Measurement: Energy最新文献

{"title":"Electrical losses combat – Detecting theft spots in low voltage networks considering available data from the free energy market","authors":"Rubens Lucian S. Corrêa,&nbsp;Henrique O. Henriques","doi":"10.1016/j.meaene.2025.100040","DOIUrl":"10.1016/j.meaene.2025.100040","url":null,"abstract":"<div><div>With the advancement of IoT technologies and their increasing prevalence in households in developed countries—such as smart sockets, circuit breakers, lamps, and similar devices—the Brazilian market is also experiencing a growing normalization of these technologies as more residential consumers adopt them. In this context, considering the challenges of non-technical losses, the emerging free energy market in Brazil, and the widespread adoption of smart devices, this paper aims to conduct a top-down analysis of the distribution system, with a focus on the low-voltage network where instances of energy fraud often occur. The proposed method identifies busbars affected by theft and pinpoints the responsible consumers by utilizing thermal sensors (a principle employed in technical loss meters), power flow analysis, and statistical testing based on available average load curves.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"5 ","pages":"Article 100040"},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429070","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":"Control of regulated and unregulated emissions of an automotive spark ignition engine with alternative fuels (methanol, ethanol and hydrogen)","authors":"Akshey Marwaha,&nbsp;K.A. Subramanian","doi":"10.1016/j.meaene.2025.100039","DOIUrl":"10.1016/j.meaene.2025.100039","url":null,"abstract":"<div><div>The present research study investigates the effect of alternative fuels (Methanol, Ethanol, Hydrogen) on the performance, combustion, and emission (regulated and unregulated) characteristics of an automotive spark ignition engine. The FTIR (Fourier Transform Infrared) spectrometer was used to measure instantaneous emissions with a frequency of 5. The regulated (carbon monoxide, acetylene, ethene, propene, nitric oxide, nitrogen dioxide) and the unregulated (methane, hydrogen cyanide, nitrous oxide) emissions with ethanol, methanol, and hydrogen are compared with base gasoline. The engine was run at a constant speed of 3500 rpm. The brake thermal efficiency (BTE) was enhanced by 1.2 %, 2.9 %, and 25.9 % with ethanol, methanol, and hydrogen compared to gasoline. The experimental results indicate that the acetylene, propene, methane, and hydrogen cyanide emissions were reduced to zero levels with methanol. Acetylene emissions decreased by 25.3 % and 61.6 % with ethanol and hydrogen compared to base gasoline. The propene emissions were zero with ethanol and methanol. Nitric oxide emissions decreased by 62.4 % and 98.9 % with ethanol and methanol. Hydrogen cyanide emissions decreased by 20 % with hydrogen and to zero with methanol. Nitrogen dioxide emissions decreased by 65.6 % with ethanol. Nitrous oxide emissions decreased by 38.8 %, 75.4 %, and 32.1 % with ethanol, methanol, and hydrogen. Hence, the regulated and unregulated emissions from combustion engines could be controlled and reduced to a minimal level along with enhanced thermal efficiency using alternative fuels such as methanol, ethanol, and hydrogen.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"5 ","pages":"Article 100039"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350101","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":"Design and implementation of a solar powered kit for measurement and logging of environmental parameters using the SEN55 sensor","authors":"J. Nchi Muhvu ,&nbsp;M.D. Fendji ,&nbsp;C.M. Ekengoue","doi":"10.1016/j.meaene.2025.100038","DOIUrl":"10.1016/j.meaene.2025.100038","url":null,"abstract":"<div><div>Air pollution poses significant threats to human health, ecosystems, and biodiversity, exacerbating climate change and reducing life expectancy. This study presents the design and implementation of a 6 W, 5 V solar-powered air quality monitoring system using the SEN55 environmental sensor node. The device measures particulate matter (PM1, PM2.5, PM4, PM10), humidity, temperature, volatile organic compounds (VOC), and nitrogen oxides (NOx), with data logged via an ATmega2560 microcontroller and displayed for real-time visualization. Field tests conducted in urban (Muea) and peri-urban (Awae-Ewangkang) areas in Cameroon revealed significant differences in air quality. Muea showed higher PM and humidity levels, exceeding recommended thresholds, while Awae-Ewangkang demonstrated better air quality but elevated humidity. Investigations highlighted the severe air pollution caused by traditional lighting sources such as kerosene lamps and candles. The device's sensitivity and reliability in identifying pollution hotspots were validated, emphasizing the potential health risks associated with these sources. The study underscores the device's ability to enhance environmental awareness, guide public health initiatives, and promote sustainable alternatives like solar energy systems. This novel system contributes to advancing air quality monitoring, offering a scalable and impactful solution for reducing pollution and safeguarding health.