Pub Date : 2022-07-21DOI: 10.1109/ICICCSP53532.2022.9862474
Subhashis Dey, Shamik Dasadhikari, Cherosree Dolui, Debabrata Roy
Iron-gallium alloys, known as Galfenol, can generate electrical energy from ambient vibrations. The device consists of a strip of Magnetostrictive Material Galfenol combined with a stainless-steel frame, copper coil, bias magnet, and soft iron to hold the bias magnet together. The total length of the energy harvester is 120 mm and a sinusoidal force is provided at the tip of the energy harvester. This paper deals with the experimental output of coil voltage obtained by varying the size of Galfenol and bias magnet (up to a possible range). The Magnetostrictive material (Galfenol) is varied from a length of 26 mm to 48 mm, similarly, these bias magnets are also varied from a length of 6 mm each to 11.5 mm each. Different values of coil voltage are obtained from different values of the length.
{"title":"Effect on Coil Voltage by Varying the Size of Galfenol in Magnetostrictive Energy Harvester","authors":"Subhashis Dey, Shamik Dasadhikari, Cherosree Dolui, Debabrata Roy","doi":"10.1109/ICICCSP53532.2022.9862474","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862474","url":null,"abstract":"Iron-gallium alloys, known as Galfenol, can generate electrical energy from ambient vibrations. The device consists of a strip of Magnetostrictive Material Galfenol combined with a stainless-steel frame, copper coil, bias magnet, and soft iron to hold the bias magnet together. The total length of the energy harvester is 120 mm and a sinusoidal force is provided at the tip of the energy harvester. This paper deals with the experimental output of coil voltage obtained by varying the size of Galfenol and bias magnet (up to a possible range). The Magnetostrictive material (Galfenol) is varied from a length of 26 mm to 48 mm, similarly, these bias magnets are also varied from a length of 6 mm each to 11.5 mm each. Different values of coil voltage are obtained from different values of the length.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133517510","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 : 2022-07-21DOI: 10.1109/ICICCSP53532.2022.9862482
K. Sameer, K. Haritha, N. Ramchander, B. Reddy, K. Rayudu, K. R. Reddy
Renewable energy is being produced through various resources, mostly natural and abundantly available, such as wind, solar, and geothermal. Solar PV technology is a novice alternate renewable energy system which is becoming popular during 21st century. In Solar Photovoltaic (SPV) power systems, the major component are polycrystalline PV modules which have a shelf-life of around 25 years, as claimed by most of the PV module producers. Most of the installations started 10 years ago and there is a need to investigate the ageing upshot or digression of PV modules. To this end, a seven-year-old large-scale PV plant is considered for case study. Field experiments are conducted to know the power output of these modules and the manufactures claim of 25 years life with indicated digression is validated with the field values. Also, machine learning technique is used to derive an empirical relation for the power output of age old PV modules. Finally, conclusions are drawn with respect to ageing upshot and life predictions of PV Modules.
{"title":"Field Investigation of Solar Photovoltaic Modules Digression Against Manufacture's Claim and Application of Machine Learning Model in Life Prediction: A Case Study","authors":"K. Sameer, K. Haritha, N. Ramchander, B. Reddy, K. Rayudu, K. R. Reddy","doi":"10.1109/ICICCSP53532.2022.9862482","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862482","url":null,"abstract":"Renewable energy is being produced through various resources, mostly natural and abundantly available, such as wind, solar, and geothermal. Solar PV technology is a novice alternate renewable energy system which is becoming popular during 21st century. In Solar Photovoltaic (SPV) power systems, the major component are polycrystalline PV modules which have a shelf-life of around 25 years, as claimed by most of the PV module producers. Most of the installations started 10 years ago and there is a need to investigate the ageing upshot or digression of PV modules. To this end, a seven-year-old large-scale PV plant is considered for case study. Field experiments are conducted to know the power output of these modules and the manufactures claim of 25 years life with indicated digression is validated with the field values. Also, machine learning technique is used to derive an empirical relation for the power output of age old PV modules. Finally, conclusions are drawn with respect to ageing upshot and life predictions of PV Modules.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134136459","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 : 2022-07-21DOI: 10.1109/ICICCSP53532.2022.9862353
Radharani Panigrahi, N. Patne, Sumanth Pemmada, Ashwini D. Manchalwar
This paper emphasizes the capability of Deep Learning (DL) models to conquer the Demand Response (DR) inherent when predicting the Electric Energy Consumption (EEC) of an office building. The prediction of EEC plays a key role in DR programs in a smart grid environment. In this study, historical energy consumption and ambient temperature data of three different climatic days (summer, winter, and cloudy days) of an office building located in Portugal at 10 seconds intervals are taken. A DL technique-based Deep Neural Network model is proposed for the prediction of future EEC. In this paper predictability of EEC of the whole office building has been analyzed. This study describes an evince DL application for commercial energy consumption prediction at 10 seconds intervals and performed precursory success. Moreover, two conventional Machine Learning (ML) models i.e., Support Vector Regressor (SVR) and Random Forest (RF) are developed and analyzed. Furthermore, the proposed DL model is compared with SVR and RF in terms of performance evaluation parameters such as Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). All the models are developed and executed on TensorFlow deep learning platform. The proposed model defeats SVR by 91.65%and RF by 87.38% on a summer day, similarly defeats SVR by 93.85% and RF by 91.68% on a winter day and defeats SVR by 95.63% and RF by 92.67% on a cloudy day in terms of MSE.
