Pub Date : 2021-11-27DOI: 10.1109/i-PACT52855.2021.9696983
C. R, Rahul S G, Amruthavalli Archakam, Sai Pramitha Meesala, Jaswanth Reddy Modium, Jagadish Kumar Pakalapati
The proper functioning of the Cardiovascular system is dependent on several tissues like the heart, blood, arteries, and veins. These tissues are in contact with each other. In this paper, we present a cardiovascular model based on equivalent mathematical relations in Simulink environment for analyzing the changes that occur due to baroreceptors. The system aims to quantify and depicts the relation between various cardiac parameters like blood pressure, heart rate, stroke volume for different delays. The cardiovascular output is determined by stroke volume and heart rate, with heart rate being determined by blood pressure. A change in blood pressure will cause the appropriate variation in heart rate and cardiac output individually. So, in this work, a closed-loop Simulink system is used for analyzing the variation through the baroreflex function with necessary feedback. The simulation response of the various cardiac parameters for different time delays is analyzed using MATLAB-Simulink.
{"title":"Analysis of Baroreflex Function in Cardiovascular Variability Model","authors":"C. R, Rahul S G, Amruthavalli Archakam, Sai Pramitha Meesala, Jaswanth Reddy Modium, Jagadish Kumar Pakalapati","doi":"10.1109/i-PACT52855.2021.9696983","DOIUrl":"https://doi.org/10.1109/i-PACT52855.2021.9696983","url":null,"abstract":"The proper functioning of the Cardiovascular system is dependent on several tissues like the heart, blood, arteries, and veins. These tissues are in contact with each other. In this paper, we present a cardiovascular model based on equivalent mathematical relations in Simulink environment for analyzing the changes that occur due to baroreceptors. The system aims to quantify and depicts the relation between various cardiac parameters like blood pressure, heart rate, stroke volume for different delays. The cardiovascular output is determined by stroke volume and heart rate, with heart rate being determined by blood pressure. A change in blood pressure will cause the appropriate variation in heart rate and cardiac output individually. So, in this work, a closed-loop Simulink system is used for analyzing the variation through the baroreflex function with necessary feedback. The simulation response of the various cardiac parameters for different time delays is analyzed using MATLAB-Simulink.","PeriodicalId":335956,"journal":{"name":"2021 Innovations in Power and Advanced Computing Technologies (i-PACT)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131148776","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 : 2021-11-27DOI: 10.1109/i-PACT52855.2021.9696646
Sathyaseelan K, S. Sarathambekai
Machine Learning is the subset of artificial intelligence where the machines are programmed to learn without any human intervention. The hidden complex patterns inside the data can be extracted with the help of machine learning algorithms. Health care industry can generate, store and analyze huge heterogeneous data like CT scan, MRI, fMRI, PET, SPECT, DTI, DOT etc. Apart from these data hospitals is responsible for various sources include hospital administrative records, medical records of patients, results of medical examinations, and data generated by the devices. Dealing with this type of multi-dimensional data manually is the challenging task and it may lead to reduce the prediction accuracy which will affect directly to the life of the patient. Hence we are in need of proper smart data management and analysis mechanism for deriving the accurate and meaningful information. Machine learning can play vital role in modern health care industry by providing the relevant solutions in less time with high accuracy. It also provides systematic and algorithmic approach tools for data management, analysis and interpretation. This paper presents the state-of-the -art of works related to machine learning techniques in health care sector.
