Measurement: Energy最新文献

{"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}

{"title":"Energy loss calculation and voltage profile improvement for the rehabilitation of 0.4 kV low voltage distribution network (LVDN)","authors":"Hasham Khan ,&nbsp;Main Farhan Ullah ,&nbsp;Muhammad Saqib Ali ,&nbsp;Muhammad Shahzaib Shah ,&nbsp;Muhammad Adnan Khan ,&nbsp;Muhammad Waseem ,&nbsp;Ahmed Mohammed Saleh","doi":"10.1016/j.meaene.2024.100029","DOIUrl":"10.1016/j.meaene.2024.100029","url":null,"abstract":"<div><div>In recent decades, electrical utilities have made significant advancements in Electrical Power Distribution Systems (EPDS). However, energy loss in distribution networks, particularly in 0.4 kV systems, remains a critical challenge. It is increasingly essential for countries to review and update their power loss policies to deliver electrical energy to consumers at the most feasible and economical rates. The performance of Low Voltage Distribution Networks (LVDN) often falls short, resulting in high voltage fluctuations and significant energy losses for end consumers in 0.4 kV systems. This research aims to address these issues by minimizing energy losses and enhancing the voltage profile of the 0.4 kV distribution network. Various rehabilitation techniques have been employed to significantly improve the efficiency of the LVDN. Since most energy losses are directly associated with the LVDN, two 0.4 kV distribution networks in the villages of New Kalyam and Upper Tharjial in the Mandra Sub-Division, under Islamabad Electricity Supply Corporation (IESCO), Pakistan, were selected as case studies. Field surveys were conducted in collaboration with the local utility staff to collect actual data. The analysis was performed using computer-aided tools, including the Computer-Aided Distribution Planning and Design (CADPAD) software and Energy Loss Reduction (ELR) programs. The proposed methodology is straightforward and practical. The case study results demonstrate significant improvements in the voltage profile and reductions in energy losses within the LVDN. These results have been validated and found to be within permissible standard limits, underscoring the effectiveness of the proposed approach.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"4 ","pages":"Article 100029"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161609","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":"Highly sensitive real-time microwave sensor for detection of organic liquid solvents in an oleic phase","authors":"Bushra Kamal ,&nbsp;Amirhossein Yazdanicherati ,&nbsp;Mabkhot S. BinDahbag ,&nbsp;Zahra Abbasi ,&nbsp;Hassan Hassanzadeh","doi":"10.1016/j.meaene.2024.100028","DOIUrl":"10.1016/j.meaene.2024.100028","url":null,"abstract":"<div><div>Accurately determining the concentration of organic solvents in an oleic phase is essential for various industrial applications, including enhanced oil recovery. Popular detection methods, like chromatographic and distillation-based approaches, suffer from sample processing-induced solvent loss. There is a lack of standard methods for detecting solvents in produced fluid streams during solvent-aided oil recovery. We propose a novel sensing approach for solvent monitoring using planar microwave sensors. The proposed sensor consists of a chipless tag-reader pair communicating wirelessly using electromagnetic coupling. The sensor has a high sensitivity response to variations in permittivity at various solvent concentrations, which is reflected in the resonance-frequency spectrum. To maximize repeatability response of sensor, the sensor is integrated into a plastic container to form a sensing probe that can be used as an on-site in-line instrument. The experiments were conducted using four solvents, including n-pentane, n-hexane, n-heptane, and ethyl acetate. The results demonstrated that when solvent concentration changes from zero to 20 wt%, the frequency shift of resonance peak changes by 2.71, 2.01, 1.66, and 2.10 MHz for the examined solvents, respectively, indicating an exceptional capability of real-time monitoring for measuring solvents in oleic phase. The proposed approach offers the potential for applying planar microwave sensors to detect organic solvents in industrial processes.