<![CDATA[Recent Advances in Electrical & Electronic Engineering (Volume 17 - Issue 5)]]> https://www.eurekaselect.com/journal/152 RSS Feed for Journals | BenthamScience EurekaSelect (+https://www.eurekaselect.com) 2024-03-17 <![CDATA[Recent Advances in Electrical & Electronic Engineering (Volume 17 - Issue 5)]]> https://www.eurekaselect.com/journal/152 <![CDATA[Nonlinear Loads Modelling and Harmonics Analysis: A Review]]>https://www.eurekaselect.com/article/1338962024-03-17 <![CDATA[Bidirectional DC-DC Converter and Improved Electrical Vehicle Dynamic Response Control]]>https://www.eurekaselect.com/article/1334602024-03-17 Background: In automotive applications where bidirectional power flow is necessary to lighten the power system, dual active bridge (DAB) converters are frequently employed. Variations in the required output voltage, erratic input voltage, and shifting loads all have an impact on this converter. As a result, converter performance has to be improved. To increase efficiency, the current stress of the DC-DC converter must be optimised. This paper proposes a control scheme for the coupled inductor bidirectional DC-DC (CIB DC-DC) converter utilising both model predictive control (MPC) and a proportional-integral (PI) controller. The integration of these control techniques aims to enhance the performance and efficiency of the converter.

Methods: The MPC algorithm is employed to predict the converter's future behaviour based on a dynamic model, taking into account system constraints and performance criteria. By optimising the control action over a finite time horizon, the MPC algorithm ensures an optimal response, considering the current state and anticipated changes. Additionally, a PI controller is incorporated to augment the control strategy. The proportional component of the PI controller enables a fast initial response to the error between the desired and actual converter outputs. The integral component eliminates steady-state errors and provides robustness against disturbances, resulting in improved overall system performance.

Results: The proposed control scheme is implemented and evaluated through simulations and experimental tests on a prototype converter. The results demonstrate the effectiveness of the combined MPC and PI controller approach.

Conclusion: The coupled inductor bidirectional DC-DC (CIB DC-DC) converter using MPC can provide precise control of power flow between two voltage domains, enabling efficient bidirectional power transfer. The predictive capabilities of MPC allow it to adapt to varying load conditions and respond quickly to changes, ensuring stable operation and accurate regulation of voltage and current. Overall, the coupled inductor bidirectional DC-DC converter controlled using MPC over PI offers improved performance, efficiency, and flexibility compared to traditional control methods. MPC can handle the complex dynamics response and non-linear characteristics of the converter, making it suitable for bi-directional vehicle charging applications, where precise control and high efficiency can be achieved.

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<![CDATA[Customer Churn Prevention For E-commerce Platforms using Machine Learning-based Business Intelligence]]>https://www.eurekaselect.com/article/1329282024-03-17 Aims & Background: Businesses in the E-commerce sector, especially those in the business- to-consumer segment, are engaged in fierce competition for survival, trying to gain access to their rivals' client bases while keeping current customers from defecting. The cost of acquiring new customers is rising as more competitors join the market with significant upfront expenditures and cutting-edge penetration strategies, making client retention essential for these organizations.

Objectives: The main objective of this research is to detect probable churning customers and prevent churn with temporary retention measures. It's also essential to understand why the customer decided to go away to apply customized win-back strategies.

Methodology: Predictive analysis uses the hybrid classification approach to address the regression and classification issues. The process for forecasting E-commerce customer attrition based on support vector machines is presented in this paper, along with a hybrid recommendation strategy for targeted retention initiatives. You may prevent future customer churn by suggesting reasonable offers or services.

Results: The empirical findings demonstrate a considerable increase in the coverage ratio, hit ratio, lift degree, precision rate, and other metrics using the integrated forecasting model.

Conclusion: To effectively identify separate groups of lost customers and create a customer churn retention strategy, categorize the various lost customer types using the RFM principle.

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<![CDATA[Network Coding with Parallel Path Protection for Multiple Link Failure in WOBAN]]>https://www.eurekaselect.com/article/1338762024-03-17 Background: WOBAN is a high-speed network and hence any kind of failure results in huge data loss. Using the proposed network coding technique with parallel path protection can handle multiple link failures, the performance of the network can be improved.

Aims: This study aims to improve network performance using network coding with parallel path protection routing algorithm (NC-PPR) for multiple link failures in WOBAN.