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"5 ","pages":"Article 100038"},"PeriodicalIF":0.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154183","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":"Investigation of combustion-induced vibration sources in a diesel engine in the time-frequency domain using the wavelet analysis method and wavelet cross-correlation analysis method","authors":"Natsuki Takahashi,&nbsp;Shun Nakagawa,&nbsp;Masato Mikami","doi":"10.1016/j.meaene.2025.100037","DOIUrl":"10.1016/j.meaene.2025.100037","url":null,"abstract":"<div><div>This study investigated the characteristics of in-cylinder pressure and vibration acceleration for a single-cylinder diesel engine for different engine speeds using three analysis methods: FFT analysis, wavelet analysis, and wavelet cross-correlation analysis. The cross-correlation coefficients between the in-cylinder pressure and the vibration acceleration of the outer wall near the combustion chamber in the 1/3-octave band were examined to determine whether there was any engine speed dependency and what vibration-transmission characteristics were present. The results from the engine speed dependency suggest that the vibration at 2500 Hz is caused by the piston-connecting rod coupled vibration, which is one of the natural frequencies of the engine, the vibration at 8000 Hz is caused by a combustion chamber resonance, at 6300 Hz a combustion chamber resonance may be linked with a piston natural vibration, and a piston slap may cause the vibration at 5000 Hz. These analysis methods are useful for predicting the source of combustion-induced vibration.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"5 ","pages":"Article 100037"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154216","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 evaluation and impedance spectroscopy of carbon-felt and reinforced stainless-steel mesh electrodes in terrestrial microbial fuel cells for biopower generation","authors":"Meshack Imologie Simeon ,&nbsp;Amarachi C. Alaka ,&nbsp;Peter Daniel ,&nbsp;Olalekan David Adeniyi","doi":"10.1016/j.meaene.2025.100036","DOIUrl":"10.1016/j.meaene.2025.100036","url":null,"abstract":"<div><div>Terrestrial Microbial Fuel Cells (TMFCs) offer promising potential for renewable energy by harnessing microbial metabolism to generate electricity from soil-based organic matter. Electrode materials are key to TMFC performance, facilitating electron transfer between microbes and the circuit. However, the effect of electrode impedance on TMFC efficiency is not well understood. This study fills that gap by comparing surface-modified stainless-steel mesh (SMS) and carbon felt (CF) electrodes, focusing on performance metrics and impedance spectroscopy to optimize electrode design for improved power generation from TMFCs. The SMS electrode fabricated using the pasting and reinforcement process demonstrated superior performance with a maximum power of 859 μW compared to the 234 μW power of the CF electrode. This better performance of the SMS electrode was attributed to its pseudocapacitive behavior, enhancing internal charge storage capacity and overall MFC efficiency. Electrochemical impedance spectroscopy revealed a substantially higher charge transfer resistance in the CF electrode, resulting in a 190.8 % difference between the two electrodes. Conversely, the SMS electrode exhibited lower resistance and improved diffusion characteristics, facilitating efficient electron transfer and mass transport. These findings underscore the significance of tailored electrode materials in optimizing MFC performance and emphasize the utility of electrochemical impedance spectroscopy in elucidating complex electrochemical processes within MFC systems, thus guiding future advancements in sustainable power production in terrestrial MFCs.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"5 ","pages":"Article 100036"},"PeriodicalIF":0.0,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154182","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":"Fault diagnosis of distribution network based on time constraint intuition fuzzy Petri nets","authors":"Chuannuo Xu,&nbsp;Xuezhen Cheng,&nbsp;Xueshan Zhuang,&nbsp;Jiming Li","doi":"10.1016/j.meaene.2025.100034","DOIUrl":"10.1016/j.meaene.2025.100034","url":null,"abstract":"<div><div>In response to the limitations of traditional Petri net-based fault diagnosis models, which struggle to swiftly and accurately pinpoint faulty components in online fault diagnosis scenarios characterized by uncertainty and incomplete information, a fault diagnosis method of distribution network based on time constrained intuition fuzzy Petri nets is proposed. Due to the superior handling of uncertainty by intuition fuzzy sets over fuzzy sets, this paper employs the former to replace the latter. Given the strict hierarchical coordination inherent in relay protection systems, there exists a precise temporal constraint relationship among alarm signals. A forward and reverse temporal inference mechanism is introduced to meticulously scrutinize each alarm signal, thereby refining the initial confidence levels of abnormal alarm data. Building upon the interplay between protection devices and circuit breakers, an intuition fuzzy Petri net model imbued with temporal constraints is constructed. The efficacy of this novel approach is substantiated and benchmarked against existing methods through a series of numerical simulations, underscoring its prowess in accurately identifying defective components within the network.