{"title":"Prediction of Electric Energy Consumption for Demand Response using Deep Learning","authors":"Radharani Panigrahi, N. Patne, Sumanth Pemmada, Ashwini D. Manchalwar","doi":"10.1109/ICICCSP53532.2022.9862353","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862353","url":null,"abstract":"This paper emphasizes the capability of Deep Learning (DL) models to conquer the Demand Response (DR) inherent when predicting the Electric Energy Consumption (EEC) of an office building. The prediction of EEC plays a key role in DR programs in a smart grid environment. In this study, historical energy consumption and ambient temperature data of three different climatic days (summer, winter, and cloudy days) of an office building located in Portugal at 10 seconds intervals are taken. A DL technique-based Deep Neural Network model is proposed for the prediction of future EEC. In this paper predictability of EEC of the whole office building has been analyzed. This study describes an evince DL application for commercial energy consumption prediction at 10 seconds intervals and performed precursory success. Moreover, two conventional Machine Learning (ML) models i.e., Support Vector Regressor (SVR) and Random Forest (RF) are developed and analyzed. Furthermore, the proposed DL model is compared with SVR and RF in terms of performance evaluation parameters such as Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). All the models are developed and executed on TensorFlow deep learning platform. The proposed model defeats SVR by 91.65%and RF by 87.38% on a summer day, similarly defeats SVR by 93.85% and RF by 91.68% on a winter day and defeats SVR by 95.63% and RF by 92.67% on a cloudy day in terms of MSE.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"228 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130314212","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 : 2022-07-21DOI: 10.1109/ICICCSP53532.2022.9862341
Narayan Nahak, R. Singh, S. Parida, Samarjeet Satapathy, P. Nayak
This work proposes an optimal fractional power system stabilizer control action to improve dynamic stability of grid integrated micro grid system. A fractional PID controller-based PSS has been implemented here whose gains are optimized by sailfish algorithm. The solar and wind generations in the micro grid are varied in step and random manner creating disturbances which is variation in angular frequency of power system. By proposed sailfish algorithm tuned PSS action this variation in angular frequency is heavily damped that has been compared with PSO & DE algorithms. System Eigen analysis has been performed to validate proposed optimal control action. The system eigen distributions and results analysis predict that proposed action is more efficient and is simple to implement for a micro grid system.
