Measurement: Energy最新文献

{"title":"Sparrow optimization gated recurrent convolutional network for temperature modeling of wave rotor refrigeration process","authors":"Qi Li ,&nbsp;Kun Han ,&nbsp;Shifa Cui ,&nbsp;Yaru Shi","doi":"10.1016/j.meaene.2025.100061","DOIUrl":"10.1016/j.meaene.2025.100061","url":null,"abstract":"<div><div>Temperature modeling plays an important role in the wave rotor refrigeration process control and optimization. However, considering data-driven nonlinear and time-delay modeling, how to determine the structure of the model is a challenging problem. To solve this problem, a novel sparrow optimization gated recurrent convolutional network (SGRC) deep learning method is proposed. Firstly, to exploit the advantages of convolutional neural network (CNN), the sample data is converted into grids along the time axis similar to the image input, which contains model structure and dynamic time-delay information. The multivariate and dynamic time-delay information is input into the CNN to extract the multivariate model structure features of the data. Then, after flattening the data into one-dimensional time series, input it into gated recurrent unit (GRU) layers to learn the temporal dependencies of the wave rotor refrigeration. The hyperparameters of the SGRC network are optimized using the sparrow search algorithm (SSA). Finally, simulation results based on wave rotor refrigeration industry data show that the proposed SGRC method achieves superior performance compared to traditional machine learning and other deep learning approaches, exhibiting lower RMSE and MAE values while attaining a higher R² score. This enhancement significantly improves the generalization capability of the temperature model in the wave rotor refrigeration process.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"7 ","pages":"Article 100061"},"PeriodicalIF":0.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757118","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 analysis of solar thermal collectors: A comprehensive review”","authors":"Tejas V. Kadam ,&nbsp;Shrawan Y. Pakhare ,&nbsp;Akshay B. Godse","doi":"10.1016/j.meaene.2025.100059","DOIUrl":"10.1016/j.meaene.2025.100059","url":null,"abstract":"<div><div>Solar thermal collectors (STCs) are central to the transition toward sustainable energy systems, enabling the conversion of solar radiation into useable heat for residential, commercial, and industrial applications. This review presents a critical analysis of the performance, classification, and recent advancements in STCs, including flat-plate collectors, evacuated tube collectors, concentrating systems, and hybrid photovoltaic/thermal configurations. Emphasis is placed on the evaluation of key performance metrics—thermal efficiency, optical properties, and fluid dynamics—under standardized protocols such as ASHRAE 93–2003 and ISO 9806. The review further explores state-of-the-art enhancements involving nanofluids, selective coatings, and phase change materials (PCMs), highlighting their effectiveness and integration challenges. Special attention is given to instrumentation techniques, measurement uncertainties, and real-time monitoring systems used in performance assessment. Finally, emerging research directions, including intelligent control strategies, hybridization, and climate-adaptive designs, are discussed to guide future development of more efficient, durable, and scalable solar thermal technologies.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"7 ","pages":"Article 100059"},"PeriodicalIF":0.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702414","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":"Development of low-cost adsorbents from coconut shell for energy-efficient dye removal from laboratory effluent discharge","authors":"Abdulhalim Musa Abubakar ,&nbsp;Naeema Nazar ,&nbsp;Abdulghaffaar Assayyidi Yusuf ,&nbsp;Enyomeji Ademu Idama ,&nbsp;Moses NyoTonglo Arowo ,&nbsp;Aisha Maina Ma'aji ,&nbsp;Irnis Azura Zakarya","doi":"10.1016/j.meaene.2025.100057","DOIUrl":"10.1016/j.meaene.2025.100057","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Laboratory effluents containing dyes, particularly methylene blue (MB) impact human health negatively and the need to eliminate it from wastewaters using low-cost adsorbent such as coconut shell activated carbon (CS-AC) is paramount. Sodium hydroxide (NaOH) was used to activate the conversion of CS to AC, which was subsequently employed to adsorb MB from laboratory effluent. Preliminary optimization study led to the selection of 0.2g CS-AC dosage, pH of 6, 303K temperature, 45 min contact time, 100 mg/L initial MB concentration and a 0.5 L solution, which gives a maximum adsorption capacity (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;q&lt;/mi&gt;&lt;mi&gt;max&lt;/mi&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) of 153.765 mg/g and 62.285 % removal efficiency (RE). A suite of isotherm (Langmuir, Freundlich, Temkin, Halsey, and Dubinin–Radushkevich) and kinetic (Pseudo-First Order, Pseudo-Second Order, Elovich, Intraparticle Diffusion, Boyd, Bhattacharya-Venkobachar, and Natarajan–Khalaf) models were applied using both graphical (GT) and nonlinear regression techniques (NRT) using LAB Fit V7.2.50 software. The Langmuir model, using NRT, gave a valid separation factor (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; = 