{"title":"Machine Learning based Prediction Model for Health Care Sector - A Survey","authors":"Sathyaseelan K, S. Sarathambekai","doi":"10.1109/i-PACT52855.2021.9696646","DOIUrl":"https://doi.org/10.1109/i-PACT52855.2021.9696646","url":null,"abstract":"Machine Learning is the subset of artificial intelligence where the machines are programmed to learn without any human intervention. The hidden complex patterns inside the data can be extracted with the help of machine learning algorithms. Health care industry can generate, store and analyze huge heterogeneous data like CT scan, MRI, fMRI, PET, SPECT, DTI, DOT etc. Apart from these data hospitals is responsible for various sources include hospital administrative records, medical records of patients, results of medical examinations, and data generated by the devices. Dealing with this type of multi-dimensional data manually is the challenging task and it may lead to reduce the prediction accuracy which will affect directly to the life of the patient. Hence we are in need of proper smart data management and analysis mechanism for deriving the accurate and meaningful information. Machine learning can play vital role in modern health care industry by providing the relevant solutions in less time with high accuracy. It also provides systematic and algorithmic approach tools for data management, analysis and interpretation. This paper presents the state-of-the -art of works related to machine learning techniques in health care sector.","PeriodicalId":335956,"journal":{"name":"2021 Innovations in Power and Advanced Computing Technologies (i-PACT)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126988380","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 : 2021-11-27DOI: 10.1109/i-PACT52855.2021.9696699
Hamza Mubarak, H. Mokhlis, Nurulafiqah Nadzirah Binti Mansor, Mahazani Mohamad, Anis Salwa Mohd Khairuddin, Suhail Afzal
This paper proposes an optimal distribution networks expansion planning (DNEP) considering costs minimization to expand the networks, DG installation, and power losses under N-1 contingencies for every branch in the network with different load profiles. The proposed strategy has the capability to determine critical branches that significantly impacting the system power losses in case of outages. Due to the complexity of the proposed problem, hybrid metaheuristic technique comprises of Firefly algorithm and Particle Swarm Optimization were applied. The IEEE 33 bus system was utilized to validate the performance of the proposed method. The results shown that some branches have a huge impact on power losses during the N-1 contingency analysis. In addition, the power losses are minimized by 14.32% when DG is optimally considered in the planning strategy in terms of its sizing and placement.
{"title":"Optimal Distribution Networks Expansion Planning with DG for Power Losses Reduction","authors":"Hamza Mubarak, H. Mokhlis, Nurulafiqah Nadzirah Binti Mansor, Mahazani Mohamad, Anis Salwa Mohd Khairuddin, Suhail Afzal","doi":"10.1109/i-PACT52855.2021.9696699","DOIUrl":"https://doi.org/10.1109/i-PACT52855.2021.9696699","url":null,"abstract":"This paper proposes an optimal distribution networks expansion planning (DNEP) considering costs minimization to expand the networks, DG installation, and power losses under N-1 contingencies for every branch in the network with different load profiles. The proposed strategy has the capability to determine critical branches that significantly impacting the system power losses in case of outages. Due to the complexity of the proposed problem, hybrid metaheuristic technique comprises of Firefly algorithm and Particle Swarm Optimization were applied. The IEEE 33 bus system was utilized to validate the performance of the proposed method. The results shown that some branches have a huge impact on power losses during the N-1 contingency analysis. In addition, the power losses are minimized by 14.32% when DG is optimally considered in the planning strategy in terms of its sizing and placement.","PeriodicalId":335956,"journal":{"name":"2021 Innovations in Power and Advanced Computing Technologies (i-PACT)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116513364","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 : 2021-11-27DOI: 10.1109/i-PACT52855.2021.9696487
S. N, Monica R. Mundada
Internet of things is acknowledged by free progression of data among different low-power inserted gadgets which utilize the internet to communicate with each other. It is anticipated that the IoT will be generally conveyed and will discover pertinence in different areas of life. Requests of IoT have recently pulled in tremendous consideration, and associations are amped up for the business estimation of the information that will be generated by deployment of such networks. Unexpectedly, IoT has different privacy and security worries for the end users which limit its multiplication. This paper manages the different proposed IoT security requirements that are capable for verification, accessibility, encryption and trustworthiness for secure communication. The paper has been centered on security necessities for Internet of Things sensors, difficulties and open issues for various security prerequisites in IoT network communication, technologies for IoT communication, and their applications and open research issues. Each model has been compared and all the critical features have been presented.
{"title":"Secured Communication Strategies for Internet of Things Sensors","authors":"S. N, Monica R. Mundada","doi":"10.1109/i-PACT52855.2021.9696487","DOIUrl":"https://doi.org/10.1109/i-PACT52855.2021.9696487","url":null,"abstract":"Internet of things is acknowledged by free progression of data among different low-power inserted gadgets which utilize the internet to communicate with each other. It is anticipated that the IoT will be generally conveyed and will discover pertinence in different areas of life. Requests of IoT have recently pulled in tremendous consideration, and associations are amped up for the business estimation of the information that will be generated by deployment of such networks. Unexpectedly, IoT has different privacy and security worries for the end users which limit its multiplication. This paper manages the different proposed IoT security requirements that are capable for verification, accessibility, encryption and trustworthiness for secure communication. The paper has been centered on security necessities for Internet of Things sensors, difficulties and open issues for various security prerequisites in IoT network communication, technologies for IoT communication, and their applications and open research issues. Each model has been compared and all the critical features have been presented.","PeriodicalId":335956,"journal":{"name":"2021 Innovations in Power and Advanced Computing Technologies (i-PACT)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133607655","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 : 2021-11-27DOI: 10.1109/i-PACT52855.2021.9696650