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"5 ","pages":"Article 100034"},"PeriodicalIF":0.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154180","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":"Efficient fault detection and categorization in electrical distribution systems using hessian locally linear embedding on measurement data","authors":"K. Victor Sam Moses Babu ,&nbsp;Sidharthenee Nayak ,&nbsp;Divyanshi Dwivedi ,&nbsp;Pratyush Chakraborty ,&nbsp;Chandrashekhar Narayan Bhende ,&nbsp;Pradeep Kumar Yemula ,&nbsp;Mayukha Pal","doi":"10.1016/j.meaene.2025.100035","DOIUrl":"10.1016/j.meaene.2025.100035","url":null,"abstract":"<div><div>Faults on electrical power lines could severely compromise both the reliability and safety of power systems, leading to unstable power delivery and increased outage risks. They pose significant safety hazards, necessitating swift detection and mitigation to maintain electrical infrastructure integrity and ensure continuous power supply. Hence, accurate detection and categorization of electrical faults are pivotal for optimized power system maintenance and operation. In this work, we propose a novel approach for detecting and categorizing electrical faults using the Hessian locally linear embedding (HLLE) technique and subsequent clustering with t-SNE (t-distributed stochastic neighbor embedding) and Gaussian mixture model (GMM). First, we employ HLLE to transform high-dimensional (HD) electrical data into low-dimensional (LD) embedding coordinates. This technique effectively captures the inherent variations and patterns in the data, enabling robust feature extraction. Next, we perform the Mann–Whitney U test based on the feature space of the embedding coordinates for fault detection. This statistical approach allows us to detect electrical faults providing an efficient means of system monitoring and control. Furthermore, to enhance fault categorization, we employ t-SNE with GMM to cluster the detected faults into various categories. To evaluate the performance of the proposed method, we conduct extensive simulations on an electrical system integrated with solar farm. Our results demonstrate that the proposed approach exhibits effective fault detection and clustering across a range of fault types with different variations of the same fault. Overall, this research presents an effective methodology for robust fault detection and categorization in electrical systems, contributing to the advancement of fault management practices and the prevention of system failures.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"5 ","pages":"Article 100035"},"PeriodicalIF":0.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154179","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":"Design of an IoT model for forecasting energy consumption of residential buildings based on improved long short-term memory (LSTM)","authors":"Mustafa Wassef Hasan","doi":"10.1016/j.meaene.2024.100033","DOIUrl":"10.1016/j.meaene.2024.100033","url":null,"abstract":"<div><div>Long short-term memory (LSTM) networks are critical in predicting periodic time series data on energy consumption, as many other forecasting methods do not take into account periodicity. Despite the effective forecasting capabilities of LSTM networks in predicting periodic energy consumption data, they are hindered by the dead region effect, which is caused by the sigmoid and hyperbolic tangent activation functions. These functions control the flow of information and determine which data is suitable for updating and learning within specific boundaries, but they also create unused regions that impact the accuracy and efficiency of the learning process in LSTM networks. To address this issue, this study introduces an Internet of Things (IoT) energy consumption forecasting model based on an improved long short-term memory (ILSTM) approach. This model aims to overcome the dead region problem and enhance the accuracy and learning process of traditional LSTM networks. The study collected actual energy consumption data from a residential building using a CT (SCT-013-030) sensor and ESP8266 NodeMCU real model with the Thingspek cloud platform for data processing. Additionally, a storage data recycling (SDR) technique is utilized to address data clustering shortages and fill missing information. The ILSTM forecasting model was assessed using various evaluation metrics including mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Additionally, comparisons were made between the throughput, latency, and bill information of the proposed ILSTM forecasting model and the ARIMA, DBN Regression, and conventional LSTM (CLSTM) forecasting models. The evaluation demonstrated that the ILSTM network outperformed the CLSTM network, showing improvements of 61.697% in MAE, 59.248% in MSE, and 50.537% in RMSE. Furthermore, the ILSTM network exhibited lower throughput values for varying energy consumption data compared to the CLSTM, and demonstrated reduced latency compared to ARIMA, DBN Regression, and CLSTM by 40.1, 21.1, and 13.5 cycles, respectively. Lastly, the results revealed that the ILSTM network provided more accurate energy consumption forecasting and bill estimation than the CLSTM.