Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570999
Willy Stephen Tounsi Fokui, Michael Juma Saulo, L. Ngoo
Photovoltaic (PV) systems are proving to be a promising solution to off-grid electrification in Kenya due to the abundance of the solar resource in the country. As the output of PV systems is not constant owing to the intermittency of the solar resource, standalone PV systems are usually equipped with energy storage systems, mostly battery storage systems (BSS). BSS are faced with the problem of the short lifetime of the batteries due to various factors including excessive discharge. In this paper, a standalone PV system is sized and modelled for a typical household in Nairobi, Kenya using PSCAD/EMTDC. A battery management system (BMS) is designed for the standalone PV system using Proteus Professional. The BMS takes charge of managing the state of charge (SOC) of the battery and supplies the various home appliances accordingly. Simulation results show the effectiveness and flexibility of the proposed BMS.
{"title":"Sizing and Modelling of a Standalone PV System with Battery Management System for a Typical Household in Nairobi, Kenya","authors":"Willy Stephen Tounsi Fokui, Michael Juma Saulo, L. Ngoo","doi":"10.1109/africon51333.2021.9570999","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570999","url":null,"abstract":"Photovoltaic (PV) systems are proving to be a promising solution to off-grid electrification in Kenya due to the abundance of the solar resource in the country. As the output of PV systems is not constant owing to the intermittency of the solar resource, standalone PV systems are usually equipped with energy storage systems, mostly battery storage systems (BSS). BSS are faced with the problem of the short lifetime of the batteries due to various factors including excessive discharge. In this paper, a standalone PV system is sized and modelled for a typical household in Nairobi, Kenya using PSCAD/EMTDC. A battery management system (BMS) is designed for the standalone PV system using Proteus Professional. The BMS takes charge of managing the state of charge (SOC) of the battery and supplies the various home appliances accordingly. Simulation results show the effectiveness and flexibility of the proposed BMS.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"505 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120886659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570909
Aristotelis Charalampous, Andreas Papadopoulos, Stavros Hadjiyiannis, P. Philimis
According to the European Environment agency, water demand across Europe has steadily increased over the past 50 years, partly due to population growth and people moving to cities and towns, especially in densely populated areas. Household use is reported to account for 12% of total water use in Europe. Effective water management practices are being put in place EU-wide, but those that target residential end users are limited to public awareness campaigns, promoting the purchase and use of water-saving devices. Our system aims to bridge the gap between consumers and appliances by accurate and on-time monitoring of household water consumption, at individual appliance level, helping users ease into enduring water-saving practices. Our system’s platform incorporates advanced signal processing methodologies combined with supervised machine learning classifiers to classify water flows, thus identifying residential water appliances with high accuracy. Our experimentation confirms that our models achieve accuracy of ~91% in classifying the four most used household water appliances. This is crucial in assisting end users in reducing their households’ overall water consumption.
{"title":"Towards hydro-informatics modernization with real-time water consumption classification","authors":"Aristotelis Charalampous, Andreas Papadopoulos, Stavros Hadjiyiannis, P. Philimis","doi":"10.1109/africon51333.2021.9570909","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570909","url":null,"abstract":"According to the European Environment agency, water demand across Europe has steadily increased over the past 50 years, partly due to population growth and people moving to cities and towns, especially in densely populated areas. Household use is reported to account for 12% of total water use in Europe. Effective water management practices are being put in place EU-wide, but those that target residential end users are limited to public awareness campaigns, promoting the purchase and use of water-saving devices. Our system aims to bridge the gap between consumers and appliances by accurate and on-time monitoring of household water consumption, at individual appliance level, helping users ease into enduring water-saving practices. Our system’s platform incorporates advanced signal processing methodologies combined with supervised machine learning classifiers to classify water flows, thus identifying residential water appliances with high accuracy. Our experimentation confirms that our models achieve accuracy of ~91% in classifying the four most used household water appliances. This is crucial in assisting end users in reducing their households’ overall water consumption.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"44 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116506939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570858
Tawanda Matereke, Clement N. Nyirenda, Mehrdad Ghaziasgar
This paper presents a detailed evaluation of three spatio-temporal deep learning architectures for crime prediction. These network architectures are as follows: the Spatio Temporal Residual Network (ST-ResNet), the Deep Multi View Spatio Temporal Network (DMVST-Net), and the Spatio Temporal Dynamic Network (STD-Net). The architectures were trained using the Chicago crime data set. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used as performance metrics to evaluate the models. Results show that the STD-Net achieved the best results of the three approaches, with an accuracy of 0.89, RMSE of 0.2870, and MAE of 0.2093. The ST-ResNet and DMVST-Net also showed considerable promise. The ST-ResNet achieved an accuracy of 0.83, RMSE of 0.4033 and an MAE of 0.3278 while the DMVST-Net achieved an accuracy of 0.79, RMSE of 0.4171 and an MAE of 0.3455. Future work will include training these algorithms with crime data, which is augmented with external data such as climate and socioeconomic data. Hyperparameter optimization of these algorithms using techniques, such as evolutionary computation, will also be explored.