V. R, S. S., Mohammed Tajuddin, Vinay Singhal, D. C.
In this paper, the goal is to improve the following exactness and the directing solace of an independent vehicle which depends on a fuzzy-logic-based Model Predictive Controller. The idea of self-governing vehicles is quick acquiring an enormous measure of fame for the right reasons. There are various frameworks that help oneself driving vehicle control the vehicle. Frameworks that require improvement incorporate the vehicle route framework, the circumstance framework, the electronic guide, the guide coordinating, the overall way arranging, the climate insight, the laser discernment, the radar insight, the visual discernment, the vehicle control, the view of vehicle speed and heading, and way following. Way following is a significant piece of its anything but a little mistake may cause mishaps. The future will without a doubt comprise of such vehicles, yet it faces a few obstacles to accomplish total substitution of present-day vehicles. The essential Model Predictive Controller is additionally acceptable arrangement contrasted with control calculations like PID. Model Predictive Controller is ideal for this application since it's anything but an ideal methodology and it can implant requirements which is vital as far as self-governing vehicles as imperatives like speed, yaw point and so on are available. After However, to accomplish a superior guiding solace, the proposed regulator is utilized. The job of fuzzy-logic in the framework is to adaptively differ the loads in order to accomplish the previously mentioned goals. Utilizing the CarSim and MATLAB/Simulink programming, it very well may be reproduced to show that it gives the necessary outcomes.
{"title":"Route Tracking Controller for Self-Directed Vehicles Based on an Enhanced Adaptive Weight Model Predictive Control","authors":"V. R, S. S., Mohammed Tajuddin, Vinay Singhal, D. C.","doi":"10.1109/i-PACT52855.2021.9696650","DOIUrl":"https://doi.org/10.1109/i-PACT52855.2021.9696650","url":null,"abstract":"In this paper, the goal is to improve the following exactness and the directing solace of an independent vehicle which depends on a fuzzy-logic-based Model Predictive Controller. The idea of self-governing vehicles is quick acquiring an enormous measure of fame for the right reasons. There are various frameworks that help oneself driving vehicle control the vehicle. Frameworks that require improvement incorporate the vehicle route framework, the circumstance framework, the electronic guide, the guide coordinating, the overall way arranging, the climate insight, the laser discernment, the radar insight, the visual discernment, the vehicle control, the view of vehicle speed and heading, and way following. Way following is a significant piece of its anything but a little mistake may cause mishaps. The future will without a doubt comprise of such vehicles, yet it faces a few obstacles to accomplish total substitution of present-day vehicles. The essential Model Predictive Controller is additionally acceptable arrangement contrasted with control calculations like PID. Model Predictive Controller is ideal for this application since it's anything but an ideal methodology and it can implant requirements which is vital as far as self-governing vehicles as imperatives like speed, yaw point and so on are available. After However, to accomplish a superior guiding solace, the proposed regulator is utilized. The job of fuzzy-logic in the framework is to adaptively differ the loads in order to accomplish the previously mentioned goals. Utilizing the CarSim and MATLAB/Simulink programming, it very well may be reproduced to show that it gives the necessary outcomes.","PeriodicalId":335956,"journal":{"name":"2021 Innovations in Power and Advanced Computing Technologies (i-PACT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131913748","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 : 2021-11-27DOI: 10.1109/i-PACT52855.2021.9696793
L. J., Chrystella Jacob, S. T, Riswanth S, S. M.
Fluid flow through pipes are characterised by the pressure, temperature, geometric constrictions and the regulatory devices set across its path. The study in this paper concentrates on the data obtained when the pressurised nitrogen gas is made to pass through a control valve to achieve the set pressure at the downstream. The data contains numerous data of pressure, temperature and the PID feedback values at different points along its path. The principal components involved in reaching the set pressure need to be taken for consideration for any future analysis. Hence Principal Component Analysis (PCA) is employed to find the variables with considerable variance. It was observed that the temperature at the inner surface and outer surface at the exit were the principal components.
