International Journal of Electrical and Electronics Research最新文献

{"title":"Simulation and Analysis of Optimal Power Injection System Based on Intelligent Controller","authors":"Abdullah Sami Assaf, S. Kurnaz","doi":"10.37391/ijeer.120140","DOIUrl":"https://doi.org/10.37391/ijeer.120140","url":null,"abstract":"Many countries are seeing significant improvements in the fields of building, urban planning, technology, network management, and the need for diverse forms of energy and different generating techniques, as well as the necessity for low and middle distributing voltage in all areas. Depending on the needs of the user, starting needs, capacity, intended usage, waste output, and economic efficiency, many methods are used to generate this energy. To solve the problems brought on by the suggested excessive voltage of the provided system, energy collection devices can be used, and they can be used efficiently with smart grid intelligent control systems. A mathematical model was developed with four main components: simulation, correlation, and evaluation following the solar the program was set of photovoltaic panels solar panels, An Adaptive Neuro-Fuzzy Inference System (ANFIS) controller based on Maximum Power Point Tracking (MPPT), as well as 600-volt electric network, in order to examine and analyze the viability of the proposed network collaboration and storage of electricity in private photovoltaic networks based on solar energy. This phase next looks at the output power impact on the network, as well as the influence of network temperature and coincident radiation. An analysis was conducted to ascertain the impact of these basic limitations on actual use. This section covers the computer simulation of the proposed system. The final section contains the created system's block diagram. The system's input light is transformed into electricity that circulates in this system's power. The main electrical system with a 600-volt capacity can use this energy. The suggested system was evaluated using MATLAB simulation tapes and graphing for each system component, and the simulation outcomes of the entire system were considered.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"29 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372975","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":"Static Synchronous Compensator (STATCOM) and Static VAR Compensators (SVCs) -based neural network controllers for improving power system grid","authors":"Raghad Hameed Ahmed, Ahmed Said Nouri","doi":"10.37391/ijeer.120134","DOIUrl":"https://doi.org/10.37391/ijeer.120134","url":null,"abstract":"The stability of the electrical network is considered a major challenge in the development of energy systems based on various sources. This research provides a comparison of the dynamic performance of FACTS devices such as STATCOM and SVC. These techniques, which are integrated stability devices with a multi-source power system, are used. The neural network technology unit is used to control FACTS devices to enhance the performance of power sources under abnormal and different conditions. Testing is conducted under conditions of three-phase short circuit to ground at bus (3) in the system. MATLAB/Simulink is used for modeling and simulation. The obtained results demonstrate the impact of the control unit based on SVC and STATCOM in reducing system oscillations and improving dynamic system performance during the post-fault period. The comparison confirms the superior dynamic performance and quick fault recovery of the control unit.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388281","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":"Advancements in Steel Surface Defect Detection: An Enhanced YOLOv5 Algorithm with EfficientNet Integration","authors":"Fei Ren, ZiAng Zhang, Jiajie Fei, Hongsheng Li, B. Doma Jr","doi":"10.37391/ijeer.120137","DOIUrl":"https://doi.org/10.37391/ijeer.120137","url":null,"abstract":"Steel surface defect detection is of utmost importance for ensuring product quality, cost reduction, enhanced safety, and heightened customer satisfaction. To address the limitations of traditional steel surface defect detection algorithms, which often yielded singular detection results and suffered from high miss detection rates, we proposed an enhanced Yolov5 steel surface defect detection algorithm. In this approach, this paper employed the EfficientNet network as a replacement for the Yolov5 backbone network. Subsequently, we trained and tested this modified network on a steel surface defect dataset to mitigate the challenges associated with high miss detection rates and underperforming evaluation metrics. Our experimental findings underscored the superiority of the improved algorithm, particularly when compared to Yolov5. This enhanced algorithm exhibited substantial improvements across several key performance metrics, including Precision, Recall, mAP@0.5, parameter count, and pt file size. Noteworthy achievements included a 6.39% increase in Precision for Yolov5-EfficientNetB4, a remarkable 7.75% improvement in Recall for Yolov5-EfficientNetB0, and a 5.57% boost in mAP@0.5 for Yolov5-EfficientNetB6. Additionally, the pt file size for Yolov5-EfficientNetB0 saw a substantial 39.65% reduction, although it was important to note that the inference time for the improved algorithm increased. Among the models, Yolov5-EfficientNetB6 struck the best balance in terms of performance.