Pub Date : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151428
Anurag Verma, D. Chaturvedi
Neurological disorders are abnormal behavior of nervous system occurring due to irregular firing of neurons. These disorders cause both physical and psychological imbalance to human being suffering from it and may cause even death in some cases. Few of these disorders are epilepsy, Alzheimer’s disease, dementia, cerebro vascular diseases including stroke, migraine, Parkinson’s disease and many more. This manuscript presents neurological disorder detection using electroencephalogram (EEG) signals with machine learning methods. Here, neurological disorders like epilepsy and Attention deficit/hyperactivity disorder (ADHD) have discussed. These Neurological disorders can be differentiated from normal healthy brain using EEG Signal features and efficient classification methods ANN, SVM, Random Forrest and Ensemble methods etc. Some of the robust features like, RMS value, entropy and wavelet coefficients have been explored. For seizures, epilepsy, and ADHD patients time frequency features like wavelet coefficients are the robust one. One of the databases utilized in this study for epilepsy detection is BONN dataset.
{"title":"Neurological disorder detection using EEG signal processing and Machine Learning","authors":"Anurag Verma, D. Chaturvedi","doi":"10.1109/REEDCON57544.2023.10151428","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151428","url":null,"abstract":"Neurological disorders are abnormal behavior of nervous system occurring due to irregular firing of neurons. These disorders cause both physical and psychological imbalance to human being suffering from it and may cause even death in some cases. Few of these disorders are epilepsy, Alzheimer’s disease, dementia, cerebro vascular diseases including stroke, migraine, Parkinson’s disease and many more. This manuscript presents neurological disorder detection using electroencephalogram (EEG) signals with machine learning methods. Here, neurological disorders like epilepsy and Attention deficit/hyperactivity disorder (ADHD) have discussed. These Neurological disorders can be differentiated from normal healthy brain using EEG Signal features and efficient classification methods ANN, SVM, Random Forrest and Ensemble methods etc. Some of the robust features like, RMS value, entropy and wavelet coefficients have been explored. For seizures, epilepsy, and ADHD patients time frequency features like wavelet coefficients are the robust one. One of the databases utilized in this study for epilepsy detection is BONN dataset.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116709647","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151143
Ankitta Bhatt, S. Loan
This work focuses on the design and simulation of a differential Colpitts voltage controlled oscillator (VCO) in integer and fractional domains. The proposed VCO employs n and p-type metal oxide semiconductor field effect transistor (MOSFET) coupling along with injected orthogonal signals at base. The designing uses 32nm MOS technology node at an operating voltage of 1 volt. Both integer order capacitors and the fractional order based pseudo-capacitors have been used for designing the VCOs. The comparative analysis of the key performance measuring parameters has been done using HSPICE. It has been observed that the fractional order circuitry provides much greater phase and frequency control due to an additional parameter, fractional order α, which is not possible in an integer order circuitry. The simulations have shown that increasing α decreases the oscillation frequency of the VCO. At α=0.4, the oscillation frequency is 13.33 GHz and it decreases to 12,6 GHz for α=0.81. However, the fractional order VCO circuit results in more power consumption than the integer order, which is its biggest limitation.
{"title":"Fractional Order Voltage Controlled Oscillator with Injected Orthogonal Signals at Base: Design and Simulation","authors":"Ankitta Bhatt, S. Loan","doi":"10.1109/REEDCON57544.2023.10151143","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151143","url":null,"abstract":"This work focuses on the design and simulation of a differential Colpitts voltage controlled oscillator (VCO) in integer and fractional domains. The proposed VCO employs n and p-type metal oxide semiconductor field effect transistor (MOSFET) coupling along with injected orthogonal signals at base. The designing uses 32nm MOS technology node at an operating voltage of 1 volt. Both integer order capacitors and the fractional order based pseudo-capacitors have been used for designing the VCOs. The comparative analysis of the key performance measuring parameters has been done using HSPICE. It has been observed that the fractional order circuitry provides much greater phase and frequency control due to an additional parameter, fractional order α, which is not possible in an integer order circuitry. The simulations have shown that increasing α decreases the oscillation frequency of the VCO. At α=0.4, the oscillation frequency is 13.33 GHz and it decreases to 12,6 GHz for α=0.81. However, the fractional order VCO circuit results in more power consumption than the integer order, which is its biggest limitation.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114294632","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10150564
Md Sarfraz Khusroo, Anwar Ullah Khan, Tarikul Isalm
This paper deals with developing an interfacing circuit for measuring A.C. powers (active, reactive, and apparent power), power factors, and load parameters (resistance, inductance) in a smart grid. The reactive power measurement is an important requirement with the interconnection of various renewable energy sources to the grid. In order to determine the actual value of active and reactive power, the average value quadrature and in-phase components of output are required. Based on the proposed measurement scheme, we obtain an average value of two voltage signals, quadrature and in-phase, which are proportional to the active and reactive power of the system, separately and independently. Mathematical proofs back the proposed method, and the simulation results obtained from LTspice software precisely verify the proposed technique. Experiments were conducted for different inductive loads for the measurement of various parameters. Simulation results show that the maximum error of reactive power measurement is below 1.5%. The major contributions of the proposed method are frequency invariance, cost effective, easy working, least component count, quick response (within a power cycle), and online measurement features.