Objective: We investigated the multiple link failures in WOBAN by the proposed approach namely Coded Path Protection Algorithm, which enhances the survivability of the WOBAN against multiple link failures in the front end and eliminates the need of backup resources.

Method: Extensive simulation is carried out to implement proposed work. A simulation model and code is developed in MATLAB to get the performance enhancement of WOBAN.

Result: We compared the performance of proposed algorithm with existing algorithm. The obtained results show that the proposed algorithm has superior performance than the existing algorithm.

Conclusion: In this paper, a new routing approach, which works in three phases namely path finding, encoding, and decoding, using random linear network coding (RLNC) is introduced to address the survivability issue of the WOBAN. The proposed approach also enhances the network performance in terms of PDR, overhead, and delay.

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<![CDATA[Assessment of Power Quality Enhancement in a Grid-tied PV Network via ANN-based UPQC]]>https://www.eurekaselect.com/article/1339272024-03-17 Background: Microgrid is the recent decade terminology that surpasses the long-run issues associated with the public and utility grids. Among the renewable energy sources, solar PV units have gained greater importance owing to their huge potential availability and laidback operating characteristics on technological grounds. Conversely, it offers pollution-free electricity and perhaps the dependability is volatile in most situations. The literature study accumulates the foresaid setback and presents the fluctuation-less and controlled standard quality of power outputs.

Objective: The aim of this particular research is to propose an assessment of Power Quality enhancement in a Grid-tied photovoltaic (PV) network via ANN-based UPQC. The novel idea behind this proposed approach is the UPQC component which deliberately regulates and controls the power system to achieve higher levels of power quality, ultimately meeting the recent IEEE standards.

Method: This particular research enhances the performances of UPQC employed in the microgrid unit by replacing the traditional PI controller with a multi-layered feed-forward-type ANN controller for the current regulation of the series active filter. Additionally, a training algorithm for the ANN controller is built, trained and simulated via MATLAB/Simulink platform. The ANN-based UPQC is proposed to alleviate the power quality challenges like sag and swell in voltage, harmonic distortion, the time required for voltage compensation, and power factor. Therefore, UPQC is equipped to enrich the standard of power transfer at the point of common coupling inside the power frameworks, respectively.

Result: Finally, the simulation results are presented to validate the operation of the grid-tied PV network via an ANN-based UPQC system. To show the enriched performance of the proposed topology, a comparative analysis is made with PI controller-based UPQC, and outcomes infer to be in agreement with the theoretical discussions. Also, the ANN-based proposed approach reduces the restoration time and THD as well under both sag and swell conditions, respectively.

Conclusion: In this articulated work, a PV power system network with a DC-DC converter and three-phase inverter is employed for grid integration. The peak power extraction is ensured via a DC-DC converter with an incremental conductance algorithm. Both UPQCs are analysed and experimented via MATLAB/Simulink platform with inconstant nonlinear loads to investigate the indices mentioned above and corroborate the same within the operating regions.

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<![CDATA[Non-destructive Machine Vision System based Rice Classification using Ensemble Machine Learning Algorithms]]>https://www.eurekaselect.com/article/1328272024-03-17 Aims and Background: Agriculture plays a major role in the global economy, providing food, raw materials, and jobs to billions of people and driving economic growth and poverty reduction. Rice is the most widely consumed crop domestically, making it a particularly important crop for rural populations. The exact number of rice varieties worldwide is difficult to determine as new varieties are constantly being developed and marketed.

Objectives: The most common method of rice variety identification is a comparison of its physical and chemical properties to a reference collection of known types.

Methodology: This is a relatively quick and cost-effective approach that can be used to accurately differentiate between distinct varieties. In some cases, genetic testing may be used to confirm the identity of a variety, although this technique is more expensive and time-consuming. However, we can also utilize efficient, precise, and cost-effective digital image processing and machine vision techniques.

Results: This study describes different types of ensemble methods, such as bagging (Decision Tree, Random Forest, Extra Tree), boosting (AdaBoost, Gradient Boost, and XGBoost), and voting classifiers to classify five different varieties of rice. Extreme Gradient Boosting (XGBoost) has achieved the highest average classification accuracy of 99.60% among all the algorithms.

Conclusion: The findings of the performance measurement indicated that the proposed model was successful in classifying the various varieties of rice.