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388814","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":"Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning Model for IIoT Environment","authors":"E. Anbalagan, Dr P S V Srinivasa Rao, Dr Amarendra Alluri, Dr. D. Nageswari, Dr.R. Kalaivani","doi":"10.37391/ijeer.120131","DOIUrl":"https://doi.org/10.37391/ijeer.120131","url":null,"abstract":"Intrusion Detection in the Industrial Internet of Things (IIoT) concentrations on the security and safety of critical structures and industrial developments. IIoT extends IoT principles to industrial environments, but linked sensors and devices can be deployed for monitoring, automation, and control of manufacturing, energy, and other critical systems. Intrusion detection systems (IDS) in IoT drive to monitor network traffic, device behavior, and system anomalies for detecting and responding to security breaches. These IDS solutions exploit a range of systems comprising signature-based detection, anomaly detection, machine learning (ML), and behavioral analysis, for identifying suspicious actions like device tampering, unauthorized access, data exfiltration, and denial-of-service (DoS) attacks. This study presents an Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning (IID-SBODL) model for IIoT Environment. The IID-SBODL technique initially preprocesses the input data for compatibility. Next, the IID-SBODL technique applies Echo State Network (ESN) model for effectual recognition and classification of the intrusions. Finally, the SBO algorithm optimizes the configuration of the ESN, boosting its capability for precise identification of anomalies and significant security breaches within IIoT networks. By widespread simulation evaluation, the experimental results pointed out that the IID-SBODL technique reaches maximum detection rate and improves the security of the IIoT environment. Through comprehensive experimentation on both UNSW-NB15 and UCI SECOM datasets, the model exhibited exceptional performance, achieving an average accuracy of 99.55% and 98.87%, precision of 98.90% and 98.93%, recall of 98.87% and 98.80%, and F-score of 98.88% and 98.87% for the respective datasets. The IID-SBODL model contributes to the development of robust intrusion detection mechanisms for safeguarding critical industrial processes in the era of interconnected and smart IIoT environments.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388983","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":"Integrating PEVs into Smart Home Energy Management: A Vehicle-to-Home Backup Power Solution with Solar power system","authors":"T. R. S. Reddy, I. Kumaraswamy","doi":"10.37391/ijeer.120135","DOIUrl":"https://doi.org/10.37391/ijeer.120135","url":null,"abstract":"This study focuses on leveraging the capabilities of plug-in electric vehicles (PEVs) to serve as an alternative power supply for suburban demands during disruptions, encompassing backup solutions, particularly in emerging or deprived regions. This initiative is part of an overarching strategy to establish household microgrids. Importantly, this utilization of PEVs for backup power is engineered to have no adverse impact on their primary function as electric vehicles. The proposed Vehicle-to-Home (V2H) system integrates seamlessly with solar photovoltaic (PV) charging. This synergy transforms the entire setup into a nano grid, a self-contained energy ecosystem. In a specific capacity, the plug-in electric vehicle (PEV) operates as a household load, utilizing its battery that gets charged either from solar photovoltaic (PV) systems or grid connections. The pivotal focus, however, remains on maximizing solar energy utilization, thereby reducing dependence on grid-based charging. To achieve this, a multi-faceted approach is adopted. Throughout daylight hours, various charging modes such as slow DC charging, fast DC charging, constant voltage, and constant current charging are employed to tap into and leverage solar energy resources effectively. The primary goals of this initiative include addressing various aspects: reducing household energy expenses, decreasing dependence on the conventional grid, enhancing power supply reliability to meet suburban demands during load shedding and power outages, and optimizing the utilization of solar energy from rooftop photovoltaic arrays. Essentially, this study aims to creatively integrate plug-in electric vehicles (PEVs), solar photovoltaics (PV), and smart grid technologies to improve energy resilience and efficiency in residential settings.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":" 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388485","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":"Hybrid Data Driven Clock Gating and Data Gating Technique for Better Saving Power in ALU RISC-V","authors":"Minh Huan Vo","doi":"10.37391/ijeer.120133","DOIUrl":"https://doi.org/10.37391/ijeer.120133","url":null,"abstract":"The study proposes a hybrid data driven clock gating and data gating technique which is applied to ALU in RISC-V. By doing so, the proposed low power technique can improve the power saving efficiency. The proposed low power technique is compared with various low power techniques such as latch-free based clock gating, latch-based clock gating, single data driven clock gating, and single data gating. The results show that the proposed low power ALU saves 46.67% power consumption compared to original ALU. The proposed ALU also shows better saving power than the latch-free based clock gating, latch-based clock gating, sdata driven clock gating, and data gating from 10.84% to 22.23%. The comparison is also implemented on CPU which consists of memory, ALU and control unit. The proposed low power CPU saves 12.11% at least compared to the original CPU. However, the proposed low power CPU is reduced to 15.1% maximum frequency operation compared to the original CPU. The area overhead of the proposed ALU also increased to 33 LUTS (8.2%) and 2 registers (1.6%) compared to the original ALU.