{"title":"An Accurate Circuit for the Measurements of Multiple Smart Grid Parameters","authors":"Md Sarfraz Khusroo, Anwar Ullah Khan, Tarikul Isalm","doi":"10.1109/REEDCON57544.2023.10150564","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10150564","url":null,"abstract":"This paper deals with developing an interfacing circuit for measuring A.C. powers (active, reactive, and apparent power), power factors, and load parameters (resistance, inductance) in a smart grid. The reactive power measurement is an important requirement with the interconnection of various renewable energy sources to the grid. In order to determine the actual value of active and reactive power, the average value quadrature and in-phase components of output are required. Based on the proposed measurement scheme, we obtain an average value of two voltage signals, quadrature and in-phase, which are proportional to the active and reactive power of the system, separately and independently. Mathematical proofs back the proposed method, and the simulation results obtained from LTspice software precisely verify the proposed technique. Experiments were conducted for different inductive loads for the measurement of various parameters. Simulation results show that the maximum error of reactive power measurement is below 1.5%. The major contributions of the proposed method are frequency invariance, cost effective, easy working, least component count, quick response (within a power cycle), and online measurement features.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127751066","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10150680
B. V. Kumar, Aneesa Farhan M A
There is an enormous growth in the popularity of electric vehicles (EVs), which has brought forth a considerable rise in the installation of infrastructure and the development of electric vehicle charging stations (EVCSs). Thus, it is quite impossible to ignore the detrimental impact of increased EVCSs loads on the distribution system. The power distribution system parameters like voltage and current will be gravely impacted by a significant number of EVCSs. According to an existing investigation, poorly planned EV charging stations will interfere with the power system ability to operate efficiently. The implementation of EVs is expected to have a variety of positive effects, one of which is that they are a good choice for lowering transportation-related emissions. This paper reviews the impact of EV charging stations on the power distribution network. Additionally, the paper mentions mitigating the impact using better technical support for the development of EVCSs.
{"title":"A Review of Technical Impact of Electrical Vehicle Charging Stations on Distribution Grid","authors":"B. V. Kumar, Aneesa Farhan M A","doi":"10.1109/REEDCON57544.2023.10150680","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10150680","url":null,"abstract":"There is an enormous growth in the popularity of electric vehicles (EVs), which has brought forth a considerable rise in the installation of infrastructure and the development of electric vehicle charging stations (EVCSs). Thus, it is quite impossible to ignore the detrimental impact of increased EVCSs loads on the distribution system. The power distribution system parameters like voltage and current will be gravely impacted by a significant number of EVCSs. According to an existing investigation, poorly planned EV charging stations will interfere with the power system ability to operate efficiently. The implementation of EVs is expected to have a variety of positive effects, one of which is that they are a good choice for lowering transportation-related emissions. This paper reviews the impact of EV charging stations on the power distribution network. Additionally, the paper mentions mitigating the impact using better technical support for the development of EVCSs.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"30 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132653902","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151069
S. Sarwar, S. Jabin
Cone-beam computed tomography (CBCT) is a popular imaging modality in dentistry for diagnosing and planning treatment for a variety of oral diseases with the ability to produce detailed, three-dimensional images of the teeth, jawbones, and surrounding structures. CBCT imaging has emerged as an essential diagnostic tool in dentistry. CBCT imaging has seen significant improvements in terms of its diagnostic value, as well as its accuracy and efficiency, with the most recent development of artificial intelligence (AI) techniques. This paper reviews recent AI trends and practices in dental CBCT imaging. AI has been used for lesion detection, malocclusion classification, measurement of buccal bone thickness, and classification and segmentation of teeth, alveolar bones, mandibles, landmarks, contours, and pharyngeal airways using CBCT images. Mainly machine learning algorithms, deep learning algorithms, and super-resolution techniques are used for these tasks. This review focuses on the potential of AI techniques to transform CBCT imaging in dentistry, which would improve both diagnosis and treatment planning. Finally, we discuss the challenges and limitations of artificial intelligence in dentistry and CBCT imaging.