{"title":"Dynamic stability improvement of a micro grid system by optimized PSS controller","authors":"Narayan Nahak, R. Singh, S. Parida, Samarjeet Satapathy, P. Nayak","doi":"10.1109/ICICCSP53532.2022.9862341","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862341","url":null,"abstract":"This work proposes an optimal fractional power system stabilizer control action to improve dynamic stability of grid integrated micro grid system. A fractional PID controller-based PSS has been implemented here whose gains are optimized by sailfish algorithm. The solar and wind generations in the micro grid are varied in step and random manner creating disturbances which is variation in angular frequency of power system. By proposed sailfish algorithm tuned PSS action this variation in angular frequency is heavily damped that has been compared with PSO & DE algorithms. System Eigen analysis has been performed to validate proposed optimal control action. The system eigen distributions and results analysis predict that proposed action is more efficient and is simple to implement for a micro grid system.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130365147","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 : 2022-07-21DOI: 10.1109/ICICCSP53532.2022.9862447
Ankur Adhikary, Niloy Goswami, Kaushik Barua, Ratul Dey, Arindam Barman, M. A. Shawon
The ease of Doubly-Fed Induction Generator (DFIG) based wind turbines is largely deployed due to their variable speed feature and hence influencing system dynamics. However, owing to grid faults, the output power fluctuation in the DFIG wind turbine system brings a major concern to power system stability. In this paper, the grid voltage and frequency stability of the wind power system investigates different cases such as DFIG and the approach of a Battery Energy Storage System (BESS). Designing of a wind turbine model including Rotor Side Controller (RSC) and Grid Side Controller (GSC) and connected to the grid. An equivalent BESS is introduced in the power system model and connected to the grid through a three-phase inverter. The BESS system is designed to stabilize the frequency at a constant value with controlled active power also; voltage is controlled by reactive power. To design the wind turbine only active power is considered in this specific work. Therefore, the system performance has improved after including BESS. The performance analysis is observed by simulation work through “PSCAD/EMTDC” professional software, which is the most realistic and well-organized software, especially for power system analysis.
{"title":"Performance Analysis of a DFIG Based Wind Turbine with BESS System for Voltage and Frequency Stability during Grid Fault","authors":"Ankur Adhikary, Niloy Goswami, Kaushik Barua, Ratul Dey, Arindam Barman, M. A. Shawon","doi":"10.1109/ICICCSP53532.2022.9862447","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862447","url":null,"abstract":"The ease of Doubly-Fed Induction Generator (DFIG) based wind turbines is largely deployed due to their variable speed feature and hence influencing system dynamics. However, owing to grid faults, the output power fluctuation in the DFIG wind turbine system brings a major concern to power system stability. In this paper, the grid voltage and frequency stability of the wind power system investigates different cases such as DFIG and the approach of a Battery Energy Storage System (BESS). Designing of a wind turbine model including Rotor Side Controller (RSC) and Grid Side Controller (GSC) and connected to the grid. An equivalent BESS is introduced in the power system model and connected to the grid through a three-phase inverter. The BESS system is designed to stabilize the frequency at a constant value with controlled active power also; voltage is controlled by reactive power. To design the wind turbine only active power is considered in this specific work. Therefore, the system performance has improved after including BESS. The performance analysis is observed by simulation work through “PSCAD/EMTDC” professional software, which is the most realistic and well-organized software, especially for power system analysis.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115227292","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 : 2022-07-21DOI: 10.1109/ICICCSP53532.2022.9862469
Ashish Laddha, Satyanarayana Neeli
This article discusses the smart implementation of two-phase interleaved boost converter (TP-IBC) for fuel cell-powered electric vehicle (FCEV). The varying nature of the fuel cell (FC) output voltage and the load cause the common DC bus voltage to deviate from its referenced value. Thus, this manuscript proposes the fuzzy logic-led PID control scheme for the regulation of the common DC bus voltage. Employing of the fuzzy logic adds a factor of intelligence to the PID controller. This factor enables gains of the PID controller to adjust themselves according to the changing operational conditions.
{"title":"Intelligent Control of a Two-phase Interleaved Boost Converter-interfaced Fuel Cell Electric Vehicle","authors":"Ashish Laddha, Satyanarayana Neeli","doi":"10.1109/ICICCSP53532.2022.9862469","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862469","url":null,"abstract":"This article discusses the smart implementation of two-phase interleaved boost converter (TP-IBC) for fuel cell-powered electric vehicle (FCEV). The varying nature of the fuel cell (FC) output voltage and the load cause the common DC bus voltage to deviate from its referenced value. Thus, this manuscript proposes the fuzzy logic-led PID control scheme for the regulation of the common DC bus voltage. Employing of the fuzzy logic adds a factor of intelligence to the PID controller. This factor enables gains of the PID controller to adjust themselves according to the changing operational conditions.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114706746","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 : 2022-07-21DOI: 10.1109/ICICCSP53532.2022.9862320
Arnab Ari, Aashish Kumar Bohre
Renewable energy resources have an inherent drawback: they are intermittent. Due to this, individual renewable resources in standalone scenarios are not reliable and thus cannot be utilized for practical large-scale applications. A hybrid renewable energy system solves this issue by integrating multiple renewable, non-renewable and storage systems. Since multiple sources are used, distributed generation is possible in the case of HRES. This will not only help to satisfy the demand but also reduce losses and improve voltage profile besides reducing carbon footprint. This paper studies the effect of PV and WTG on the load flow and harmonics in a gird connected radial distribution system. Modeling of the system is discussed with four different cases for comparison. Harmonic analysis is performed to obtain the THD and TIF parameters to understand the voltage and current distortions and their effect on communication systems. It is found that the PV and Wind power generation system provides the best voltage profile. The bus voltage distortions are decreased and the branch currents are relatively distorted.