{"title":"Analysis of the Flow Parameters of Gases Across Closed Pipe ection using Principal Component Analysis","authors":"L. J., Chrystella Jacob, S. T, Riswanth S, S. M.","doi":"10.1109/i-PACT52855.2021.9696793","DOIUrl":"https://doi.org/10.1109/i-PACT52855.2021.9696793","url":null,"abstract":"Fluid flow through pipes are characterised by the pressure, temperature, geometric constrictions and the regulatory devices set across its path. The study in this paper concentrates on the data obtained when the pressurised nitrogen gas is made to pass through a control valve to achieve the set pressure at the downstream. The data contains numerous data of pressure, temperature and the PID feedback values at different points along its path. The principal components involved in reaching the set pressure need to be taken for consideration for any future analysis. Hence Principal Component Analysis (PCA) is employed to find the variables with considerable variance. It was observed that the temperature at the inner surface and outer surface at the exit were the principal components.","PeriodicalId":335956,"journal":{"name":"2021 Innovations in Power and Advanced Computing Technologies (i-PACT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134552004","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 : 2021-11-27DOI: 10.1109/i-PACT52855.2021.9696760
B. Rohith, T. Santhosh, R. B. Alfred, R. R. Singh
This article proposes a technique for power distribution in the smart grid. This concept is based on a deep learning technique that employs the long short-term memory (LSTM), which is a recurrent neural network (RNN) architecture with respect to various parameters. The smart meter acquires data of different parameters including active power, reactive power, global intensity, and voltage from three independent households. The collected data is synced with a cloud and used with a sequential neural network model to forecast electricity consumption. In addition, the entire system was integrated by building a graphical user interface that allows customers to examine power at any specific date and time. This could be used to seek more power from the subsystem.
{"title":"GUI Energy Demand Forecast using LSTM Deep Learning Model in Python Platform","authors":"B. Rohith, T. Santhosh, R. B. Alfred, R. R. Singh","doi":"10.1109/i-PACT52855.2021.9696760","DOIUrl":"https://doi.org/10.1109/i-PACT52855.2021.9696760","url":null,"abstract":"This article proposes a technique for power distribution in the smart grid. This concept is based on a deep learning technique that employs the long short-term memory (LSTM), which is a recurrent neural network (RNN) architecture with respect to various parameters. The smart meter acquires data of different parameters including active power, reactive power, global intensity, and voltage from three independent households. The collected data is synced with a cloud and used with a sequential neural network model to forecast electricity consumption. In addition, the entire system was integrated by building a graphical user interface that allows customers to examine power at any specific date and time. This could be used to seek more power from the subsystem.","PeriodicalId":335956,"journal":{"name":"2021 Innovations in Power and Advanced Computing Technologies (i-PACT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134580314","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 : 2021-11-27DOI: 10.1109/i-PACT52855.2021.9696878
Inturi Meghana, Kumar Cherukupalli, M. Sravani, Paruchuri Chandra Babu Naidu
This paper mainly focuses on slip compensation in three-phase induction motor. It is important to reduce the slip of induction motor to maintain stable operation at different frequencies and loads. In order to decrease the slip, the difference of actual rotor and synchronous speed is added to the rotor speed using comparators and PI controllers. The decrease in slip is compensated by increasing the rotor frequency and feeding back to the input. Two techniques are being used in this project. They are feedback control technique and feedforward control technique. These are used to maintain the constant speed operation of an induction motor. This project deals with the slip compensation of open-loop v/f control of induction motor drive MATLAB. The slip compensation is used to improve the accuracy of the speed when the load is operated. It is mainly effective for v/f control technique.
