Photovoltaic (PV) have become a priority renewable energy source to be developed in Indonesia to achieve new and renewable energy (NRE) target of 23% in 2025 and 31% in 2050. The operation of a significant number of rooftop PV can also change the type of power system operating configuration to Distributed Energy Generation (DEG). The majority of DEGs which are NRE generators are capable of causing new problems because of their intermittent nature. Hosting Capacity is a high penetration limit for NRE without causing problems and limits on operational violations. The hosting capacity method used is based on the generator's minimum operation. In the test system consisting of 3 power plants such as hydro power plant, coal power plant, and geothermal power plant, the PV capacity that can be injected into the system is 139.1 MW. With PV injection based on hosting capacity, the system becomes better with the same average voltage profile as before PV injection, namely 0.991 p.u. System stability by reviewing the frequency, rotor angle, and rotor speed, the system after PV injection is better than before PV injection.
{"title":"Investigation of Photovoltaic Hosting Capacity Using Minimum Generation Operation Approach","authors":"Syafii Syafii, Thoriq Kurnia Agung, Dawam Habibullah","doi":"10.52549/ijeei.v11i3.4856","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.4856","url":null,"abstract":"Photovoltaic (PV) have become a priority renewable energy source to be developed in Indonesia to achieve new and renewable energy (NRE) target of 23% in 2025 and 31% in 2050. The operation of a significant number of rooftop PV can also change the type of power system operating configuration to Distributed Energy Generation (DEG). The majority of DEGs which are NRE generators are capable of causing new problems because of their intermittent nature. Hosting Capacity is a high penetration limit for NRE without causing problems and limits on operational violations. The hosting capacity method used is based on the generator's minimum operation. In the test system consisting of 3 power plants such as hydro power plant, coal power plant, and geothermal power plant, the PV capacity that can be injected into the system is 139.1 MW. With PV injection based on hosting capacity, the system becomes better with the same average voltage profile as before PV injection, namely 0.991 p.u. System stability by reviewing the frequency, rotor angle, and rotor speed, the system after PV injection is better than before PV injection.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135032017","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}
The millennial generation has a pivotal role in leading the industrial digital revolution. Energy-saving behavior and millennials’ awareness of energy consumption for educational context become crucial in performing a smart campus. This study tries to identify the success factors in measuring the millennial generation’s energy-saving Behavior toward the smart campus. The measurement model considers two significant constructs, including energy-saving attitudes with energy-saving education (organizational saving climate); energy-saving education and environment knowledge (personal saving climate); and energy-saving information publicity as sub-indicators, and construct energy-saving Behavior viz sub-indicators Behavior regarding energy and behavior control. In order to determine the preference level of each indicator and sub-indicator, the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) approach was executed by disseminating the questionnaire to 100 respondents from energy practitioners, students, and academicians in Indonesia. The calculation reveals that the energy-saving behavior construct has a higher priority value (0.94) than the energy-saving attitude (0.06). Meanwhile, energy-saving education and environment knowledge (personal saving climate) have been analyzed at the cutting-edge sub-indicator, followed by energy-saving information publicity and education (organizational saving climate). In addition, the sub-indicator for behaviors regarding energy becomes more demanding compared to behavioral control. As a novelty, the priority analysis of this Model aids the management of the campus and government in developing smart campus policies and governance. This Model can be used as a guideline for the management level to execute the smart campus practices. Thus, the effectiveness and optimization of smart campus transformation can be cultivated and accelerated. Besides, the potential coming of risks can be avoidable.