0.215) and a high maximum capacity (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;q&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; = 172.12 mg/g), confirming favorable monolayer adsorption. Freundlich model yielded a realistic adsorption intensity (n = 2.7) with &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;K&lt;/mi&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; = 56.48 L/g, suggesting heterogeneous adsorption, although GT estimates were physically inconsistent. Temkin and Halsey models also showed good physical reliability under NRT with adsorption energies of 1.485 kJ/mol and exponent &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;n&lt;/mi&gt;&lt;mi&gt;h&lt;/mi&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; = 3.06, respectively. Dubinin–Radushkevich model supported a physisorption mechanism with a low energy (E = 0.047 kJ/mol) from GT and a consistent &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;q&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; of 161.03 mg/g from NRT. Pseudo-Second Order is the best kinetic model fit – reflecting chemisorption at high &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;q&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; and moderate ‘h’. FTIR analysis confirmed the presence of oxygen-containing functional groups and the absence of nitrogen functionalities, which may explain the moderate adsorption performance. SEM images revealed significant morphological changes post-adsorption, including pore blockage and surface smoothing. A general assessment show that NRT estimates proved more reliable, indicating that CS-AC exhibits a predominantly physical, favorable, and heterogeneous adsorption behavior toward MB. This study confirms CS-AC's potential as a cost-effective adsorbent for dye-contaminated laboratory wastewater and recommends further surface engineering to enhance adsorption performance. Water is in constant use in the laboratory, whose recycle or treated dis","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"7 ","pages":"Article 100057"},"PeriodicalIF":0.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338496","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":"Resilient load frequency control of cyber–physical power systems with off-the-shelf redundant communication channels under FDI attacks","authors":"Chongxin Huang ,&nbsp;Song Deng ,&nbsp;Hui Ge","doi":"10.1016/j.meaene.2025.100053","DOIUrl":"10.1016/j.meaene.2025.100053","url":null,"abstract":"<div><div>The ICT-dependent cyber–physical power systems are exposed to increasing cyber security threats. It has been revealed that cyber-attacks may significantly deteriorate control performances or even destroy system stability. Since the load frequency control (LFC) application plays a vital role in the frequency stability of power systems, multiple redundant communication channels have been designed for an existing LFC system in advance. This paper considers the real multi-channel feature of the power communication network and proposes a secure signal extraction method for the LFC system against false data injection (FDI) attacks. To ensure the reliability of the measurement and control signals, we adopt the median filtering (MF) algorithm to extract the intact signal from multiple redundant signal channels. Furthermore, we prove that the MF algorithm can always obtain the secure signal from multi-channel signals when the number of normal signals is greater than that of abnormal signals. Eventually, we use the benchmark IEEE 30-bus system to validate the effectiveness of the proposed method.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"7 ","pages":"Article 100053"},"PeriodicalIF":0.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306780","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":"A machine learning based model for classifying incipient faults and degree in power transformer windings using voltage current diagram technique","authors":"Sametah Macine Ngong ,&nbsp;Ftatsi Mbetmi Guy-de-patience ,&nbsp;Mohaman Gonza ,&nbsp;Ndjiya Ngasop","doi":"10.1016/j.meaene.2025.100056","DOIUrl":"10.1016/j.meaene.2025.100056","url":null,"abstract":"<div><div>Power transformers are important components of electrical systems. Their failure or malfunction can have serious consequences, affecting the overall functionality or safety of the electrical system. Rapid and accurate diagnosis of transformer internal faults are key factors of efficient and safe operation. in the literature, several techniques of power transformers windings diagnosis exist. The voltage current diagram is a promising and powerful technique acknowledged to be efficient and quick in winding faults diagnosis. However, two major limitations of this technique reported in the literature are: its inability to detect incipient faults and the method of faults classification which is manual. In this study, two machine learning algorithms (SVM and linear regression) are combined with voltage current diagram to diagnose internal incipient faults by analysing data collected during fault simulations on a layer type power transformer. Three main faults responsible of power transformer failure are considerate: turn to turn short-circuit, buckling stress and axial displacement. For each type of fault, a dataset is generated and the model is trained. The SVM algorithm is used to identify the type of fault (classification), and the linear regression algorithm is used to determine its degree of severity. The highest performance of classification was obtained using the RBF kernel (82 %) and the determination of the degree of severity using R-squared gave a score of 99,9 %.