{"title":"Harmonic and TIF Analysis in Distribution System with Integration of PV and Wind Systems","authors":"Arnab Ari, Aashish Kumar Bohre","doi":"10.1109/ICICCSP53532.2022.9862320","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862320","url":null,"abstract":"Renewable energy resources have an inherent drawback: they are intermittent. Due to this, individual renewable resources in standalone scenarios are not reliable and thus cannot be utilized for practical large-scale applications. A hybrid renewable energy system solves this issue by integrating multiple renewable, non-renewable and storage systems. Since multiple sources are used, distributed generation is possible in the case of HRES. This will not only help to satisfy the demand but also reduce losses and improve voltage profile besides reducing carbon footprint. This paper studies the effect of PV and WTG on the load flow and harmonics in a gird connected radial distribution system. Modeling of the system is discussed with four different cases for comparison. Harmonic analysis is performed to obtain the THD and TIF parameters to understand the voltage and current distortions and their effect on communication systems. It is found that the PV and Wind power generation system provides the best voltage profile. The bus voltage distortions are decreased and the branch currents are relatively distorted.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115007741","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 : 2022-07-21DOI: 10.1109/ICICCSP53532.2022.9862386
Pragya, Ritula Thakur
This paper reviews architecture of hybrid AC/DC microgrid and several controlling strategies for hybrid AC/DC microgrid. Interconnected group of networks of loads, energy storage system and distributed energy sources defines a microgrid. To avoid multiple conversion that occurs in individual AC as well as DC grid in the microgrid system, hybrid microgrid is a solution for such issues. Balancing of power between both the microgrids is done with an Interlinking converter. It will balance power by transfer of power through one microgrid to another. This paper summarizes various controlling methods from the several aspects and existing issues in every method presented here.
{"title":"A Review of Architecture and Control Strategies of Hybrid AC/DC Microgrid","authors":"Pragya, Ritula Thakur","doi":"10.1109/ICICCSP53532.2022.9862386","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862386","url":null,"abstract":"This paper reviews architecture of hybrid AC/DC microgrid and several controlling strategies for hybrid AC/DC microgrid. Interconnected group of networks of loads, energy storage system and distributed energy sources defines a microgrid. To avoid multiple conversion that occurs in individual AC as well as DC grid in the microgrid system, hybrid microgrid is a solution for such issues. Balancing of power between both the microgrids is done with an Interlinking converter. It will balance power by transfer of power through one microgrid to another. This paper summarizes various controlling methods from the several aspects and existing issues in every method presented here.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121079357","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 : 2022-07-21DOI: 10.1109/ICICCSP53532.2022.9862405
Subir Datta, S. Deb, Robert Singh, Rahul Roy, Akibul Islam, S. Adhikari
Nowadays, oscillation due to low frequency is a very serious issue in power system. It affects steady state power transfer which hampers security and economic operation of the system. FACTs devices play a key role to mitigate the low frequency oscillations. Therefore, in this paper STATCOM and its associated controllers are considered in order to damp out oscillations produced due to low frequency in power system and Firefly Algorithm (FA) is also used to optimize the gain values of STATCOM controllers. An extensive simulation of the study system has been implemented using MATLAB/Simulink platform. System responses have been obtained with PSS and also with compensator comprising of both PSS and STATCOM. Time domain simulation studies are utilized to check effectiveness of the FA based proposed controllers. The simulation results obtained revealed that PSS with STATCOM has excellent capabilities in damping power system oscillations with low frequency.