{"title":"AI Techniques for Cone Beam Computed Tomography in Dentistry: Trends and Practices","authors":"S. Sarwar, S. Jabin","doi":"10.1109/REEDCON57544.2023.10151069","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151069","url":null,"abstract":"Cone-beam computed tomography (CBCT) is a popular imaging modality in dentistry for diagnosing and planning treatment for a variety of oral diseases with the ability to produce detailed, three-dimensional images of the teeth, jawbones, and surrounding structures. CBCT imaging has emerged as an essential diagnostic tool in dentistry. CBCT imaging has seen significant improvements in terms of its diagnostic value, as well as its accuracy and efficiency, with the most recent development of artificial intelligence (AI) techniques. This paper reviews recent AI trends and practices in dental CBCT imaging. AI has been used for lesion detection, malocclusion classification, measurement of buccal bone thickness, and classification and segmentation of teeth, alveolar bones, mandibles, landmarks, contours, and pharyngeal airways using CBCT images. Mainly machine learning algorithms, deep learning algorithms, and super-resolution techniques are used for these tasks. This review focuses on the potential of AI techniques to transform CBCT imaging in dentistry, which would improve both diagnosis and treatment planning. Finally, we discuss the challenges and limitations of artificial intelligence in dentistry and CBCT imaging.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131641248","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151241
Uzma Salmaz, T. Islam
Capacitive sensors are easy to fabricate, compact in size, economical, and quite efficient in the detection of impurities and adulteration in fluids (like milk, fruit, vegetable juices, etc.) and other consumable food items. The change in the dielectric constant of consumable fluids due to the mixing of adulterants and preservatives can be utilized to detect the extent of adulteration and hence its quality. The change in a dielectric property of a fluid can be reflected in terms of the change in capacitance values utilizing these highly economical and compact capacitive sensors. In this work, a simulation of parallel plate capacitive sensors of different sizes and configurations using copper material is done. Also, cross capacitive sensor with brass material is simulated in various shapes of electrodes with different dimensions. The shift in the base value of capacitances due to a milk sample drop is acquired for the sensors to comment on their sensitivity. It is found that for the cross-capacitive sensor if the dimension is close to the size of the drop the maximum change in capacitance is obtained. For the parallel plate sensor by changing the size of the electrodes the base value and sensitivity change to an extent till the optimum value of dimensions for which sensitivity is maximum.
{"title":"Design and Simulation of Capacitive Sensors of Various Geometries for Drop based Quality Analysis of Beverages","authors":"Uzma Salmaz, T. Islam","doi":"10.1109/REEDCON57544.2023.10151241","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151241","url":null,"abstract":"Capacitive sensors are easy to fabricate, compact in size, economical, and quite efficient in the detection of impurities and adulteration in fluids (like milk, fruit, vegetable juices, etc.) and other consumable food items. The change in the dielectric constant of consumable fluids due to the mixing of adulterants and preservatives can be utilized to detect the extent of adulteration and hence its quality. The change in a dielectric property of a fluid can be reflected in terms of the change in capacitance values utilizing these highly economical and compact capacitive sensors. In this work, a simulation of parallel plate capacitive sensors of different sizes and configurations using copper material is done. Also, cross capacitive sensor with brass material is simulated in various shapes of electrodes with different dimensions. The shift in the base value of capacitances due to a milk sample drop is acquired for the sensors to comment on their sensitivity. It is found that for the cross-capacitive sensor if the dimension is close to the size of the drop the maximum change in capacitance is obtained. For the parallel plate sensor by changing the size of the electrodes the base value and sensitivity change to an extent till the optimum value of dimensions for which sensitivity is maximum.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133291270","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10150567
Paulami Purkayastha, M. Choudhry, Manjeet Kumar
This Image-Dehazing paper proposes to combine the Multi-Scale Fusion technique with the Retinex Algorithm. The paper proposes to extract reflectance matrices and incorporate them into the multi-scale fusion algorithm. The technique proposed aims to reduce the halo effect observed in image-dehazing applications and related works for heavily hazy images. Moreover, an improvement in the quality of the output using the proposed novel algorithm is observed. Quantitative, as well as a visual display of results, using the DENSE HAZE dataset, give an accurate interpretation of the effectiveness of the proposed work. The best value of Structural Similarity Index (SSIM) obtained is 0.9128 which shows a 62% increase in image quality as compared to average SSIM values of previously known methods. The Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) show improvement by 78% (TT Playroom) and 95% (Castle) respectively. To allow analysis with regards to pixel compression that may have resulted during the process, two No Reference Image Quality Metrics have been also computed.