{"title":"A Comparative Evaluation of Spatio Temporal Deep Learning Techniques for Crime Prediction","authors":"Tawanda Matereke, Clement N. Nyirenda, Mehrdad Ghaziasgar","doi":"10.1109/africon51333.2021.9570858","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570858","url":null,"abstract":"This paper presents a detailed evaluation of three spatio-temporal deep learning architectures for crime prediction. These network architectures are as follows: the Spatio Temporal Residual Network (ST-ResNet), the Deep Multi View Spatio Temporal Network (DMVST-Net), and the Spatio Temporal Dynamic Network (STD-Net). The architectures were trained using the Chicago crime data set. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used as performance metrics to evaluate the models. Results show that the STD-Net achieved the best results of the three approaches, with an accuracy of 0.89, RMSE of 0.2870, and MAE of 0.2093. The ST-ResNet and DMVST-Net also showed considerable promise. The ST-ResNet achieved an accuracy of 0.83, RMSE of 0.4033 and an MAE of 0.3278 while the DMVST-Net achieved an accuracy of 0.79, RMSE of 0.4171 and an MAE of 0.3455. Future work will include training these algorithms with crime data, which is augmented with external data such as climate and socioeconomic data. Hyperparameter optimization of these algorithms using techniques, such as evolutionary computation, will also be explored.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129434314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570996
O. Ajayi, Reolyn Heymann
Multi-Layered Perceptron is a type of artificial neural networks and obtaining the optimal weights and biases of the model is critical to achieving good performance. In this study, Moth Swarm Algorithm has been proposed to train a Multi-Layered Perceptron neural network by finding the best combination of weights and biases that produce outputs with the least possible Mean Squared Error. The model has been applied for predicting the energy demand of a data centre. The simulations have been conducted using real life data obtained from an anonymous data centre operator in South Africa. The input parameters considered in the model are the ambient temperature, ambient relative humidity, chiller output temperature and CRAC air supply temperature. The performance of the proposed method has been evaluated based on the Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error and accuracy values obtained for the training and testing set. By comparing the results obtained with other models like Moth Flame Optimization, Ant Lion Optimization and Whale Optimization Algorithm, it was found that the Moth Swarm Algorithm-trained Multi-Layered Perceptron outperformed the other models. Further, a Percentage Relative Contribution analysis has been conducted to highlight the level of influence each of the input parameters considered has on the energy demand pattern of the data centre. Analyses show that the ambient temperature has the highest influence of 31.7% on the energy demand of the building.