{"title":"Simulation of Slip Compensation for Induction Motor Drive Using MATLAB","authors":"Inturi Meghana, Kumar Cherukupalli, M. Sravani, Paruchuri Chandra Babu Naidu","doi":"10.1109/i-PACT52855.2021.9696878","DOIUrl":"https://doi.org/10.1109/i-PACT52855.2021.9696878","url":null,"abstract":"This paper mainly focuses on slip compensation in three-phase induction motor. It is important to reduce the slip of induction motor to maintain stable operation at different frequencies and loads. In order to decrease the slip, the difference of actual rotor and synchronous speed is added to the rotor speed using comparators and PI controllers. The decrease in slip is compensated by increasing the rotor frequency and feeding back to the input. Two techniques are being used in this project. They are feedback control technique and feedforward control technique. These are used to maintain the constant speed operation of an induction motor. This project deals with the slip compensation of open-loop v/f control of induction motor drive MATLAB. The slip compensation is used to improve the accuracy of the speed when the load is operated. It is mainly effective for v/f control technique.","PeriodicalId":335956,"journal":{"name":"2021 Innovations in Power and Advanced Computing Technologies (i-PACT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133016426","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 : 2021-11-27DOI: 10.1109/i-PACT52855.2021.9696528
S. Shanmugam, P. Dhanasekaran, S. A. Lakshmanan, S. Balaganapathy, A. Sharmila
In today's world, we come across various incidents of traffic violations which can be solved with a number of approaches. Riding motorcycles/bikes without a helmet is violating the traffic rules which has led to a drastic increase in the number of road accidents and deaths. The already existing methods requires a lot of time and manpower since the number of violators are large in terms of frequency due to increase in the number of daily bike riders. Hence, a system which would automatically look for non-helmet riders and extract their number on the license plate is important. This paper explains the procedure to read the license plate of the riders who do not wear helmets. In this paper, object detection using neural networks and deep learning in multiple stages is proposed. The objects detected are humans, bikes (two-wheelers) in the first stage, helmet detection in the second and license plate number extraction in the last stage using deep learning algorithms. Results are shown to validate the performance of the proposed method.
{"title":"Deep Learning Algorithm based License Plate Detection for Traffic Control","authors":"S. Shanmugam, P. Dhanasekaran, S. A. Lakshmanan, S. Balaganapathy, A. Sharmila","doi":"10.1109/i-PACT52855.2021.9696528","DOIUrl":"https://doi.org/10.1109/i-PACT52855.2021.9696528","url":null,"abstract":"In today's world, we come across various incidents of traffic violations which can be solved with a number of approaches. Riding motorcycles/bikes without a helmet is violating the traffic rules which has led to a drastic increase in the number of road accidents and deaths. The already existing methods requires a lot of time and manpower since the number of violators are large in terms of frequency due to increase in the number of daily bike riders. Hence, a system which would automatically look for non-helmet riders and extract their number on the license plate is important. This paper explains the procedure to read the license plate of the riders who do not wear helmets. In this paper, object detection using neural networks and deep learning in multiple stages is proposed. The objects detected are humans, bikes (two-wheelers) in the first stage, helmet detection in the second and license plate number extraction in the last stage using deep learning algorithms. Results are shown to validate the performance of the proposed method.","PeriodicalId":335956,"journal":{"name":"2021 Innovations in Power and Advanced Computing Technologies (i-PACT)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133374085","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 : 2021-11-27DOI: 10.1109/i-PACT52855.2021.9696577
A. K. V. K. Reddy, K. Narayana
PV parameter estimation is a crucial step for the design of converters for solar panels. The optimal PV parameter estimation through two combinatorial social group whale optimization algorithms (HS-WOA and HS-WOA+) is done for single diode and double diode models. The optimization requires the minimization of the error function, formulated based on the three major points of operation of a PV panel. A comparative analysis involving monocrystalline, polycrystalline and thin-film PV cells is performed. The hybrid algorithms outperformed the parent algorithms in terms of optimality, accuracy and faster convergence.
{"title":"Extraction of Optimal Solar PV Parameters using Hybrid Optimization Techniques","authors":"A. K. V. K. Reddy, K. Narayana","doi":"10.1109/i-PACT52855.2021.9696577","DOIUrl":"https://doi.org/10.1109/i-PACT52855.2021.9696577","url":null,"abstract":"PV parameter estimation is a crucial step for the design of converters for solar panels. The optimal PV parameter estimation through two combinatorial social group whale optimization algorithms (HS-WOA and HS-WOA+) is done for single diode and double diode models. The optimization requires the minimization of the error function, formulated based on the three major points of operation of a PV panel. A comparative analysis involving monocrystalline, polycrystalline and thin-film PV cells is performed. The hybrid algorithms outperformed the parent algorithms in terms of optimality, accuracy and faster convergence.","PeriodicalId":335956,"journal":{"name":"2021 Innovations in Power and Advanced Computing Technologies (i-PACT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133811601","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：