{"title":"A Retinex Prior to Multi-Scale Fusion for Single Image Dehazing","authors":"Paulami Purkayastha, M. Choudhry, Manjeet Kumar","doi":"10.1109/REEDCON57544.2023.10150567","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10150567","url":null,"abstract":"This Image-Dehazing paper proposes to combine the Multi-Scale Fusion technique with the Retinex Algorithm. The paper proposes to extract reflectance matrices and incorporate them into the multi-scale fusion algorithm. The technique proposed aims to reduce the halo effect observed in image-dehazing applications and related works for heavily hazy images. Moreover, an improvement in the quality of the output using the proposed novel algorithm is observed. Quantitative, as well as a visual display of results, using the DENSE HAZE dataset, give an accurate interpretation of the effectiveness of the proposed work. The best value of Structural Similarity Index (SSIM) obtained is 0.9128 which shows a 62% increase in image quality as compared to average SSIM values of previously known methods. The Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) show improvement by 78% (TT Playroom) and 95% (Castle) respectively. To allow analysis with regards to pixel compression that may have resulted during the process, two No Reference Image Quality Metrics have been also computed.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133786327","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151398
Umair Yousuf, Sambhavi, Abdul Haq Nalband, Mohammed Riyaz Ahmed
Next-generation wireless networks’ attractive use cases call for more extensive coverage and highly dependable connectivity. A promising candidate that considerably helps to fulfil these requirements is beamforming. In massive Multiple-Input-Multiple-Output (MIMO) systems, the conventional digital beamforming method results in significant costs and hardware complexity. By using fewer RF chains than the conventional digital beamforming method, hybrid beamforming lowers the hardware needed. However, due to the restrictions on hardware consumption, it is difficult to arrive at the open optimal solution for joint optimization problems. We suggest a hybrid beamformer that learns to maximize spectral efficiency using deep learning as its foundation. To achieve the optimal beamforming weights, the channel state information (CSI) is supplied into the deep learning model. Both perfect and imperfect CSI are used to validate the proposed hybrid beamforming scheme. Simulation results reveal that the proposed method outperforms the current statistical approaches while lowering cost and hardware complexity. It is also more robust to poor CSI.