{"title":"Training a Multi-Layered Perceptron using Moth Swarm Algorithm for Predicting Energy Demand of a Data Centre and Weights-Based Analysis of Input Parameters","authors":"O. Ajayi, Reolyn Heymann","doi":"10.1109/africon51333.2021.9570996","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570996","url":null,"abstract":"Multi-Layered Perceptron is a type of artificial neural networks and obtaining the optimal weights and biases of the model is critical to achieving good performance. In this study, Moth Swarm Algorithm has been proposed to train a Multi-Layered Perceptron neural network by finding the best combination of weights and biases that produce outputs with the least possible Mean Squared Error. The model has been applied for predicting the energy demand of a data centre. The simulations have been conducted using real life data obtained from an anonymous data centre operator in South Africa. The input parameters considered in the model are the ambient temperature, ambient relative humidity, chiller output temperature and CRAC air supply temperature. The performance of the proposed method has been evaluated based on the Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error and accuracy values obtained for the training and testing set. By comparing the results obtained with other models like Moth Flame Optimization, Ant Lion Optimization and Whale Optimization Algorithm, it was found that the Moth Swarm Algorithm-trained Multi-Layered Perceptron outperformed the other models. Further, a Percentage Relative Contribution analysis has been conducted to highlight the level of influence each of the input parameters considered has on the energy demand pattern of the data centre. Analyses show that the ambient temperature has the highest influence of 31.7% on the energy demand of the building.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"528 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130252877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570995
J. Obadha, P. Akuon, Vitalis Oduol Kalecha
In this study we propose a novel communication scheme of conveying additional spatial information through antenna sequences. The additional information is conveyed through fading channels such as Rayleigh fading. The sequence in which the three antennas transmit a symbol over the three time slots is used to convey additional information, over and above that carried by the symbol. We show that in this way, three antennas can be used to convey at least 4 bits in the spatial and signal domain. We present simulated results for both BPSK and 4QAM modulation schemes. Using information theoritic criteria, we derive the analytical expressions of the bit error rate and compare the results with those obtained through Monte Carlo simulations. The theoritical and simulated results closely match each other. We conclude that this method improves spectral efficiency and has a better BER performance compared to the conventional Spatial Modulation (SM).
{"title":"BER Performance of Antenna Sequence Modulation (ASM)","authors":"J. Obadha, P. Akuon, Vitalis Oduol Kalecha","doi":"10.1109/africon51333.2021.9570995","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570995","url":null,"abstract":"In this study we propose a novel communication scheme of conveying additional spatial information through antenna sequences. The additional information is conveyed through fading channels such as Rayleigh fading. The sequence in which the three antennas transmit a symbol over the three time slots is used to convey additional information, over and above that carried by the symbol. We show that in this way, three antennas can be used to convey at least 4 bits in the spatial and signal domain. We present simulated results for both BPSK and 4QAM modulation schemes. Using information theoritic criteria, we derive the analytical expressions of the bit error rate and compare the results with those obtained through Monte Carlo simulations. The theoritical and simulated results closely match each other. We conclude that this method improves spectral efficiency and has a better BER performance compared to the conventional Spatial Modulation (SM).","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134559845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570993
Beatus Mbunda, D. Machuve, S. Mirau
With technology advancement, the application of technology in conglomerate companies is crucial for company performance.Technology utilization in industry in developing countries is a challenge. Often there is a crisis of equipment, goods, and items destruction or loss in warehouses. The enhancement of the warehouse management system for the company helps to utilize resources effectively, thus improving company performance.This study aimed to enhance the management of warehouses through the use of information communication and technology. The study developed mobile applications for customer registration, order management, and stock management. The study also extended the web applications for account management, order management, invoice generation, client registration, and stock management. The study was conducted at AtoZ Textiles Company Limited located in the Kisongo area, Arusha Region.
{"title":"Warehouse Management System Enhancement: A Case Study of ATOZ Textiles Limited.*","authors":"Beatus Mbunda, D. Machuve, S. Mirau","doi":"10.1109/africon51333.2021.9570993","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570993","url":null,"abstract":"With technology advancement, the application of technology in conglomerate companies is crucial for company performance.Technology utilization in industry in developing countries is a challenge. Often there is a crisis of equipment, goods, and items destruction or loss in warehouses. The enhancement of the warehouse management system for the company helps to utilize resources effectively, thus improving company performance.This study aimed to enhance the management of warehouses through the use of information communication and technology. The study developed mobile applications for customer registration, order management, and stock management. The study also extended the web applications for account management, order management, invoice generation, client registration, and stock management. The study was conducted at AtoZ Textiles Company Limited located in the Kisongo area, Arusha Region.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134176980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570965
G. I. Mwandosya
mobile technologies have impacted people’s lives by enhancing the way activities are performed in industries in different sectors including the education sector. The use of mobile devices by teachers and students in Higher Education Institutions (HEIs) have brought up online sharing and exchange of educational materials. This mode of educational communication has influenced (or impacted) the performance of students and teachers specifically in the academic field through innovative teaching and learning. However, in Tanzania little is known on the extent of the use of mobile education tools (METs) for enhancing innovative teaching and learning. This study, therefore, applied the modified technology acceptance model (TAM) to assess the use of METs for innovative teaching and learning in Four HEIs in Tanzania. The study was mainly quantitatively combined with an initial qualitative study whereby nine (9) teachers and twenty-one (21) students were purposefully selected for semi-structured interviews. In total 600 participants from the four HEIs in Dar es Salaam participated in the study. The qualitative data were thematically analyzed and the quantitative data were analyzed using IBM Statistical Package for Social Sciences (SPSS) version 23 descriptive analysis based on means and standard deviations. Findings revealed that electronic learning (e-learning) systems are generally operative in all of the four HEIs as well as learning management systems which have some educational tools embedded for the improvement of exchange and sharing of educational materials between teachers and students. Apart from e-learning systems, the use of METs is not formal so that teachers can use their own educational tools for teaching purposes.