{"title":"Firefly Algorithm based STATCOM Controller for Enhancement of Power System Dynamic Stability","authors":"Subir Datta, S. Deb, Robert Singh, Rahul Roy, Akibul Islam, S. Adhikari","doi":"10.1109/ICICCSP53532.2022.9862405","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862405","url":null,"abstract":"Nowadays, oscillation due to low frequency is a very serious issue in power system. It affects steady state power transfer which hampers security and economic operation of the system. FACTs devices play a key role to mitigate the low frequency oscillations. Therefore, in this paper STATCOM and its associated controllers are considered in order to damp out oscillations produced due to low frequency in power system and Firefly Algorithm (FA) is also used to optimize the gain values of STATCOM controllers. An extensive simulation of the study system has been implemented using MATLAB/Simulink platform. System responses have been obtained with PSS and also with compensator comprising of both PSS and STATCOM. Time domain simulation studies are utilized to check effectiveness of the FA based proposed controllers. The simulation results obtained revealed that PSS with STATCOM has excellent capabilities in damping power system oscillations with low frequency.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124212363","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 : 2022-07-21DOI: 10.1109/ICICCSP53532.2022.9862477
Soumik Kundu, Subhankit Prusti, S. Patnaik
Ventricular Fibrillation is a potentially fatal cardiac disorder that occurs when electrical impulses in the ventricles are disrupted, causing the heart to quiver instead of pump. In order to preserve lives during this form of arrhythmia, a strong current impulse is passed. Electrocardiograms (ECGs) record the electrical activity of the human heart, and specialists with years of experience may interpret the ECG signal to determine the heart's condition. Since it is a life-threatening disease, its earlier detection and prevention can help survive a patient's life. The fundamental idea behind tackling this challenge was to create an algorithm that could identify trends from continuous ECG readings from various individuals and identify arrhythmias early on. An efficient data was built for classification utilizing a Random Forest classifier algorithm employing signal processing tools such as Empirical Mode Decomposition (EMD) and Discrete Fourier Transform (DFT) for feature extraction. The pre-processed data when fed into the proposed machine learning method results in an accuracy of 96.58% and two classes were classified correctly with equal confidence (Specificity = 94.26% and Sensitivity = 98.97%). Furthermore, the results are compared with various other machine learning classification algorithms like Logistic Regression, Decision Tree classifier, Extra tree classifier where the accuracy was 86.49%, 91.77%, 95.84% respectively. The results obtained after experimental validation of proposed Random Forest classifier algorithm against the other machine learning achieves highest accuracy with optimal specificity and sensitivity.
{"title":"Detection of Ventricular Fibrillation by combining Signal Processing and Machine Learning approach","authors":"Soumik Kundu, Subhankit Prusti, S. Patnaik","doi":"10.1109/ICICCSP53532.2022.9862477","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862477","url":null,"abstract":"Ventricular Fibrillation is a potentially fatal cardiac disorder that occurs when electrical impulses in the ventricles are disrupted, causing the heart to quiver instead of pump. In order to preserve lives during this form of arrhythmia, a strong current impulse is passed. Electrocardiograms (ECGs) record the electrical activity of the human heart, and specialists with years of experience may interpret the ECG signal to determine the heart's condition. Since it is a life-threatening disease, its earlier detection and prevention can help survive a patient's life. The fundamental idea behind tackling this challenge was to create an algorithm that could identify trends from continuous ECG readings from various individuals and identify arrhythmias early on. An efficient data was built for classification utilizing a Random Forest classifier algorithm employing signal processing tools such as Empirical Mode Decomposition (EMD) and Discrete Fourier Transform (DFT) for feature extraction. The pre-processed data when fed into the proposed machine learning method results in an accuracy of 96.58% and two classes were classified correctly with equal confidence (Specificity = 94.26% and Sensitivity = 98.97%). Furthermore, the results are compared with various other machine learning classification algorithms like Logistic Regression, Decision Tree classifier, Extra tree classifier where the accuracy was 86.49%, 91.77%, 95.84% respectively. The results obtained after experimental validation of proposed Random Forest classifier algorithm against the other machine learning achieves highest accuracy with optimal specificity and sensitivity.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125321406","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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