{"title":"The Success Factors in Measuring the Millennial Generation’s Energy-Saving Behavior Toward the Smart Campus","authors":"Lola Oktavia, Okfalisa Okfalisa, Pizaini Pizaini, Rahmad Abdillah, Saktioto Saktioto","doi":"10.52549/ijeei.v11i3.4885","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.4885","url":null,"abstract":"The millennial generation has a pivotal role in leading the industrial digital revolution. Energy-saving behavior and millennials’ awareness of energy consumption for educational context become crucial in performing a smart campus. This study tries to identify the success factors in measuring the millennial generation’s energy-saving Behavior toward the smart campus. The measurement model considers two significant constructs, including energy-saving attitudes with energy-saving education (organizational saving climate); energy-saving education and environment knowledge (personal saving climate); and energy-saving information publicity as sub-indicators, and construct energy-saving Behavior viz sub-indicators Behavior regarding energy and behavior control. In order to determine the preference level of each indicator and sub-indicator, the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) approach was executed by disseminating the questionnaire to 100 respondents from energy practitioners, students, and academicians in Indonesia. The calculation reveals that the energy-saving behavior construct has a higher priority value (0.94) than the energy-saving attitude (0.06). Meanwhile, energy-saving education and environment knowledge (personal saving climate) have been analyzed at the cutting-edge sub-indicator, followed by energy-saving information publicity and education (organizational saving climate). In addition, the sub-indicator for behaviors regarding energy becomes more demanding compared to behavioral control. As a novelty, the priority analysis of this Model aids the management of the campus and government in developing smart campus policies and governance. This Model can be used as a guideline for the management level to execute the smart campus practices. Thus, the effectiveness and optimization of smart campus transformation can be cultivated and accelerated. Besides, the potential coming of risks can be avoidable.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135295937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-28DOI: 10.52549/ijeei.v11i3.4892
Siriporn Chimphlee, Witcha Chimphlee
Machine Learning (ML) models and the massive quantity of data accessible provide useful tools for analyzing the advancement of climate change trends and identifying major contributors. Random Forest (RF), Gradient Boosting Regression (GBR), XGBoost (XGB), Support Vector Machines (SVC), Decision Trees (DT), K-Nearest Neighbors (KNN), Principal Component Analysis (PCA), ensemble methods, and Genetic Algorithms (GA) are used in this study to predict CO2 emissions in Thailand. A variety of evaluation criteria are used to determine how well these models work, including R-squared (R2), mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and correctness. The results show that the RF and XGB algorithms function exceptionally well, with high R-squared values and low error rates. KNN, PCA, ensemble methods, and GA, on the other hand, outperform the top-performing models. Their lower R-squared values and higher error scores indicate that they are unable to accurately anticipate CO2 emissions. This paper contributes to the field of environmental modeling by comparing the effectiveness of various machine learning approaches in forecasting CO2 emissions. The findings can assist Thailand in promoting sustainable development and developing policies that are consistent with worldwide efforts to combat climate change.
{"title":"Forecasting Carbon Dioxide Emission in Thailand Using Machine Learning Techniques","authors":"Siriporn Chimphlee, Witcha Chimphlee","doi":"10.52549/ijeei.v11i3.4892","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.4892","url":null,"abstract":"Machine Learning (ML) models and the massive quantity of data accessible provide useful tools for analyzing the advancement of climate change trends and identifying major contributors. Random Forest (RF), Gradient Boosting Regression (GBR), XGBoost (XGB), Support Vector Machines (SVC), Decision Trees (DT), K-Nearest Neighbors (KNN), Principal Component Analysis (PCA), ensemble methods, and Genetic Algorithms (GA) are used in this study to predict CO2 emissions in Thailand. A variety of evaluation criteria are used to determine how well these models work, including R-squared (R2), mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and correctness. The results show that the RF and XGB algorithms function exceptionally well, with high R-squared values and low error rates. KNN, PCA, ensemble methods, and GA, on the other hand, outperform the top-performing models. Their lower R-squared values and higher error scores indicate that they are unable to accurately anticipate CO2 emissions. This paper contributes to the field of environmental modeling by comparing the effectiveness of various machine learning approaches in forecasting CO2 emissions. The findings can assist Thailand in promoting sustainable development and developing policies that are consistent with worldwide efforts to combat climate change.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135470272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-28DOI: 10.52549/ijeei.v11i3.5009
Mohamad Iqmal Jamaludin, Teddy Surya Gunawan, Rajendra Kumar Karupiah, Suriza Ahmad Zabidi, Mira Kartiwi, Zamzuri Zakaria
