e-Prime - Advances in Electrical Engineering, Electronics and Energy最新文献

{"title":"Adversarial measurements for convolutional neural network-based energy theft detection model in smart grid","authors":"Santosh Nirmal ,&nbsp;Pramod Patil ,&nbsp;Sagar Shinde","doi":"10.1016/j.prime.2025.100909","DOIUrl":"10.1016/j.prime.2025.100909","url":null,"abstract":"<div><div>Electricity theft has become a major problem worldwide and is a significant headache for utility companies. It not only results in revenue loss but also disrupts the quality of electricity, increases generation costs, and raises overall electricity prices. Electricity or Energy theft detection (ETD) systems utilizing machine learning, particularly those employing neural networks, have high accuracy and have become popular in literature, achieving higher detection performance. Recent studies reveal that machine learning and deep learning models are vulnerable. Day by day, different attack techniques are coming up in different fields, including energy, financial, etc. As the use of machine learning for energy theft detection has grown, it has become important to explore its weaknesses. Research has shown that most of the ETD models are vulnerable to evasion attacks (EA). Its goal is to reduce electricity costs by deceiving the model into recognizing a fraudulent customer as legitimate.</div><div>In this paper, four different experiments are conducted in which we check the performance of Convolutional Neural Network and adaboost (CNN-Adaboost) ETD system. Then, we design an evasion attack to assess the model's performance under attack. The attack comprises two methods: the first is we originally propose a novel Adversarial Data Generation Method (ADGM), which is an algorithm designed to generate adversarial data, and the other is Fast Gradient Sign Method (FGSM). In the third scenario, test the attack success rate on different percentages of malicious consumers. Finally, the performance of CNN-Adaboost and other state-of-the-art methods is tested and compared using 10 % and 20 % adversarial data. Our proposed attack is validated with State Grid Corporation of China (SGCC) dataset.</div><div>ADGM and FGSM attack models generate adversarial evasion attack samples by modifying the benign sample along with already available malicious data. These samples are transferred to the surrogate model in order to test how efficiently it works on malicious data, and we forward only those data that successfully deceive the surrogate model. The CNN_Adaboost ETD model's overall performance significantly decreased for both methods. The accuracy reduced up to 53.61 % from 96.3 % for ADGM and 63.42 % for FGSM and the transferability rates are 95.82 % and 90.68 % for ADGM and FGSM, respectively. Our findings reveal that the attack success rate (ASR) of ADGM is 94.11 % which is better than FGSM. It is also observed that as the percentage of adversarial data increased, the accuracy of the models decreased. The accuracy of CNN-Adaboost, initially 96.3 %, decreased to 85.45 % and 79.43 % for 10 % and 20 % adversarial data, respectively. These adversaries are transferable and are useful for designing robust and secure machine learning (ML) models.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100909"},"PeriodicalIF":0.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative analysis of real and theoretical data in offshore wind energy generation","authors":"Fernando M. Camilo ,&nbsp;Paulo J. Santos ,&nbsp;Armando J. Pires","doi":"10.1016/j.prime.2025.100901","DOIUrl":"10.1016/j.prime.2025.100901","url":null,"abstract":"<div><div>Wind energy plays a key role in the global shift towards renewable energy, requiring accurate prediction models for integration with power grids and effective energy distribution. This study validates the accuracy of wind speed forecasts from three widely used sources – European Centre for Medium-Range Weather Forecasts (ERA5), Modern-Era Retrospective Analysis for Research and Applications, MERRA-2 (NASA), and the Wind Atlas – against actual power generation data from the WindFloat Atlantic offshore wind farm near Viana do Castelo, Portugal, over the years 2022 and 2023. The results show that NASA’s forecasts were the most precise, with annual relative errors of 5 % for 2022 and 1.6% for 2023, outperforming the other models. This analysis underscores the importance of validated forecasting models to enhance renewable energy management through multi-year data for precise local calibration. The findings also emphasize the necessity of consistent short-term load forecasting models for reliable daily energy production. Overall, this research demonstrates that combining global wind datasets with local validation improves offshore wind prediction accuracy. In this context, NASA’s dataset emerges as the most reliable for operational and planning purposes in offshore renewable energy systems.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100901"},"PeriodicalIF":0.