by each DG, and its effect is reflected in the estimated d current component of the VMPC, thus providing better frequency regulation. In grid-connected mode, Direct Power Model Predictive Control (DPMPC) was implemented to manage the power flow between each DG and the utility grid. The control approach also enables the DG plug and play characteristics. The performance of the control strategy was investigated and verified using the PSCAD/EMTDC software platform.
{"title":"A Novel Model Predictive Control for Stability Improvement of Small Scaled Zero-inertia Multiple DGs Micro-grid","authors":"Abdulrahman J. Babqi","doi":"10.3311/ppee.19232","DOIUrl":"https://doi.org/10.3311/ppee.19232","url":null,"abstract":"A zero-inertia micro-grid is a power system consisting of multiple renewable energy power sources and energy storage systems without the presence of conventional synchronous generators. In such a system, a large variation of the load or source sides during the islanded mode of operation extremely degrades the micro-grid's voltage and frequency stability. This study presents a virtual inertia-based predictive control strategy for a small-scale zero-inertia multiple distributed generators (DGs) micro-grid. In islanded mode, Voltage Model Predictive Control (VMPC) was implemented to control and maintain the voltage and frequency of the micro-grid. However, instabilities in frequency and voltage may rise at the Point of Common Coupling (PCC) due to large variations at both source and load sides. Therefore, the proposed virtual inertia loop calculates the amount of active power to be delivered or absorbed by each DG, and its effect is reflected in the estimated d current component of the VMPC, thus providing better frequency regulation. In grid-connected mode, Direct Power Model Predictive Control (DPMPC) was implemented to manage the power flow between each DG and the utility grid. The control approach also enables the DG plug and play characteristics. The performance of the control strategy was investigated and verified using the PSCAD/EMTDC software platform.","PeriodicalId":37664,"journal":{"name":"Periodica polytechnica Electrical engineering and computer science","volume":"84 1","pages":"163-173"},"PeriodicalIF":0.0,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80656852","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}
Sara Al-waisawy, A. K. Abdullah, H. A. Hamed, Ali A. Al-bakri
In this research, the pure titanium foil was treated in glycerol base electrolyte with 0.7 wt.% NH4F and a small amount of H2O at 17 V for 2 hours by electrochemical anodization process in order to prepare Titania nanotube arrays at room temperature (~25 ºC), different water content was added to the electrolyte as a tube enhancing agent. The high density uniform arrays are prepared by using organized and well aligned these tubes. The average size of tube diameter, ranging from 57 to 92 nm which found it increases with increasing water content, and the length of the tube ranging from 2.76 to 4.12 µm, also found to increase with increasing water content and ranging in size of wall thickness from 23 to 35 nm. A possible growth mechanism is presented. The X-ray diffraction (XRD), atomic force microscopy (AFM), and scanning electron microscopy (SEM) were utilized to study the structure and morphology of the Titania films.
{"title":"Study the Effect of Water Content on the Structure of Electrochemically Prepared TiO2 Nanotubes","authors":"Sara Al-waisawy, A. K. Abdullah, H. A. Hamed, Ali A. Al-bakri","doi":"10.3311/ppee.17586","DOIUrl":"https://doi.org/10.3311/ppee.17586","url":null,"abstract":"In this research, the pure titanium foil was treated in glycerol base electrolyte with 0.7 wt.% NH4F and a small amount of H2O at 17 V for 2 hours by electrochemical anodization process in order to prepare Titania nanotube arrays at room temperature (~25 ºC), different water content was added to the electrolyte as a tube enhancing agent. The high density uniform arrays are prepared by using organized and well aligned these tubes. The average size of tube diameter, ranging from 57 to 92 nm which found it increases with increasing water content, and the length of the tube ranging from 2.76 to 4.12 µm, also found to increase with increasing water content and ranging in size of wall thickness from 23 to 35 nm. A possible growth mechanism is presented. The X-ray diffraction (XRD), atomic force microscopy (AFM), and scanning electron microscopy (SEM) were utilized to study the structure and morphology of the Titania films.","PeriodicalId":37664,"journal":{"name":"Periodica polytechnica Electrical engineering and computer science","volume":"14 1","pages":"99-104"},"PeriodicalIF":0.0,"publicationDate":"2022-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84641734","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}
A. Afandi, A. Wibawa, S. Patmanthara, G. Fujita, S. Hani, Yuni Sulistyowati
The electricity system is generally rapidly developing for covering various power demands with requiring a reliable and safe supply where the substructures are expanding further in generation systems, transmission systems, and distribution systems. However, the system must be run economically to access energy at a cost-effective level related to existing energy enterprises and energy consumption in the load which is represented periodically in the total costs of operations for all operating units. As a basis for its determination, the transmission of economic power within the technical limits applicable is taken into consideration. Environmental factors, on the other hand, are also an impediment to technical limitations. As a result, the operation's economic measure is expressed in the process of providing and selling energy to customers. These works use the Artificial Bees Colony algorithm to determine the scheduling of generating units using the basic principle of optimization to describe its relationship as an economic function. The IEEE-30 bus system is used as a basic model for system development. The analysis' findings show that the weighting factor scheme has an impact on the minimum total cost and that the combination of the electricity distribution process and environmental factors has implications for the operational financial condition and electricity production. The power output, in particular, is proportional to the cost of each generating unit.