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"7 ","pages":"Article 100056"},"PeriodicalIF":0.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262324","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 of CO2 reduction in green materials","authors":"P. Manikandan ,&nbsp;E. Veeramanipriya ,&nbsp;S. Sumitha","doi":"10.1016/j.meaene.2025.100054","DOIUrl":"10.1016/j.meaene.2025.100054","url":null,"abstract":"<div><div>The concrete sector has come under a greater microscope to lower its carbon dioxide emissions due to growing sustainability awareness. Most of the carbon dioxide emissions from the industry are caused by transporting materials and manufacturing Portland cement, the primary binder in concrete. To improve sustainability and achieve net-zero emissions by 2050, the industry has, nevertheless, made a number of hopeful moves in the last ten years. This study examines the permeability conduct of polymer E waste in concrete. Specifically, it finds that the optimal amount of fly ash content in concrete, regardless of the presence of recycled polymer E waste, reduces saturated water absorption and increases it as fly ash mixing increases. With an increase in fly ash and E waste, porosity and sorptivity are somewhat rising. Linear regression in IBM SPSS 23 Statistical analysis is used to demonstrate the compressive strength of the tests using a statistical software. The experimental value is compared with the predicted value using the relationship between the predicted and experimental values of recycled polymer E waste concrete to evaluate the percentage of error. This research examined how the construction industry addresses these sustainability challenges to raise awareness among its stakeholders and the industry itself also the present work is intended to describes the performance assessment and statistical analysis of green building materials (recycled polymer E waste concrete) with the mixture of fly ash and analyzing various characteristics for CO₂ reduction.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"7 ","pages":"Article 100054"},"PeriodicalIF":0.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290980","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":"Handling concept drift in data-oriented power grid operations","authors":"Yasushi Miyata ,&nbsp;Hiroshi Ishikawa","doi":"10.1016/j.meaene.2025.100052","DOIUrl":"10.1016/j.meaene.2025.100052","url":null,"abstract":"<div><div>Data-oriented business transformation, also known as “digitalization”, can improve business tasks by providing better insights into the subject through the data. In digitalizing the power grid, more accurate state recognition from the measurement data is expected to promote a low-cost and stable power supply. Acquiring measurement data from the power grid, clustering, and anomaly detection to recognize the current state could lead to better decision-making for power grid operations. While measurement data serves as the starting point, the interpretation of data trends changes due to the influence of the surrounding environment and aging in the real world. This change in data trends, known as concept drift, poses a challenge to efficient data-oriented power grid operations with accurate state recognition using data clustering models. This is because the data clustering model, especially for complex systems like a power grid, is also built data-oriented, and data trends affect the model. To address this combined challenge of concept drift and its impact on the data clustering model, we propose Re-DBSCAN, a stream data clustering model capable of handling uncertain distributions, to detect concept drift and sequentially update its model for data streams from the power grid. The evaluation uses the WECC179 power grid model to simulate power oscillations and their trend changes with the basic concept drift types: abrupt, incremental, and gradual. Compared to other stream data clustering methods that lack a concept drift detection mechanism, the proposed Re-DBSCAN showed less degradation in purity, indicating higher clustering accuracy. The results suggest that by handling concept drift by detecting data trend changes and sequentially adapting the clustering model, Re-DBSCAN can more accurately cluster measurement data containing concept drift based on its trend changes.