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling solar power plant electricity supply chain toward renewable energy consumption","authors":"Mohammad Reza Eslami Rasekh ,&nbsp;Farzad Mohammad Sharifi ,&nbsp;Somaieh Alavi ,&nbsp;Nassibeh Janatyan","doi":"10.1016/j.prime.2025.100907","DOIUrl":"10.1016/j.prime.2025.100907","url":null,"abstract":"<div><div>This paper aims to introduce a model of the solar plant electricity supply chain, encompassing mixed power plants, transmission lines, and consumers, with a focus on optimization and consideration of uncertainties. Within this article, the supply chain of solar power plants is delineated based on various parameters. The quantities of power plants and solar panels are determined by different priorities, such as investment levels, pollution mitigation, and reduction of gas consumption by conventional power plants, utilizing the particle swarm algorithm for optimal outcomes. The proposed model addresses uncertainties related to electricity demand, solar radiation levels, and consequently, the power production of solar panels, through the application of type 2 fuzzy logic. The optimization of the model is done keeping in mind various constraints including the supply of electricity and the maximum allowed use of solar cells. The innovation of this article is in the design of the supply chain model from the point of view of the uncertainty of electric power production and the amount of consumer demand and the optimal selection of solar panels for solar power plants to minimize the electricity consumption of the gas power plant and the amount of pollution caused by it. Based on the results obtained from the simulation of this article, it has been shown that considering the maximum investment capacity, up to 76 % of the electric energy can be supplied by building five solar power plants at certain distances from the electric substations around the case study. Considering the maximum weight coefficients for CO₂ plant emissions and gas consumption, five solar power plants are the optimal number that is achieved by proposed algorithm. The power capacity of five solar power plants is optimized <span><math><mrow><mn>4.100</mn><mo>,</mo><mspace></mspace><mn>4.222</mn><mo>,</mo><mspace></mspace><mn>3.920</mn><mo>,</mo><mspace></mspace><mn>4.375</mn><mspace></mspace></mrow></math></span>and <span><math><mrow><mn>3.991</mn><mspace></mspace><mi>M</mi><mi>W</mi></mrow></math></span>, respectively. To evaluation of the proposed model, PSO algorithm is compared to GA and the results show that cost function and convergence time in PSO is less than GA in the various weight coefficients scenarios. This optimal mode leads to the maximum reduction of gas consumption in the gas power plant, and on the other hand, the amount of pollution is minimized. The prediction of this number of power plants with different priorities is presented in this article and different policies can be considered strategically for this model of the supply chain.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100907"},"PeriodicalIF":0.0,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new hybrid distance and similarity based scenario reduction approach for stochastic economic operation of microgrid","authors":"Gaurav Gangil,&nbsp;Amit Saraswat,&nbsp;Sunil Kumar Goyal","doi":"10.1016/j.prime.2025.100905","DOIUrl":"10.1016/j.prime.2025.100905","url":null,"abstract":"<div><div>This paper attempts to develop a multi-time period stochastic optimization model for economic operations of a typical microgrid by employing a scenario-based analysis approach to exploit various uncertainties associated with variable renewable energy (VRE) generations, electricity prices, and load demand. This stochastic model is aimed at generating the optimum schedules for various dispatchable generating resources such as micro-turbines, fuel cells, utility grid, energy storage devices as per the availability of the various VRE resources to meet the uncertain demand for a day-to-day microgrid operation. Further, a suitable scenario reduction approach named hybrid distance and similarity (HDS) approach is proposed to cater for two diverse objectives i.e., minimization of the Manhattan distance and maximization of the similarity index between an optimal scenario pair for generating a reduced scenario set by eliminating large redundant scenarios from its original large set. To verify the effectiveness of the proposed HDS, its performance is compared with three well developed distinct methods such as SBR (simultaneous backward reduction method), FFS (fast forward selection method), and SIMCOR (similarity-correlation method) on two different stochastic optimization problems including one real-life economic microgrid problem. All the competing scenario reduction methods are compared in terms of various performance indices i.e. OSDI (Output Sample Deviation Index), PSRI (Percentage Scenario Reduction Index), objective values, and computation time to verify their suitability and effectiveness on complex optimization problems. The proposed HDS method is found to be capable in achieving the lowest OSDI value of 5.68 at 98 % scenario reduction while compared to other competing methods i.e. 12.95 by SBR, 14.76 by FFS, and 16.32 by SIMCOR for the real-life microgrid problem. Moreover, the proposed HDS methods also outperforms the other three competing methods in terms of their objective function values after 98 % scenario reduction with a least computation time burden i.e. 87.6 %, 1.11 %, and 53 % less computing times are needed by HDS, FFS, and SIMCOR, respectively. These comprehensive simulation results reveal that the proposed HDS method is capable to generate high-quality scenarios, better approximation, superior stability, and with lower computation time burden as compared to the other three competing scenario reduction approaches.