Journal of Electrical Systems and Information Technology最新文献

{"title":"A novel ANFIS-controlled customized UPQC device for power quality enhancement","authors":"S. Srimatha, Balasubbareddy Mallala, J. Upendar","doi":"10.1186/s43067-023-00121-1","DOIUrl":"https://doi.org/10.1186/s43067-023-00121-1","url":null,"abstract":"Power quality is crucial for the reliable and efficient operation of domestic and industrial loads. Nonlinear power electronic converter loads have increased in usage, which has led to a decline in both voltage and current quality. To overcome these power quality issues, a universal power quality compensator (UPQC) has been developed and integrated into the power distribution network. The UPQC comprises dual active power filters which are equipped with a DC-link capacitor. The voltage of the DC-link capacitor is controlled by a DC-link voltage controller. However, the conventional PI controller is not suitable for regulating voltage at a defined level due to inappropriate gain values. In this work, we propose an intelligent adaptive neuro-fuzzy inference system-based DC voltage controller for customized UPQC devices. The proposed control scheme aims to mitigate the existing power quality challenges by reducing the issues in classical controllers and incorporating intelligence knowledge with subjective decisions. Using the MATLAB/Simulink software tool, we have tested a customized UPQC device controlled by ANFIS for critical operation and performance. The simulation results are presented along with valid comparisons. ","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"87 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509415","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}

{"title":"Object detection using convolutional neural networks and transformer-based models: a review","authors":"Shrishti Shah, Jitendra Tembhurne","doi":"10.1186/s43067-023-00123-z","DOIUrl":"https://doi.org/10.1186/s43067-023-00123-z","url":null,"abstract":"Transformer models are evolving rapidly in standard natural language processing tasks; however, their application is drastically proliferating in computer vision (CV) as well. Transformers are either replacing convolution networks or being used in conjunction with them. This paper aims to differentiate the design of convolutional neural networks (CNNs) built models and models based on transformer, particularly in the domain of object detection. CNNs are designed to capture local spatial patterns through convolutional layers, which is well suited for tasks that involve understanding visual hierarchies and features. However, transformers bring a new paradigm to CV by leveraging self-attention mechanisms, which allows to capture both local and global context in images. Here, we target the various aspects such as basic level of understanding, comparative study, application of attention model, and highlighting tremendous growth along with delivering efficiency are presented effectively for object detection task. The main emphasis of this work is to offer basic understanding of architectures for object detection task and motivates to adopt the same in computer vision tasks. In addition, this paper highlights the evolution of transformer-based models in object detection and their growing importance in the field of computer vision, we also identified the open research direction in the same field.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"86 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509426","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}

{"title":"Analysis of grounding system transient with consideration of soil ionization and mutual coupling effect","authors":"Kaushal Pratap Sengar, Kandasamy Chandrasekaran","doi":"10.1186/s43067-023-00120-2","DOIUrl":"https://doi.org/10.1186/s43067-023-00120-2","url":null,"abstract":"Abstract The grounding system of an electrical substation provides safety to both operator and equipment. The lightning current waveform has a major influence on the dynamic performance of ground electrodes. If lightning current is large enough, soil will be ionized, which will have a great effect on the transient performance of the grounding system. Many numerical models have been developed to evaluate the transient performance of grounding system, still it is a challenging task to analyze the impulse behavior of grounding system efficiently. In this paper, the transient behavior of grounding systems with consideration of nonlinear and dynamic effects of soil ionization is presented. Grounding systems such as vertical rod, horizontal conductor and grounding grid buried in the homogeneous soil are considered as case study. Proposed model is the combination of circuit theory approach and method of moment with consideration of frequency-dependent impedance and mutual coupling effect between the conductor segments. Self and mutual component of inductance and resistance are considered in the analysis. Initially, transient performance of grounding system is analyzed for single vertical rod, horizontal and square grounding electrodes, further it is extended for vertical rod (more than one), horizontal conductor and grounding grids with different configurations. The analysis of grounding system with the consideration of mutual coupling effect is compared with devoid of mutual coupling, and differences between them are evaluated. For validating the developed method, simulated results are compared with the results reported in the literature, and good agreements are found. Analysis shows that the grounding impulse impedance decreased with increasing the magnitude of injected current. Developed method will help to improve the modeling of simple as well as complex grounding system buried in homogeneous soil.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"7 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134992131","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}