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140389298","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":"Power Transformer Inrush Current Minimization During Energization using ANFIS based Peak Voltage Tracking Approach","authors":"M. Alkhayyat, Faisal Aiwa, Ali Salah Alhfidh, Mohammed Y. Suliman","doi":"10.37391/ijeer.120130","DOIUrl":"https://doi.org/10.37391/ijeer.120130","url":null,"abstract":"Energizing the power transformer at no load causes inrush current flow. The value of this current depends on main three factors, the residual and saturation flux of the transformer core, the rating of the transformer, and the switching instant. Inrush current may decrease the life of the transformer and causes mall function of the protective relays. Many efforts were done for limiting the inrush current using a current limiter or improve the core material to reduce residual flux. Other treating is to control energizing instance. This paper focused on controlling the instant of the transformer energization switch using fuzzy logic inference system. A new technique depends on adaptive seeking the crest of the voltage waveform. By this method there is no need to zero-crossing technique or phase looked loop. At this point, the flux of the core reaches the minimum value. Simulation and laboratory results show the success of this technique in reducing the inrush current. This technique gives the freedom to the operational engineering for energizing the power transformer at any time.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388716","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":"Design of a Deep Learning based Intelligent Receiver for a Wireless Communication System","authors":"Drakshayini M.N., Manjunath R. Kounte, Chaya Ravindra","doi":"10.37391/ijeer.120132","DOIUrl":"https://doi.org/10.37391/ijeer.120132","url":null,"abstract":"In communication systems, deep learning techniques can provide better predictions than model-based methods when the hidden features of the problem are prone to deviating substantially from the formulated assumptions. Severe signal impairments due to multipath fading and higher channel noise levels degrade the performance of conventional receivers. To overcome this, a novel intelligent receiver based on a deep learning network is presented, achieving better performance in terms of reduced bit error rate than a standalone conventional receiver. The experimental result shows that the relative decrement in the symbol error ratio due to the proposed method is about 9 percent compared to the traditional receiver when the Rician channel fading is relatively high.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140389312","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":"Performance Evaluation and Dynamic Characteristics of a Self-Excited Induction Generator for Pico Hydro Power Plants","authors":"K. Krismadinata, Derry Fiandri","doi":"10.37391/ijeer.120129","DOIUrl":"https://doi.org/10.37391/ijeer.120129","url":null,"abstract":"The dynamic performance of an isolated three-phase squirrel cage self-excited induction generator (SEIG) in a Pico Hydro Power Plant (PHPP) is examined in this work. The investigation is carried out with the help of MATLAB/Simulink for mathematical modeling and simulation of the proposed system under various operational situations. The SEIG model, which was created using the steady-state equivalent circuit approach, included the electrical, magnetic, and mechanical components of the SEIG and PHPP. The dynamic behavior of the SEIG is explored under a variety of operating situations. The effects of load variations, speed fluctuations, and other disturbances on the voltage and frequency of the generator are examined. The experiment results were used to validate the simulation results. This research has implications for the design and optimization of PHPP using SEIGs.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140391836","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":"Classification of Lung Cancer in Segmented CT Images Using Pre-Trained Deep Learning Models","authors":"P. Deepa, M. Arulselvi, S. M. Sundaram","doi":"10.37391/ijeer.120122","DOIUrl":"https://doi.org/10.37391/ijeer.120122","url":null,"abstract":"Many Diagnosis systems have been designed and used for diagnosing different types of cancer. Identification of carcinoma at an earlier stage is more important, and it is made possible due to the use of processing of medical images and deep learning techniques. Lung cancer is seen to develop often to be increased, and Computed Tomography (CT) scan images were utilized in the investigation to locate and classify lung cancer and also to determine the severity of cancer. This work is aimed at employing pre-trained deep neural networks for lung cancer classification. A Gaussian-based approach is used to segment CT scan images. This work exploits a transfer learning-based classification method for the chest CT images acquired from Cancer Image Archive and available in the Kaggle platform. The dataset includes lung CT images from the Cancer Image Archive for classifying lung cancer types. Pre-trained models such as VGG, RESNET, and INCEPTION were used to classify segmented chest CT images, and their performance was evaluated using different optimization algorithms.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140391549","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}