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100905"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection and classification of single-circuit and intra-circuit faults based on analysis of current signals of near-end terminal in double-circuit transmission lines","authors":"Mahyar Abasi ,&nbsp;Pirooz Gorjian","doi":"10.1016/j.prime.2025.100904","DOIUrl":"10.1016/j.prime.2025.100904","url":null,"abstract":"<div><div>This paper presents a method of inter-circuit and intra-circuit fault detection in a double-circuit transmission line based on the analysis of six-phase current signals of a single terminal. The method is developed by analyzing the output results of several mathematical integral transforms, including the modified Stockwell, Hilbert, and modal transforms. The algorithm is designed in three steps. In the first step, by investigating the alpha component of the modal transform, it can be recognized that the fault is single-circuit or double-circuit. Then, in the second step, by analyzing the zero component of the modal transform, it can be identified if the fault is grounded or not. In the third step, by using the analysis of a new index based on the combination of modified Stockwell and Hilbert transforms and a statistical index based on correlation, the faulty phase of the double-circuit line can be identified. The method is coded in the MATLAB environment and the test network is modeled in MATLAB/Simulink. The implementation results of different fault scenarios as well as the analysis of the algorithm's behavior under critical conditions validates the algorithm's high performance.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100904"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance analysis of a multi-user outdoor visible light communication system using wavelength division multiplexing (WDM) with RZ-OOK and NRZ-OOK modulations for V2X communications","authors":"Anass Kharbouche,&nbsp;Hamza Ouamna,&nbsp;Zhour Madini,&nbsp;Younes Zouine","doi":"10.1016/j.prime.2025.100900","DOIUrl":"10.1016/j.prime.2025.100900","url":null,"abstract":"<div><div>Vehicle-to-Everything (V2X) communication represents a revolution in traffic management and road safety, with innovation at the heart of this evolution. In this context, Visible Light Communication (VLC) emerges as a promising alternative to meet the demands for high-speed and low-latency communication. LED lamps, primarily used for lighting, can also serve as data transmission mediums. This article proposes a VLC system utilizing Wavelength Division Multiplexing (WDM) to simultaneously transmit data streams from different sources via a Free Space Optical (FSO) channel.</div><div>We evaluated the performance of this system by analyzing the impact of the Return to Zero (RZ) and Non Return to Zero (NRZ) formats at the optical transmitter on the WDM/FSO system. Simulations were conducted by varying the FSO channel distance and testing different throughput scenarios while considering weather conditions. The results show that Return to Zero, On-Off Keying (RZ-OOK) modulation can achieve high data rates at short distances, while Non Return to Zero, On-Off Keying (NRZ-OOK) modulation excels at longer distances with moderate data rates, particularly under adverse weather conditions.</div><div>These findings pave the way for innovative applications in the field of V2X communications, addressing the specific needs of both urban and non-urban environments.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100900"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized parameter estimation of lithium-ion batteries using an improved cuckoo search algorithm under variable temperature profile","authors":"Tasadeek Hassan Dar ,&nbsp;Satyavir Singh","doi":"10.1016/j.prime.2025.100902","DOIUrl":"10.1016/j.prime.2025.100902","url":null,"abstract":"<div><div>Lithium-ion batteries are an intuitive choice for electric vehicles and many other gadgets. Parameters play a critical role in addressing its performance characterization. Accurate parameter estimation and real-time monitoring of lithium-ion batteries are important in modeling equivalent circuits. The characteristics of lithium-ion batteries are dynamic due to energy storage. Dynamical behavior is characterized by RC equivalent models. This work presents the estimation of parameters associated with the n-RC equivalent circuit model in integration with the Improved Cuckoo Search Algorithm (ICSA). To get it, battery tests such as HPPC test, static capacity test, and open circuit voltage test in consideration of temperatures are carried out. The experiments are carried out under different temperature ranges to record the valid data sets. ICSA is advantageous over existing algorithms in estimating the battery parameters under temperature ranges. The performance of the proposed approach captures and estimates the parameters in the dynamic range of temperatures of the lithium-ion battery. The error profile is addressed with the root mean square error and it is found to be 0.23 % at 30 °C. It is observed that experimental data with ICSA accurately matches the simulated model data at different temperature ranges.