In the burgeoning field of Artificial Intelligence (AI) and its notable subsets, such as Deep Learning (DL), there is evidence of its transformative impact in assisting clinicians, particularly in diagnosing scoliosis. AI is unrivaled for its speed and precision in analyzing medical images, including X-rays and computed tomography (CT) scans. However, the path does not lack obstacles. Biases, unanticipated outcomes, and false positive and negative predictions present significant challenges. Our research employed three complex experimental sets, each focusing on adapting the U-Net architecture. Through a nuanced combination of feed-forward neural network (FFNN) configurations and hyperparameters, we endeavored to determine the most effective nonlinear regression model configuration for predicting the Cobb angle. This was done with the dual purpose of reducing AI training time without sacrificing predictive accuracy. Utilizing the capabilities of the PyTorch framework, we meticulously crafted and refined the deep learning models for each of the three experiments, focusing on an FFFN dropout rate of p=0.45. The Root Mean Square Error (RMSE), the number of epochs, and the number of nodes spanning three hidden layers in each FFFN were utilized as crucial performance metrics while a base learning rate of 0.001 was maintained. Notably, during the optimization phase, one of the experiments incorporated a learning rate scheduler to protect against potential pitfalls such as local minima and saddle points. A judiciously incorporated Early Stopping technique, triggered between the patience range of 5-10 epochs, ensured model stability as the Mean Squared Error (MSE) plateau loss approached approximately 1. Consequently, the model converged between 50 and 82 epochs. We hypothesize that our proposed architecture holds promise for future refinements, conditioned on assiduous experimentation with an array of medical deep learning paradigms.
{"title":"Optimizing U-Net Architecture with Feed-Forward Neural Networks for Precise Cobb Angle Prediction in Scoliosis Diagnosis","authors":"Mohamad Iqmal Jamaludin, Teddy Surya Gunawan, Rajendra Kumar Karupiah, Suriza Ahmad Zabidi, Mira Kartiwi, Zamzuri Zakaria","doi":"10.52549/ijeei.v11i3.5009","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.5009","url":null,"abstract":"In the burgeoning field of Artificial Intelligence (AI) and its notable subsets, such as Deep Learning (DL), there is evidence of its transformative impact in assisting clinicians, particularly in diagnosing scoliosis. AI is unrivaled for its speed and precision in analyzing medical images, including X-rays and computed tomography (CT) scans. However, the path does not lack obstacles. Biases, unanticipated outcomes, and false positive and negative predictions present significant challenges. Our research employed three complex experimental sets, each focusing on adapting the U-Net architecture. Through a nuanced combination of feed-forward neural network (FFNN) configurations and hyperparameters, we endeavored to determine the most effective nonlinear regression model configuration for predicting the Cobb angle. This was done with the dual purpose of reducing AI training time without sacrificing predictive accuracy. Utilizing the capabilities of the PyTorch framework, we meticulously crafted and refined the deep learning models for each of the three experiments, focusing on an FFFN dropout rate of p=0.45. The Root Mean Square Error (RMSE), the number of epochs, and the number of nodes spanning three hidden layers in each FFFN were utilized as crucial performance metrics while a base learning rate of 0.001 was maintained. Notably, during the optimization phase, one of the experiments incorporated a learning rate scheduler to protect against potential pitfalls such as local minima and saddle points. A judiciously incorporated Early Stopping technique, triggered between the patience range of 5-10 epochs, ensured model stability as the Mean Squared Error (MSE) plateau loss approached approximately 1. Consequently, the model converged between 50 and 82 epochs. We hypothesize that our proposed architecture holds promise for future refinements, conditioned on assiduous experimentation with an array of medical deep learning paradigms.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135470453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-27DOI: 10.52549/ijeei.v11i3.5037
Nomsa Puso, Tshiamo Sigwele, Oba Zubair Mustapha
The rapid growth of cloud computing has led to a significant increase in energy consumption, which is a major concern for the environment and economy. To address this issue, researchers have proposed various techniques to improve the energy efficiency of cloud computing, including the use of machine learning (ML) algorithms. This research provides a comprehensive review of energy efficiency in cloud computing using ML techniques and extensively compares different ML approaches in terms of the learning model adopted, ML tools used, model strengths and limitations, datasets used, evaluation metrics and performance. The review categorizes existing approaches into Virtual Machine (VM) selection, VM placement, VM migration, and consolidation methods. This review highlights that among the array of ML models, Deep Reinforcement Learning, TensorFlow as a platform, and CloudSim for dataset generation are the most widely adopted in the literature and emerge as the best choices for constructing ML-driven models that optimize energy consumption in cloud computing.