{"title":"Deep Learning Framework for Spectral Efficient Intelligent Hybrid Beamforming","authors":"Umair Yousuf, Sambhavi, Abdul Haq Nalband, Mohammed Riyaz Ahmed","doi":"10.1109/REEDCON57544.2023.10151398","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151398","url":null,"abstract":"Next-generation wireless networks’ attractive use cases call for more extensive coverage and highly dependable connectivity. A promising candidate that considerably helps to fulfil these requirements is beamforming. In massive Multiple-Input-Multiple-Output (MIMO) systems, the conventional digital beamforming method results in significant costs and hardware complexity. By using fewer RF chains than the conventional digital beamforming method, hybrid beamforming lowers the hardware needed. However, due to the restrictions on hardware consumption, it is difficult to arrive at the open optimal solution for joint optimization problems. We suggest a hybrid beamformer that learns to maximize spectral efficiency using deep learning as its foundation. To achieve the optimal beamforming weights, the channel state information (CSI) is supplied into the deep learning model. Both perfect and imperfect CSI are used to validate the proposed hybrid beamforming scheme. Simulation results reveal that the proposed method outperforms the current statistical approaches while lowering cost and hardware complexity. It is also more robust to poor CSI.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132149747","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10150598
Ahmad Bin Afzal, Fahad Iqbal, Y. Rafat, Abdullah Bin Afzal
In light of recent population expansion and increased industrialization, there is a substantial increase in demand for electrical energy. Due to greenhouse gas emissions, depleting fossil fuels, and high lifecycle costs, traditional energy resources have proved to be a hurdle in the path of development. Given their independence from fossil fuels, increased dependability, and zero carbon emissions, microgrids powered by renewable energy sources stand out as being among the most environmentally friendly ways to meet future energy demands. To achieve the seventh Sustainable Development Goal by 2030, academic and research institutions must be at the forefront of research and development efforts on the transition to sustainable energy. In this paper, we have explored the optimization and implementation of institutional-based sustainable microgrids based on aspects like cost analysis, carbon emission, and the availability of energy resources. To fulfill the technical and emission limits as well as to obtain the lowest investment and operating costs, the ideal size of Hybrid Renewable Energy Systems (HREs) equipment should be chosen. Hybrid Optimization of Multiple Energy Resources (HOMER) software is one of the most effective tools for modelling and optimization. NASA's (National Aero Space Agency) solar and wind data is used to obtain metrological information, such as solar irradiance and wind speed.
{"title":"Design and Optimization of a Campus Microgrid using the HOMER Simulator","authors":"Ahmad Bin Afzal, Fahad Iqbal, Y. Rafat, Abdullah Bin Afzal","doi":"10.1109/REEDCON57544.2023.10150598","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10150598","url":null,"abstract":"In light of recent population expansion and increased industrialization, there is a substantial increase in demand for electrical energy. Due to greenhouse gas emissions, depleting fossil fuels, and high lifecycle costs, traditional energy resources have proved to be a hurdle in the path of development. Given their independence from fossil fuels, increased dependability, and zero carbon emissions, microgrids powered by renewable energy sources stand out as being among the most environmentally friendly ways to meet future energy demands. To achieve the seventh Sustainable Development Goal by 2030, academic and research institutions must be at the forefront of research and development efforts on the transition to sustainable energy. In this paper, we have explored the optimization and implementation of institutional-based sustainable microgrids based on aspects like cost analysis, carbon emission, and the availability of energy resources. To fulfill the technical and emission limits as well as to obtain the lowest investment and operating costs, the ideal size of Hybrid Renewable Energy Systems (HREs) equipment should be chosen. Hybrid Optimization of Multiple Energy Resources (HOMER) software is one of the most effective tools for modelling and optimization. NASA's (National Aero Space Agency) solar and wind data is used to obtain metrological information, such as solar irradiance and wind speed.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116219376","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151114
Rupika Gandotra, Kirti Pal
The Distributed Generator (DG) unit nowadays have become an integral part of any power system network. For the effective management of power flowing in a network, the size and position of the DG unit play the important role, but DG integration on the other side, also affects the operating characteristics of the system such as losses may be increased or decreased, variation in voltage and increases the cost for consumers or generators. To eliminate such effects, it is proposed that DG units can be integrated with FACTS devices, as these devices provide more reliability to the network. On the other side, load demand is also increasing day by day therefore, to fulfil the demand it is required to make use of these DG units at various locations. In this paper, an analytical approach with optimal power flow (OPF) method is used in MATLAB environment to identify the optimal location of DG and FACTS devices in maximum loading condition.
{"title":"Optimal location of DG and FACTS devices under Maximum loading Capacity","authors":"Rupika Gandotra, Kirti Pal","doi":"10.1109/REEDCON57544.2023.10151114","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151114","url":null,"abstract":"The Distributed Generator (DG) unit nowadays have become an integral part of any power system network. For the effective management of power flowing in a network, the size and position of the DG unit play the important role, but DG integration on the other side, also affects the operating characteristics of the system such as losses may be increased or decreased, variation in voltage and increases the cost for consumers or generators. To eliminate such effects, it is proposed that DG units can be integrated with FACTS devices, as these devices provide more reliability to the network. On the other side, load demand is also increasing day by day therefore, to fulfil the demand it is required to make use of these DG units at various locations. In this paper, an analytical approach with optimal power flow (OPF) method is used in MATLAB environment to identify the optimal location of DG and FACTS devices in maximum loading condition.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115165244","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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