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<![CDATA[High-performance Smart Home System based on Optimization Algorithm]]>https://www.eurekaselect.com/article/1329922024-03-17Background: With the recent COVID-19 pandemic, people have become increasingly concerned about their physical health. Therefore, the ability to monitor changes in the surrounding environment in real-time and automatically improve the environment has become a current hot topic to improve the overall health level.

Objective: This article describes the design of a high-performance intelligent home system that can simultaneously perform monitoring and automatic adjustment functions.

Methods: The ESP8266 was used as the core controller, and the DHT11 and G12-04 sensors were used to collect data, such as temperature, humidity, and ambient light intensity. The sampling frequency was increased and the sampled data were processed to improve data accuracy. The sampled data were wirelessly transmitted to a PC or mobile terminal for real-time display. When the sampled data underwent sudden changes, an alert message was sent via the mobile terminal. Based on the real-time changes in ambient light, an improved lighting brightness adjustment algorithm combining bang-bang and single neuron adaptive PID control was used to adjust the lighting brightness.

Results: After testing the system designed in this paper and analyzing the errors compared to standard values, the temperature measurement error ranged from 0% to 0.01107%, and the humidity measurement error ranged from 0% to 0.03797%. The improved algorithm was simulated and tested using MATLAB software and compared with traditional PID algorithms and single-neuron adaptive PID algorithms. The improved algorithm did not overshoot during adjustment, and the system reached a steady state much faster than traditional algorithms.

Conclusion: The system showed good performance in real-time, stability, and accuracy, fully demonstrating the effectiveness of the devices and algorithms used in the system. This provides ideas for the design and improvement of future smart homes.

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<![CDATA[Multi-agent Iot-based System for Intelligent Vehicle Traffic Management Using Wireless Sensor Networks]]>https://www.eurekaselect.com/article/1330052024-03-17 Aims: Integrated computing technologies such as the Internet of Things (IoT), Multi- Agent Systems (MAS), and automatic networking to deliver Internet of Vehicles (IoV) applications.

Methods: The main objective of this paper is to combine MAS with IoT or IoV a new paradigm within its Cypher Physical System (CPS) for intelligent car applications. We proposed the MAS algorithm and applied it to control traffic lights at multiple intersections. When using MAS together with scattered computing architectures, IoV can achieve higher efficiency. The proposed combination is based on the independent knowledge, adaptability, assertiveness, and responsiveness that can be used in wireless sensor paradigms to bring new remedies. Smart products will explore further advancements and diverse mobility capabilities.

Results: IoT provides an appropriate atmosphere for connecting with MAS concepts and programs in addition to providing reliable, adaptable, efficient, and intelligent solutions in the automotive network. In addition, the combination of MAS with IoT and cognitive conditions could result in scalable, automated, and smart wireless sensor solutions.

Conclusion: We conduct experiments on three different datasets, and the results demonstrate that MAS outperforms several state-of-the-art algorithms in alleviating traffic congestion with shorter training time.

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<![CDATA[Novel Image Denoising Techniques Using AFMF]]>https://www.eurekaselect.com/article/1373422024-03-17 Background: In this paper, we have proposed a new image-denoising approach, which is a hybrid technique using the self-organizing migration algorithm (SOMA) and adaptive frequency median filter (AFMF).

Method and Material: The first step in this approach consists of applying (AFMF) to the noisy image in order to have the first version of the denoised image. This first version of the denoised image is considered a clean image, which is then used as an input of an image-denoising system based on SOMA. This denoising system is then applied for denoising the noisy image and then a final version of the denoised image can be obtained. This image denoising system based on SOMA has two inputs, which are the noisy image and the corresponding clean image. However, we have available only the noisy image, and for that reason, we have first applied the AFMF to the noisy image and then obtained the first version of the denoised image as the clean image. In order to improve this proposed denoising technique, we have replaced the denoising system based on SOMA with targeted image denoising (TID), which is a more recent denoising approach. The PSNR (peak-SNR) and SSIM (structural similarity) have been used for evaluating the performance of the image-denoising techniques proposed in this work.

Result: The results obtained from the computations of PSNR and SSIM show the performance of these proposed image-denoising techniques.

Conclusion: The results obtained from the computations of PSNR and SSIM show that the proposed image-denoising techniques outperform a number of image-denoising approaches existing in the literature and used here for our evaluation.

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