{"title":"Machine Learning Centered Energy Optimization In Cloud Computing: A Review","authors":"Nomsa Puso, Tshiamo Sigwele, Oba Zubair Mustapha","doi":"10.52549/ijeei.v11i3.5037","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.5037","url":null,"abstract":"The rapid growth of cloud computing has led to a significant increase in energy consumption, which is a major concern for the environment and economy. To address this issue, researchers have proposed various techniques to improve the energy efficiency of cloud computing, including the use of machine learning (ML) algorithms. This research provides a comprehensive review of energy efficiency in cloud computing using ML techniques and extensively compares different ML approaches in terms of the learning model adopted, ML tools used, model strengths and limitations, datasets used, evaluation metrics and performance. The review categorizes existing approaches into Virtual Machine (VM) selection, VM placement, VM migration, and consolidation methods. This review highlights that among the array of ML models, Deep Reinforcement Learning, TensorFlow as a platform, and CloudSim for dataset generation are the most widely adopted in the literature and emerge as the best choices for constructing ML-driven models that optimize energy consumption in cloud computing.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135585945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-27DOI: 10.52549/ijeei.v11i3.4607
Mitali Ray, Lohit Kumar Sahoo
The process of limiting the amount of energy that is utilized is known as energy conservation. This can be accomplished by making more effective use of the energy that is available. As a result, there is a requirement for more effective management of the consumption of energy in buildings. It is essential to have an accurate load calculation for a residential building because the loads for heating and cooling add up a significant portion of the total building loads. In this study, the load analysis of the HVAC (Heating, Ventilation, and Air Conditioning) system in a residential building was carried out by taking into consideration three different neural networks. These networks are known as the feed forward network, the cascaded forward back propagation network, and the Elman back propagation network. During the process of conducting a load study of the heating and cooling loads on an HVAC system, performance measurements like MAE (mean absolute error), MSE (mean square error), MRE (mean relative error), and MAPE (mean absolute percentage error) are taken into consideration. It has been discovered that the cascaded forward back propagation method is the most effective method, with MAE, MSE, MRE, and MAPE values of 0.08, 0.0336, 0.0051, and 0.51% respectively for heating load and MAE, MSE, MRE, and MAPE values of 0.0975, 0.0406, 0.0053, and 0.53% respectively for cooling load.
{"title":"HVAC Load Analysis of Residential Building Using ANN Techniques","authors":"Mitali Ray, Lohit Kumar Sahoo","doi":"10.52549/ijeei.v11i3.4607","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.4607","url":null,"abstract":"The process of limiting the amount of energy that is utilized is known as energy conservation. This can be accomplished by making more effective use of the energy that is available. As a result, there is a requirement for more effective management of the consumption of energy in buildings. It is essential to have an accurate load calculation for a residential building because the loads for heating and cooling add up a significant portion of the total building loads. In this study, the load analysis of the HVAC (Heating, Ventilation, and Air Conditioning) system in a residential building was carried out by taking into consideration three different neural networks. These networks are known as the feed forward network, the cascaded forward back propagation network, and the Elman back propagation network. During the process of conducting a load study of the heating and cooling loads on an HVAC system, performance measurements like MAE (mean absolute error), MSE (mean square error), MRE (mean relative error), and MAPE (mean absolute percentage error) are taken into consideration. It has been discovered that the cascaded forward back propagation method is the most effective method, with MAE, MSE, MRE, and MAPE values of 0.08, 0.0336, 0.0051, and 0.51% respectively for heating load and MAE, MSE, MRE, and MAPE values of 0.0975, 0.0406, 0.0053, and 0.53% respectively for cooling load.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135587360","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}
The blood flow that carries various particles results in disturbed physical flow in the heart organ caused by speed, density, and pressure. This phenomenon is complicated resulting in a wide variety of medical problems. This research provides a mathematical technique and numerical experiment for a straightforward solution to cardiac blood flow to arteries. Finite element analysis (FEA) is used to study and construct mathematical models for human blood flow through arterial branches. Furthermore, FEA is used to simulate the steady two-dimensional flow of viscous fluids across various geometries. The results showed that the blood flow in the carotid artery branching is simulated after the velocity profiles obtained are plotted against the experimental design. The computational method's validity is evaluated by comparing the numerical experiment with the analytical results of various functions