{"title":"The effect of electricity consumption determinants in household load forecasting models","authors":"Hussein A. Bakiri, Hadija Mbembati","doi":"10.1186/s43067-023-00122-0","DOIUrl":"https://doi.org/10.1186/s43067-023-00122-0","url":null,"abstract":"Abstract Usually, household electricity consumption fluctuates, often driven by several electrical consumption determinants such as income, household size, and price. Recently, research studies on the investigation of predictor variables in household electricity consumption have increased especially in the developing and newly industrialized countries. However, the studies just focus on identifying the predictor variables of household electricity consumption that influence load forecasting models. In Tanzania, for instance, scholars found that using the “income” determinant improves the performance of a forecasting model. The scholars suggest without any empirical bases that adding more predictor variables would have improved the accuracy of the model. This study aims to analyze the effect of the number of predictor variables on household load forecasting performance based on Tanzania’s data. Nonlinear regression based on a Weibull function and multivariate adaptive regression splines approaches are used for this purpose. Our findings indicate that income, household size, and number of appliances are common predictor variables of household consumption in developing countries. The measured forecasting root-mean-square error (RMSE) when using income, household size, and the number of appliances is 0.8244, 1.2314, and 0.9868, respectively. Finally, we forecasted load using all three determinants and the RMSE dropped to 0.7031. Having obtained the smaller value of RMSE when all predictors are used reveals that the inclusion of all three predictor variables in load forecasting leads to a significant decrease in RMSE by 14.73%. Therefore, the study recommends using multiple predictor variables in load forecasting models to increase accuracy.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"45 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136346383","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}

{"title":"The impact of image augmentation techniques of MRI patients in deep transfer learning networks for brain tumor detection","authors":"Peshraw Ahmed Abdalla, Bashdar Abdalrahman Mohammed, Ari M. Saeed","doi":"10.1186/s43067-023-00119-9","DOIUrl":"https://doi.org/10.1186/s43067-023-00119-9","url":null,"abstract":"Abstract The exponential growth of deep learning networks has enabled us to handle difficult tasks, even in the complex field of medicine. Nevertheless, for these models to be extremely generalizable and perform well, they need to be applied to a vast corpus of data. In order to train transfer learning networks with limited datasets, data augmentation techniques are frequently used due to the difficulties in getting data. The use of these methods is crucial in the medical industry in order to enhance the number of cancer-related magnetic resonance imaging pathology scans. This study evaluates the results of data augmentation methods on three deep transfer learning networks, such as InceptionV3, VGG16, and DenseNet169, for brain tumor identification. To demonstrate how data augmentation approaches affect the performance of the models, networks were trained both before and after the application of these methods. The outcomes revealed that the image augmentation strategies have a big impact on the networks before and after using techniques, such as the accuracy of VGG16 is 77.33% enhanced to 96.88%, and InceptionV3 changed from 86.66 to 98.44%, and DenseNet169 changed from 85.33 to 96.88% the accuracy percentage increase of the models are 19.55%, 11.78%, and 11.55%, respectively.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"81 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135391041","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}