{"title":"Economic Assessment of Power Grid Development Using Artificial Bee Colony Algorithm","authors":"A. Afandi, A. Wibawa, S. Patmanthara, G. Fujita, S. Hani, Yuni Sulistyowati","doi":"10.3311/ppee.18422","DOIUrl":"https://doi.org/10.3311/ppee.18422","url":null,"abstract":"The electricity system is generally rapidly developing for covering various power demands with requiring a reliable and safe supply where the substructures are expanding further in generation systems, transmission systems, and distribution systems. However, the system must be run economically to access energy at a cost-effective level related to existing energy enterprises and energy consumption in the load which is represented periodically in the total costs of operations for all operating units. As a basis for its determination, the transmission of economic power within the technical limits applicable is taken into consideration. Environmental factors, on the other hand, are also an impediment to technical limitations. As a result, the operation's economic measure is expressed in the process of providing and selling energy to customers. These works use the Artificial Bees Colony algorithm to determine the scheduling of generating units using the basic principle of optimization to describe its relationship as an economic function. The IEEE-30 bus system is used as a basic model for system development. The analysis' findings show that the weighting factor scheme has an impact on the minimum total cost and that the combination of the electricity distribution process and environmental factors has implications for the operational financial condition and electricity production. The power output, in particular, is proportional to the cost of each generating unit.","PeriodicalId":37664,"journal":{"name":"Periodica polytechnica Electrical engineering and computer science","volume":"325 1","pages":"72-79"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78009488","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}
{"title":"Design and Implementation of Multi-Band Fractal Slot Antennas for Energy Harvesting Applications","authors":"A. Sabaawi, Qusai H. Sultan, Tareq A. Najm","doi":"10.3311/ppee.20301","DOIUrl":"https://doi.org/10.3311/ppee.20301","url":null,"abstract":"","PeriodicalId":37664,"journal":{"name":"Periodica polytechnica Electrical engineering and computer science","volume":"36 1","pages":"253-264"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80324776","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}
{"title":"Predictive-DPC Based on Duty Cycle Control of PWM Rectifier under Unbalanced Network","authors":"T. Chikouche, K. Hartani, T. Terras","doi":"10.3311/ppee.20048","DOIUrl":"https://doi.org/10.3311/ppee.20048","url":null,"abstract":"","PeriodicalId":37664,"journal":{"name":"Periodica polytechnica Electrical engineering and computer science","volume":"1 1","pages":"139-147"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79918352","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}
{"title":"Performance Analysis of Universal Power Quality Conditioner Systems Based on Seven-level NPC Inverter Using PD-SPWM with Fuzzy Control Scheme","authors":"Chennai Salim","doi":"10.3311/ppee.20158","DOIUrl":"https://doi.org/10.3311/ppee.20158","url":null,"abstract":"","PeriodicalId":37664,"journal":{"name":"Periodica polytechnica Electrical engineering and computer science","volume":"66 1","pages":"325-335"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69499695","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}
Madhuchandra Popuri, V. K. Bhajana, M. K. Maharana
{"title":"Investigation of a Novel Interleaved Buck Converter for Renewable Energy Applications: Design and Analysis","authors":"Madhuchandra Popuri, V. K. Bhajana, M. K. Maharana","doi":"10.3311/ppee.20064","DOIUrl":"https://doi.org/10.3311/ppee.20064","url":null,"abstract":"","PeriodicalId":37664,"journal":{"name":"Periodica polytechnica Electrical engineering and computer science","volume":"89 1","pages":"270-276"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75897158","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}
In this paper, an improved Backstepping control based on a recent optimization method called Ant Lion Optimizer (ALO) algorithm for a Doubly Fed Induction Generator (DFIG) driven by a wind turbine is designed and presented. ALO algorithm is applied for obtaining optimum Backstepping control (BCS) parameters that are able to make the drive more robust with a faster dynamic response, higher accuracy and steady performance. The fitness function of the ALO algorithm to be minimized is designed using some indexes criterion like Integral Time Absolute Error (ITAE) and Integral Time Square Error (ITSE). Simulation tests are carried out in MATLAB/Simulink environment to validate the effectiveness of the proposed BCS-ALO and compared to the conventional BCS control. The results prove that the objectives of this paper were accomplished in terms of robustness, better dynamic efficiency, reduced harmonic distortion, minimization of stator powers ripples and performing well in solving the problem of uncertainty of the model parameter.
{"title":"Improved Backstepping Control of a DFIG based Wind Energy Conversion System using Ant Lion Optimizer Algorithm","authors":"Z. Zeghdi, L. Barazane, Y. Bekakra, A. Larabi","doi":"10.3311/ppee.18716","DOIUrl":"https://doi.org/10.3311/ppee.18716","url":null,"abstract":"In this paper, an improved Backstepping control based on a recent optimization method called Ant Lion Optimizer (ALO) algorithm for a Doubly Fed Induction Generator (DFIG) driven by a wind turbine is designed and presented. ALO algorithm is applied for obtaining optimum Backstepping control (BCS) parameters that are able to make the drive more robust with a faster dynamic response, higher accuracy and steady performance. The fitness function of the ALO algorithm to be minimized is designed using some indexes criterion like Integral Time Absolute Error (ITAE) and Integral Time Square Error (ITSE). Simulation tests are carried out in MATLAB/Simulink environment to validate the effectiveness of the proposed BCS-ALO and compared to the conventional BCS control. The results prove that the objectives of this paper were accomplished in terms of robustness, better dynamic efficiency, reduced harmonic distortion, minimization of stator powers ripples and performing well in solving the problem of uncertainty of the model parameter.","PeriodicalId":37664,"journal":{"name":"Periodica polytechnica Electrical engineering and computer science","volume":"21 1","pages":"43-59"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75092390","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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