{"title":"Simplified Kinetic Model of Heart Pressure for Human Dynamical Blood Flow","authors":"Saktioto Saktioto, Defrianto Defrianto, Andika Thoibah, Yan Soerbakti, Romi Fadli Syahputra, Syamsudhuha Syamsudhuha, Dedi Irawan, Haryana Hairi, Okfalisa Okfalisa, Rina Amelia","doi":"10.52549/ijeei.v11i3.3473","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.3473","url":null,"abstract":"The blood flow that carries various particles results in disturbed physical flow in the heart organ caused by speed, density, and pressure. This phenomenon is complicated resulting in a wide variety of medical problems. This research provides a mathematical technique and numerical experiment for a straightforward solution to cardiac blood flow to arteries. Finite element analysis (FEA) is used to study and construct mathematical models for human blood flow through arterial branches. Furthermore, FEA is used to simulate the steady two-dimensional flow of viscous fluids across various geometries. The results showed that the blood flow in the carotid artery branching is simulated after the velocity profiles obtained are plotted against the experimental design. The computational method's validity is evaluated by comparing the numerical experiment with the analytical results of various functions","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135587494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-26DOI: 10.52549/ijeei.v11i3.4786
Dwipen Laskar, Anjana Kakoti Mahanta
In many applications, transactions are associated with intervals related to time, temperature, humidity or other similar measures. The term "2D interval data" or "rectangle data" is used when there are two connected intervals with each transaction. Two connected intervals give rise to a rectangle. The rectangles may overlap producing regions with different density values. The density value or support of a region is the number of rectangles that contain it. A region is closed if its density is strictly bigger than any region properly containing it. For rectangle dataset, these regions are rectangular in shape.In this paper an algorithm named ACRMiner has been proposed that takes as input a sequence of rectangles and computes all closed overlapping rectangles and their density values. The algorithm is incremental and thus is suitable for dynamic environment. Depending on an input threshold the regions can be classified as dense and sparse.Here a tree-based data structure named as ACR-Tree is used. The method has been implemented and tested on synthetic and real-life datasets and results have been reported. Few applications of this algorithm have been discussed. The worst-case time complexity the algorithmis O(n 5 ) where n is the number of input rectangles.
{"title":"ACRMiner: An Incremental Approach for Finding Dense and Sparse Rectangular Regions from a 2D Interval Dataset","authors":"Dwipen Laskar, Anjana Kakoti Mahanta","doi":"10.52549/ijeei.v11i3.4786","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.4786","url":null,"abstract":"In many applications, transactions are associated with intervals related to time, temperature, humidity or other similar measures. The term \"2D interval data\" or \"rectangle data\" is used when there are two connected intervals with each transaction. Two connected intervals give rise to a rectangle. The rectangles may overlap producing regions with different density values. The density value or support of a region is the number of rectangles that contain it. A region is closed if its density is strictly bigger than any region properly containing it. For rectangle dataset, these regions are rectangular in shape.In this paper an algorithm named ACRMiner has been proposed that takes as input a sequence of rectangles and computes all closed overlapping rectangles and their density values. The algorithm is incremental and thus is suitable for dynamic environment. Depending on an input threshold the regions can be classified as dense and sparse.Here a tree-based data structure named as ACR-Tree is used. The method has been implemented and tested on synthetic and real-life datasets and results have been reported. Few applications of this algorithm have been discussed. The worst-case time complexity the algorithmis O(n 5 ) where n is the number of input rectangles.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135721623","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}
Currently, pulse width modulation (PWM) is a prevalent technique in the field of DC-DC converter control. Its primary objectives encompass maintaining the regulation of the converter's output voltage and improving the load's performance by mitigating the adverse effects caused by harmonic distortions. Unfortunately, the utilization of PWM is associated with significant levels of residual harmonics, characterized by notable amplitudes and frequencies, which have the potential to induce mechanical vibrations, acoustic disturbances, and electromagnetic interference (EMI).To address this challenge, a method known as pseudo-random modulation (PRM) has been developed. In comparison to traditional PWM, PRM offers ease of implementation and high efficacy in EMI mitigation. PRM achieves this by distributing harmonic power across a broader frequency range, thereby reducing the prominence of high-amplitude harmonics at specific frequencies. Within the context of Spread Spectrum Modulation (SSM), this study extensively explores diverse converter topologies and proposes an innovative hardware implementation using the cost-effective Atmega328p microcontroller. Furthermore, the study scrutinizes the consequences of implementing this randomized control strategy to reduce electromagnetic emissions from a Boost converter, a well-recognized source of significant interference in its operational environment. Ultimately, the aim is to evaluate the effectiveness of these applied methodologies in achieving the maximum dispersion of the power spectrum, thereby enhancing overall electromagnetic compatibility.