移动技术通过改善包括教育部门在内的不同行业的活动方式，影响了人们的生活。高等教育院校的教师和学生使用流动装置，可以在网上分享和交换教育资料。这种教育交流模式通过创新的教与学，影响(或影响)了学生和教师在学术领域的表现。然而，在坦桑尼亚，人们对使用移动教育工具(METs)来加强创新教学的程度知之甚少。因此，本研究应用改进的技术接受模型(TAM)来评估坦桑尼亚四所高等教育机构在创新教学和学习方面使用METs的情况。本研究主要是定量研究与初步定性研究相结合，有目的地选择了9名教师和21名学生进行半结构化访谈。来自达累斯萨拉姆四所高等教育机构的总共600名参与者参加了这项研究。采用IBM Statistical Package for Social Sciences (SPSS)第23版基于均值和标准差的描述性分析对定性数据进行主题分析，对定量数据进行分析。调查结果显示，所有四间高等教育院校均普遍采用电子学习系统，以及内置教育工具的学习管理系统，以改善师生之间的教材交流和分享。除了电子学习系统外，met的使用并不正式，因此教师可以使用自己的教育工具进行教学。
{"title":"Assessing the use of mobile educational tools for enhancing innovative teaching and learning in higher education institutions in Tanzania","authors":"G. I. Mwandosya","doi":"10.1109/africon51333.2021.9570965","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570965","url":null,"abstract":"mobile technologies have impacted people’s lives by enhancing the way activities are performed in industries in different sectors including the education sector. The use of mobile devices by teachers and students in Higher Education Institutions (HEIs) have brought up online sharing and exchange of educational materials. This mode of educational communication has influenced (or impacted) the performance of students and teachers specifically in the academic field through innovative teaching and learning. However, in Tanzania little is known on the extent of the use of mobile education tools (METs) for enhancing innovative teaching and learning. This study, therefore, applied the modified technology acceptance model (TAM) to assess the use of METs for innovative teaching and learning in Four HEIs in Tanzania. The study was mainly quantitatively combined with an initial qualitative study whereby nine (9) teachers and twenty-one (21) students were purposefully selected for semi-structured interviews. In total 600 participants from the four HEIs in Dar es Salaam participated in the study. The qualitative data were thematically analyzed and the quantitative data were analyzed using IBM Statistical Package for Social Sciences (SPSS) version 23 descriptive analysis based on means and standard deviations. Findings revealed that electronic learning (e-learning) systems are generally operative in all of the four HEIs as well as learning management systems which have some educational tools embedded for the improvement of exchange and sharing of educational materials between teachers and students. Apart from e-learning systems, the use of METs is not formal so that teachers can use their own educational tools for teaching purposes.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"35 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133269167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570879
Kgaogelo Mampa, A. Alonge
One of the most significant commodities of today’s world is energy. Energy usage depends on various factors such as season, day of the week, temperature etc. It is imperative that the distribution, transmission, and generation of electricity is effective while equally producing required results to electricity customers. With an expectation for increasing power outages in South Africa in the nearest future, there is a renewed focused on electricity distribution and consumption. This paper examines the electric load profile at a commercial location in Johannesburg, South Africa, for which the overall dataset (in KWh) is classified into four seasonal regimes: summer, spring, winter, and autumn. Two probabilistic models – normal and lognormal distributions – are applied to investigate the medium-term behaviour of the time series dataset over a period of two years, between 2019 and 2020. Results from this investigation suggest that normal distribution gives a better approximation to the seasonal datasets, except during the spring season. The lognormal distribution is observed to give minimal fitting errors during the spring season. Additionally, the load profile during summer and spring seasons are observed to exhibit similar characteristics, likewise, both autumn and winter seasons are found to exhibit the same trend for the same period.