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"7 ","pages":"Article 100052"},"PeriodicalIF":0.0,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144222575","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":"Sensing-based monitoring systems for electric vehicle battery – A review","authors":"Ferdous Irtiaz Khan,&nbsp;Md Moinul Hossain,&nbsp;Gang Lu","doi":"10.1016/j.meaene.2025.100050","DOIUrl":"10.1016/j.meaene.2025.100050","url":null,"abstract":"<div><div>The swift uptake of Electric Vehicles (EVs) has increased the demand for improved Battery Management Systems (BMS) to ensure the safety, efficiency, and durability of lithium-ion batteries. This review explores the current advancements in EV battery monitoring technologies, with a focus on sensing mechanisms that estimate critical parameters such as battery states and thermal conditions. Various sensor technologies, including image-based methods, acoustic sensing, force sensors, thermal sensors, magnetic probing and optical sensors, are reviewed and discussed, highlighting their advantages, limitations, and suitability for practical applications. Additionally, gaps and challenges within the field are identified, including cell-level sensing, onboard monitoring, data acquisition mechanism, fault diagnostics and the application of sensors for internal analysis. These challenges underscore the necessity of developing scalable, non-invasive, and cost-effective solutions.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"6 ","pages":"Article 100050"},"PeriodicalIF":0.0,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936172","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":"Green synthesis and characterization of nano selenium using the extract of Phyllanthus emblica","authors":"S. Reema Sagitha ,&nbsp;V. Aravindan ,&nbsp;J. Suvetha Rani ,&nbsp;M. Mahendran","doi":"10.1016/j.meaene.2025.100051","DOIUrl":"10.1016/j.meaene.2025.100051","url":null,"abstract":"<div><div>To maintain the health of humans, animals, and microorganisms, selenium is a trace and essential micronutrient. Selenium nanoparticles (Se-NPs) have recently garnered the attention of numerous researchers as a result of their outstanding biocompatibility, low toxicity and significant bioavailability. Due to their increased bioactivity, Se-NPs finds extensive use in a variety of biomedical applications. Phyllanthus emblica (an Indian gooseberry) was employed as a stabiliser and reducer in the development of a straightforward method for the synthesis of Se-NPs in the present study. Fourier Transform Infrared Spectroscopy (FT-IR) and UV–Visible Spectroscopy were employed to characterize the synthesized selenium nanoparticles, including their optical properties. The synthesized nanoparticles photocatalytic degradation efficiency was investigated using Methylene Blue (MB) Dye. We tested the antibacterial properties of the synthesized Se-NPs against both gram-positive and gram-negative bacteria using the disc diffusion method. The synthesized Se-NPs demonstrated significant inhibition and antibacterial activity against both gram-positive and gram-negative isolates.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"6 ","pages":"Article 100051"},"PeriodicalIF":0.0,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928967","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":"Electrical modelling of a grid-connected battery energy storage system via EMS and BMS data","authors":"Mirko Ledro ,&nbsp;Jan Martin Zepter ,&nbsp;Morten Paludan ,&nbsp;Mattia Marinelli","doi":"10.1016/j.meaene.2025.100048","DOIUrl":"10.1016/j.meaene.2025.100048","url":null,"abstract":"<div><div>With growing popularity of grid-connected battery energy storage systems (BESSs), operators require electrical models for optimal utilisation. These models should be provided by suppliers or manufacturers based on testing methods applied to individual cells or modules in specialised laboratories. However, operators are also interested in developing electrical models on their own. This paper explores the feasibility of modelling a grid-connected BESS without dismantling it, using only the data from its energy management system (EMS) and battery management system (BMS). The goal is to characterise a BESS directly on-site, controlling it through the available commands of its power converter system (PCS). The aim is to represent the electrical dynamics of the BESS with an equivalent Thevenin electric circuit composed of open circuit voltage, resistances, and capacitances. The overall usable capacity of the BESS and the efficiency of the PCS are also estimated. The subject of the investigation is a 300<!--> <!-->kW/652<!--> <!-->kWh Nickel-Manganese-Cobalt (NMC) Li-ion BESS composed of ten racks, each equipped with a PCS and ten battery modules. The analysis proves the feasibility of modelling the grid-connected BESS via data from EMS and BMS. An equivalent cell model is derived, with the open circuit voltage and internal impedance expressed for the entire state-of-charge range. The total resistance assumes values in the range of 1.580–2.424<!--> <!-->m<span><math><mi>Ω</mi></math></span>, whereas the total capacitance is 609.5–1,580<!--> <!-->kF. Consequently, the normalised total resistance is 3%–4%, aligned with the expectations from other NMC Li-ion cells. Finally, the energy capacity and PCS efficiency are reported as a function of the power of the PCS. The usable energy capacity per rack is approx. 59.7<!--> <!-->kWh, which is 91.5% of the rated DC energy capacity, and it is independent of the power at which the BESS is discharged. The PCS efficiency is above 94% when operating at 15% of the PCS rated power or higher, during both rectifier and inverter mode. The obtained efficiency curves differ by approx. 2% from the ones reported in the converters’ data sheet.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"6 ","pages":"Article 100048"},"PeriodicalIF":0.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898861","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}