{"title":"Application of bidirectional LSTM deep learning technique for sentiment analysis of COVID-19 tweets: post-COVID vaccination era","authors":"Oluwatobi Noah Akande, Morolake Oladayo Lawrence, Peter Ogedebe","doi":"10.1186/s43067-023-00118-w","DOIUrl":"https://doi.org/10.1186/s43067-023-00118-w","url":null,"abstract":"Abstract Background Social media platforms, especially Twitter, have turned out to be a major source of data repositories. They have become a platform that citizens can use to voice their concerns about issues that affect them. Most importantly, during the COVID-19 era, the platform was greatly used by governments and health organizations to sensitize people about the safety guidelines that they must adhere to so as to remain safe during the pandemic. As expected, people also used Twitter and other social media platforms to voice their opinions about how governments are handling the COVID-19 pandemic outbreak. Governments and organizations could, therefore, use these social media as a feedback mechanism that can help them know the view of the citizens about their policies. This could help them in making informed decisions about their policies. Aim The aim of this paper is to explore the use of BiLSTM deep learning technique for sentiment analysis of COVID-19 tweets. Methodology The study retrieved 197,327 tweets from the Nigeria Twitter domain using #COVID or #COVID-19 hashtags as keywords. The dataset was retrieved within the 1st month of COVID-19 vaccination in Nigeria, i.e., March 15–June 15, 2021. BiLSTM deep learning technique was trained using 789,306 sentiment annotated tweets obtained from Kaggle Sentiment140 tweet datasets. The preprocessed case study tweets were then used to evaluate the proposed model. Also, a precision of 78.26% and a recall value of 78.27% were also obtained. Results With an accuracy of 78.29%, 98,545 (49.93%) positive sentiments and 98,782 negative sentiments (50.06%) were recorded. Also, a precision of 78.26% and a recall value of 78.27% were also obtained. However, the presence of outliers which are tweets not related to COVID but which used the hashtag was observed. Conclusion This study has revealed the strength of BiLSTM deep learning technique for sentiment analysis. The results obtained revealed an almost balanced sentiments toward the pandemic with 49.93% positive disposition to the pandemic as compared to 50.06% negative disposition. This showed affirmed the impact of COVID vaccine in dousing citizen’s tension when it was made available for public use. However, the presence of outliers in the classified tweets could be a pointer to the reason why aspect-based sentiment analysis could be preferred to sentence-based sentiment analysis.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"42 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135584775","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}

{"title":"Techniques of infrared thermography for condition monitoring of electrical power equipment","authors":"Ezechukwu Kalu Ukiwe, Steve A. Adeshina, Jacob Tsado","doi":"10.1186/s43067-023-00115-z","DOIUrl":"https://doi.org/10.1186/s43067-023-00115-z","url":null,"abstract":"Abstract The application of computer vision continues to widen with advancement in technology. Imaging systems which provide necessary inputs to the computer-vision-based models can come in various ways. Such as X-ray images, Computed Tomography (CT) scan images, and Infrared (IR) images. This paper is a review of different application areas of infrared thermography (IRT) for monitoring the status of electrical power equipment. It summarizes in tabular form recent research and relevant works within the field of condition monitoring of power assets. A general review of the application of IRT in power devices was undertaken before a specific review of selected works based on IRT for important electrical power equipment with a tabular review of possible causes of hotspots using photovoltaic installation as a reference. Results of previous works were presented with highlights on performance metrics used and accuracies achieved. Emphasis where made on the future potential of IRT and some associated techniques. The work saw that heat production within systems during operation is an important characteristic that enables IRT to become applicable for monitoring diverse physical systems, most importantly power systems. The high cost of high-definition, and long-range IR cameras limits the wide adoption of the technology for its potential applications for monitoring power installations. The work recommends future research in the development of affordable IR imaging systems with advanced features for condition monitoring of physical systems such as power installations.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135616838","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}