{"title":"Probabilistic Distributions for Modelling Seasonal Load Profiles of Commercial Areas in South Africa","authors":"Kgaogelo Mampa, A. Alonge","doi":"10.1109/africon51333.2021.9570879","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570879","url":null,"abstract":"One of the most significant commodities of today’s world is energy. Energy usage depends on various factors such as season, day of the week, temperature etc. It is imperative that the distribution, transmission, and generation of electricity is effective while equally producing required results to electricity customers. With an expectation for increasing power outages in South Africa in the nearest future, there is a renewed focused on electricity distribution and consumption. This paper examines the electric load profile at a commercial location in Johannesburg, South Africa, for which the overall dataset (in KWh) is classified into four seasonal regimes: summer, spring, winter, and autumn. Two probabilistic models – normal and lognormal distributions – are applied to investigate the medium-term behaviour of the time series dataset over a period of two years, between 2019 and 2020. Results from this investigation suggest that normal distribution gives a better approximation to the seasonal datasets, except during the spring season. The lognormal distribution is observed to give minimal fitting errors during the spring season. Additionally, the load profile during summer and spring seasons are observed to exhibit similar characteristics, likewise, both autumn and winter seasons are found to exhibit the same trend for the same period.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"53 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130551164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570892
P. Akuon
This paper discusses measurement techniques for the total amount of power injected in an alternating current (AC) power system. It is shown that the power network consisting of reactances and resistances will have covariance elements of voltage and current. A demodulator is used to reduce correlated current and voltage waveforms into singular waveforms and the amount of power is accurately estimated from an analysis of 3-dimensional (3D) complex sinusoids. A validation framework is also presented to show that the total power injected, PT in an AC power network is an algebraic sum function of both the sine and cosine of the power factor (PF) angle, when power is written as VmIm sin(ωt) sin(ωt − θ), so PT = f(cos θ + sin θ). The proposed technique will find use in smart power meters to determine accurate amount of power generation needed, amount of power stored, and amount of power consumed, power system quality and billing rates for different classes of loads.
{"title":"Power Delivered in AC Networks as an Algebraic Sum, P + Q not Phasor Sum, P + jQ when power is expressed as VmIm sin(ωt) sin(ωt − θ)","authors":"P. Akuon","doi":"10.1109/africon51333.2021.9570892","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570892","url":null,"abstract":"This paper discusses measurement techniques for the total amount of power injected in an alternating current (AC) power system. It is shown that the power network consisting of reactances and resistances will have covariance elements of voltage and current. A demodulator is used to reduce correlated current and voltage waveforms into singular waveforms and the amount of power is accurately estimated from an analysis of 3-dimensional (3D) complex sinusoids. A validation framework is also presented to show that the total power injected, PT in an AC power network is an algebraic sum function of both the sine and cosine of the power factor (PF) angle, when power is written as VmIm sin(ωt) sin(ωt − θ), so PT = f(cos θ + sin θ). The proposed technique will find use in smart power meters to determine accurate amount of power generation needed, amount of power stored, and amount of power consumed, power system quality and billing rates for different classes of loads.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129075321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570854
S. Oladejo, S. Ekwe, L. Akinyemi
This paper addresses the dynamic resource allocation in a multi-slice multi-tier multi-domain based network with different network players. The dynamic resource allocation problem is formulated as a maximum utility optimisation problem from multi-player multi-domain perspective. Furthermore, a 3-level hierarchical business model comprising infrastructure providers (InPs), mobile virtual network operators (MVNO), service providers (SP) and slice users is investigated. We propose a multi-tier multi-domain slice user matching game and a distributed backtracking multi-player multi-domain game schemes in solving the transformed maximum utility optimisation problem. We compare the multi-tier multi-tenant multi-domain game scheme with a genetic algorithm (GA) intelligent latency- aware resource allocation scheme (GI-LARE) and, a static slicing resource allocation scheme via Monte Carlo simulation. Our findings reveal that the proposed scheme outperforms these other schemes.
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