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"5 ","pages":"Article 100033"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154181","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":"Hot water flow and temperature measurement to verify CO2 emission reductions using solar water heating","authors":"Ivan A. Hernandez-Robles","doi":"10.1016/j.meaene.2024.100032","DOIUrl":"10.1016/j.meaene.2024.100032","url":null,"abstract":"<div><div>This work focuses on reduction of CO₂ emissions study by the use of solar water heaters as well as its verification through the development of the AMS - IJ methodology Solar Water Heating Systems (SWH) V2.0 of the Clean Development Mechanism (CDM) agreement for the accreditation of carbon reductions of the United Nations on Climate Change. This study is essential for the progress and generation of knowledge associated with reducing emissions potential estimations by using solar water heaters. For this, this work measures the ambient temperature, the cold water flow and its temperature in SWH input and the hot water flow and its temperature in its output. The measurements were made at three different edifications and locations. The analysis, statistics and grouping of the historical data obtained from these parameters allowed the author to determine a model to estimate the reduction of emissions by the use of solar water heaters throughout the year. This model considers the impact of the weather seasons during the year. The energy saving potential of 3908 GWh was obtained, which represents a reduction of 0.789 Mton CO₂ emissions per year at Guanajuato Mexico. This work contributes to the knowledge and development of new techniques and technologies for measuring and verifying the reduction of emissions from energy consumption in buildings with the aim of promoting more sustainable buildings with a zero energy consumption characteristic and a neutral environmental impact.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"5 ","pages":"Article 100032"},"PeriodicalIF":0.0,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154242","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":"Integration of smart grid with Industry 5.0: Applications, challenges and solutions","authors":"Sunawar khan ,&nbsp;Tehseen Mazhar ,&nbsp;Tariq Shahzad ,&nbsp;Muhammad Amir khan ,&nbsp;Ateeq Ur Rehman ,&nbsp;Habib Hamam","doi":"10.1016/j.meaene.2024.100031","DOIUrl":"10.1016/j.meaene.2024.100031","url":null,"abstract":"<div><h3>Introduction</h3><div>In this study, an investigation of the nexus between state-of-the-art technology and green industrial processes with a view to how smart grid systems can be incorporated into industry 5.0 is done. Industry 5.0 stresses human-machine collaboration together with Artificial Intelligence, the Internet of Things, and Big Data while the recent electrical networks enriched by digital communication technologies are defined as the contemporary smart grids. Notwithstanding advances in both domains, there is a major research gap at the intersection of the two.</div></div><div><h3>Objectives</h3><div>In this study, the essential elements, advantages, and potential impacts of coupling smart grids with Industry 5.0 will be examined. These aims are aimed at sustaining and improving the reliability and efficiency of industrial processes maximizing resource consumption and minimizing ecological damage.</div></div><div><h3>Method</h3><div>ology: The use and benefits of this integration are analyzed using case studies from industrialized countries. It assesses technological developments, challenges and the emerging trends dealing with the combination of smart grid technologies with Industry 5.0.</div></div><div><h3>Findings and discussion</h3><div>In addition, smart grid technology can make industrial processes more dependable and efficient; resulting in more appropriate resource utilization and lower emissions. It promises to revolutionize the energy management systems and production procedures.</div></div><div><h3>Conclusion</h3><div>Drawing from this research, this integration offers the capabilities of developing a technologically advanced and environment-friendly industrial ecosystem that enables a truly sustainable future.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"5 ","pages":"Article 100031"},"PeriodicalIF":0.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154215","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}