{"title":"Maximum power point tracking of a partially shaded solar photovoltaic system using a modified firefly algorithm-based controller","authors":"Idoko Sunday John, Abubakar Abdulkarim, Gbenga A. Olarinoye","doi":"10.1186/s43067-023-00114-0","DOIUrl":"https://doi.org/10.1186/s43067-023-00114-0","url":null,"abstract":"Abstract Solar photovoltaic (PV) system is one of the most promising power systems based on renewable energy sources, with several advantages compared to others. However, solar PV systems have a challenge of low conversion efficiency because most of the irradiances of the sun, which are channelled to the PV panels, are not fully utilized for power consumption. A more challenging situation of the system occurs when some of its panels are obstructed from full reception of the solar irradiance, a case referred to as partial shading conditions (PSC) in solar PV systems. This leads to the generation of multiple, unequal power peaks in the system, from which the one with the highest power must be tracked for optimum utilization of the system. To this regard, this work presents a modified firefly algorithm-based controller, tied operationally with a DC–DC boost converter. A model was developed and simulated on MATLAB, for tracking the maximum power point of the system, both at constant solar irradiance and at PSC.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135731474","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}

{"title":"Dielectric materials development using bio-waste: a review","authors":"Norazwina Zainol, Nurfarhana Mustafa, Nor Hazwani Aziz, Azahani Natasha Azman, Mohamad Shaiful Abdul Karim","doi":"10.1186/s43067-023-00117-x","DOIUrl":"https://doi.org/10.1186/s43067-023-00117-x","url":null,"abstract":"Abstract The increasing global demand for food due to population expansion has led to the intensification of the agricultural sector. However, this escalation in agricultural production has brought together undesirable consequences as more waste is produced, leading to growing environmental concerns as proper disposal of these wastes is necessary. Valorization of these bio-wastes into dielectric materials offers a promising solution to conventional, non-renewable, yet costly materials. Comprehensive documentation on the development of these wastes into dielectric materials is then necessary to understand their dielectric properties and how these materials behave with the treatments during the fabrication process. This review focuses on the state-of-the-art development of dielectric materials derived from bio-wastes and agricultural waste, which are beneficial for waste management and materials engineering applications. The elemental composition of the waste materials is also discussed in this review to understand its relationship with the dielectric constant. Moreover, the fabrication process for several waste materials into dielectric materials has been provided and compared. This review provides comprehensive information on green materials in the materials engineering industries and can assist in novel studies. It is foreseen that bio-wastes and agricultural wastes can be renewable, sustainable, and low-cost resources for deriving dielectric materials in light of their ease of accessibility.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135667321","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}

{"title":"Characterization of the coding gain for M-PSK with conjoint signals under correlated Nakagami-m fading channels","authors":"Edwin Omosa, Peter Akuon, Hongjun Xu, Vitalis Oduol","doi":"10.1186/s43067-023-00113-1","DOIUrl":"https://doi.org/10.1186/s43067-023-00113-1","url":null,"abstract":"Abstract In the paper, a comprehensive analysis is presented on the conjoint coding gain achieved in arbitrarily correlated M-ary phase-shift keying (M-PSK) signals in a spatial diversity system. The system operates over Nakagami-m fading channels and is affected by additive white Gaussian noise (AWGN). The proposed signal synthesizer primarily induces an even phase shift in the output signals. To enhance the coding gain, an orthogonal transformation matrix is introduced, which effectively preserves energy and operates independently of the channel correlation matrix, making it blind to signal information measurements. The main objective is to generate additional copies of conjoint signals from the received signals, simulating the scenario as if there were more antennas deployed. To evaluate the system’s performance in terms of symbol error rate (SER), an analytical framework is developed and its accuracy is validated through Monte-Carlo simulations. Moreover, extensive tests are conducted to explore the impact of different antenna configurations and fading severity levels. The fading severity, denoted as ’m’, is varied in the experiments, considering values of 0.5, 1, 3, and 7. Additionally, the measurements incorporate varying antenna spacing values relative to the signal carrier wavelength, specifically 0.1, 0.2, and 0.5. The results obtained from the experiments demonstrate that the coding gains achieved depend on the value of ’m’, with higher gains observed for lower ’m’ values. However, as ’m’ increases, the coding gains become negligible.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209210","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}