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100902"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A compact active planar patch antenna array for sub-6 GHz 5G applications","authors":"Farid El Ghaoual ,&nbsp;Jamal Zbitou ,&nbsp;Mostafa Hefnawi ,&nbsp;Aboubakr El Hammoumi","doi":"10.1016/j.prime.2025.100903","DOIUrl":"10.1016/j.prime.2025.100903","url":null,"abstract":"<div><div>This paper presents the design, analysis, achievement, and validation of a high-performance 64-element planar patch antenna array tailored explicitly for sub-6 GHz fifth-generation (5G) communication systems. Operating at 3.5 GHz and mounted on an FR4 substrate, the antenna array exhibits a high gain of approximately 17.5 dB, with excellent input impedance matching and radiation efficiency. To further enhance the system's performance, the antenna array was integrated with a custom-designed power amplifier based on the BFP640ESD bipolar transistor. This integration enabled the achievement of an active antenna circuit, improving both transmission power and overall system efficiency. The power amplifier was designed using the Advanced Design System (ADS) solver. To ensure reliable operation, key parameters such as power efficiency, input/output impedance matching, and stability were analyzed and achieved. Integrating the power amplifier, the active antenna array was thoroughly tested into simulation and validated at 3.5 GHz. The results demonstrate that the proposed circuit not only meets the required specifications for 5G applications but also offers enhanced performance in terms of signal strength and matching input impedance across a wide bandwidth. This study confirms the benefit of incorporating an active power amplifier with a planar antenna array for modern wireless communication systems.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100903"},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate and optimal control of a bidirectional DC-DC converter: A robust adaptive approach enhanced by particle swarm optimization","authors":"Julius Derghe Cham ,&nbsp;Francis Lénine Djanna Koffi ,&nbsp;Alexandre Teplaira Boum ,&nbsp;Ambe Harrison ,&nbsp;Paul Michael Dongmo Zemgue ,&nbsp;Njimboh Henry Alombah","doi":"10.1016/j.prime.2025.100899","DOIUrl":"10.1016/j.prime.2025.100899","url":null,"abstract":"<div><div>The efficient operation of DC-microgrids is highly depend on DC-DC converters. The Half-bridge Bidirectional DC-DC converter, a special class of power electronic converters has received significant attention in DC-microgrids due to its high flexibility. However, arriving at an optimal operating performance of this converter requires robust, accurate control and regulation of its output. To address the control requirements of this system, this paper proposes a robust adaptive nonlinear control strategy based on adaptive sliding mode controller. Unlike contemporary controllers, the proposed control strategy alleviates the chattering limitations of the classical sliding mode controller through the integration of a smooth hyperbolic tangent function. Additionally, the control structure is enhanced by an optimal adjustment of its gains through particle swarm optimization. A series of numerical investigations are conducted under diverse operating conditions such as variations in reference voltage, load resistance, and input voltages. The acquired results revealed a satisfactory response of the proposed control structure. Furthermore, by thoroughly comparing its performance against existing controllers such as conventional sliding mode controller, super-twisting sliding mode controller, adaptive sliding mode controller, this paper aims to emphasize the superiority of the proposed controller in achieving accurate and robust performance of the Half-bridge bidirectional DC-DC converter. Finally, experimental results were provided to validate the proposed controller in real time.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100899"},"PeriodicalIF":0.0,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing thyroid nodule assessment with deep learning and ultrasound imaging","authors":"Jatinder Kumar ,&nbsp;Surya Narayan Panda ,&nbsp;Devi Dayal ,&nbsp;Manish Sharma","doi":"10.1016/j.prime.2025.100894","DOIUrl":"10.1016/j.prime.2025.100894","url":null,"abstract":"<div><div>The thyroid is a tiny, butterfly-shaped gland in the neck which produces hormones that are essential for controlling the body's various metabolic processes. Thyroid nodules, which are abnormal growths or lumps in the thyroid gland, are common thyroid illnesses, as are hypothyroidism, hyperthyroidism, and both. Thyroid issues are most commonly identified and categorised using thyroid ultrasonography (USG) images. They can have a range of effects on the body's metabolism and overall health. Developments in artificial intelligence (AI), particularly deep learning (DL), are helping to identify and measure patterns in clinical images because of DL's capacity towards pull out hierarchical attribute representations from images without the need for annotated images. Minimizing unnecessary fine needle aspiration (FNA) requires the essential identification of as many malignant thyroid nodules as possible, distinguishing them from benign ones. This research work introduces a technique for thyroid nodule identification in USGs, employing DL to extract relevant features. Three pre-trained DL models, namely ResNet-18, VGG-19 and AlexNet were fine-tuned before using for classification of thyroid USG images. The models' testing and training were done with Digital Database of Thyroid Ultrasound Images (DDTI) which is gold standard dataset. The results demonstrate a classification accuracy of 97.13%, 90.31% and 83.59% with ResNet-18, VGG-19 and AlexNet, respectively. The experimental findings affirm that the pre-trained network model ResNet-18 achieves superior classification performance compared to VGG-19 and AlexNet.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100894"},"PeriodicalIF":0.0,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}