{"title":"Implementing Pseudo-Random Control in Boost Converter: An Effective Approach for Mitigating Conducted Electromagnetic Emissions","authors":"Zakaria M'barki, Youssef Mejdoub, Kaoutar Senhaji Rhazi, Khalid Sabhi","doi":"10.52549/ijeei.v11i3.4832","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.4832","url":null,"abstract":"Currently, pulse width modulation (PWM) is a prevalent technique in the field of DC-DC converter control. Its primary objectives encompass maintaining the regulation of the converter's output voltage and improving the load's performance by mitigating the adverse effects caused by harmonic distortions. Unfortunately, the utilization of PWM is associated with significant levels of residual harmonics, characterized by notable amplitudes and frequencies, which have the potential to induce mechanical vibrations, acoustic disturbances, and electromagnetic interference (EMI).To address this challenge, a method known as pseudo-random modulation (PRM) has been developed. In comparison to traditional PWM, PRM offers ease of implementation and high efficacy in EMI mitigation. PRM achieves this by distributing harmonic power across a broader frequency range, thereby reducing the prominence of high-amplitude harmonics at specific frequencies. Within the context of Spread Spectrum Modulation (SSM), this study extensively explores diverse converter topologies and proposes an innovative hardware implementation using the cost-effective Atmega328p microcontroller. Furthermore, the study scrutinizes the consequences of implementing this randomized control strategy to reduce electromagnetic emissions from a Boost converter, a well-recognized source of significant interference in its operational environment. Ultimately, the aim is to evaluate the effectiveness of these applied methodologies in achieving the maximum dispersion of the power spectrum, thereby enhancing overall electromagnetic compatibility.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135721924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-25DOI: 10.52549/ijeei.v11i3.4463
Ahmad Hafiz Mohd Hashim, Norhafiz Azis, Jasronita Jasni, Mohd Amran Mohd Radzi, Masahiro Kozako, Mohamad Kamarol Mohd Jamil, Zaini Yaakub
Partial discharge (PD) measurements either through electrical or acoustic emission approaches can be subjected to noises that arise from different sources. In this study, the examination on the denoising methods for electrical and acoustic emission PD signal is carried out. The PD was produced through needle-plane electrodes configuration. Once the voltage reached to 30 kV, the electrical and acoustic emission PD signals were recorded and additive white Gaussian noise (AWGN) was introduced. These signals were then denoised using moving average (MA), finite impulse response (FIR) low/high-pass filters, and discrete wavelet transform (DWT) methods. The denoising methods were evaluated through ratio to noise level (RNL), normalized root mean square error (NRMSE) and normalized correlation coefficient (NCC). In addition, the computation times for all denoising methods were also recorded. Based on RNL, NRMSE and NCC indexes, the performances of the denoising methods were analyzed through normalization based on the coefficient of variation (𝐶𝑣). Based on the current study, it is found that DWT performs well to denoise the electrical PD signal based on the RNL and NRMSE 𝐶𝑣 index while MA has a good denoising NCC and computation time 𝐶𝑣 index for acoustic emission PD signal.
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