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"4 ","pages":"Article 100028"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161608","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":"Mixed magnesium, cobalt, nickel, copper, and zinc sulfates as thermochemical heat storage materials","authors":"Jakob Smith ,&nbsp;Peter Weinberger ,&nbsp;Andreas Werner","doi":"10.1016/j.meaene.2024.100027","DOIUrl":"10.1016/j.meaene.2024.100027","url":null,"abstract":"<div><div>Thermochemical energy storage is an emerging technology being researched for harvesting waste heat and promoting integration of renewable energy in order to combat climate change. While many simple salts such as MgSO₄⋅7H₂O have been investigated thoroughly, there remains much work to be done in the domain of materials that take advantage of synergetic effects of multiple different cations located in the same crystal. To this end, a solid solution library of divalent metal sulfates of the formula M_1-xM²_xSO₄·nH₂O (M, M² = Mg, Co, Ni, Cu, Zn) has been synthesized. Following X-ray powder diffraction to confirm phase purity, scanning electron microscopy provided insight into particle morphology. One of the most conspicuous features was the presence of star-shaped cracks in some of the materials, which may contribute to increased surface area and enhance reaction kinetics. The simultaneous thermal analysis of the mixed salt sulfates led to several conclusions. Corresponding to the high initial dehydration barrier of NiSO₄⋅6H₂O, incorporation of nickel into other sulfates led to lower degrees of dehydration at low temperatures. The opposite effect was observed with the addition of copper. Of great interest was the surprisingly facile dehydration of hydrated Mg_0.25Zn_0.75SO₄, which exceeded that of both pure MgSO₄⋅7H₂O and ZnSO₄⋅7H₂O. This promising compound is one representative of three different compounds with 75 % zinc which all have the highest dehydration activity up to 100 °C of all compounds in the series of hydrates of M_1-xZn_xSO₄·nH₂O (M = Mg, Ni, Cu).</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"4 ","pages":"Article 100027"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142756663","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":"Advanced measurement techniques for gas-solids fluidized beds in the power and energy industry - A review∗","authors":"Xiaoli Zhu ,&nbsp;Yi Xu ,&nbsp;Qiuya Tu ,&nbsp;Hanqiao Che ,&nbsp;Haigang Wang","doi":"10.1016/j.meaene.2024.100030","DOIUrl":"10.1016/j.meaene.2024.100030","url":null,"abstract":"<div><div>Gas-solids fluidized beds are widely used in the power and energy sectors for processes such as coal and biomass combustion, as well as gasification. However, the complex and dynamic flow behaviors within these reactors present significant challenges to improving energy efficiency and minimizing environmental impacts. Understanding the hydrodynamics and developing reliable methods to measure key process parameters are essential for optimizing performance and controlling operations. This review provides a comprehensive analysis of current measurement and sensor technologies used in gas-solids fluidized beds under both \"cold\" and \"hot\" conditions. It combines traditional measurement techniques with recent advances in sensor technology for industrial applications, focusing on key parameters such as solids concentration, velocity, flux, temperature, and emissions. The review also discusses fluidized bed process control based on these measurements and the potential for integrating machine learning techniques. Finally, it addresses the challenges faced in large-scale fluidized beds and explores the development of measurement technologies for high-temperature and high-pressure environments.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"4 ","pages":"Article 100030"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161610","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":"Optimization and kinetic study of glucose production from agricultural waste","authors":"Kiman Silas ,&nbsp;Habiba D. Mohammed ,&nbsp;Thlama Mperiju Mainta ,&nbsp;Mohammed Modu Aji ,&nbsp;Jerome Undiandeye","doi":"10.1016/j.meaene.2024.100026","DOIUrl":"10.1016/j.meaene.2024.100026","url":null,"abstract":"<div><div><strong>A</strong>gricultural waste consisting of sugarcane bagasse (SB), cassava peels (RH) and rice husk (RH) were characterized in this study by EDXRF, SEM/EDX, XRD, FTIR, proximate and ultimate analyses. The SB waste showed the highest potential for glucose yield production and was utilized in a Response Surface Methodology (RSM) optimization and kinetic study of enzymatic hydrolysis using isolated <em>Aspergillus niger</em>. An optimized glucose yield of maximum concentration of 92.522 mg/mL was achieved under specific conditions such as time (55.3 min), pH (4.4) and biomass (0.89g). In the kinetic study, the enzymic hydrolysis obeyed the Michaelis-Menten kinetic model, the V_max value was measured at 1.06 mg/mL/h, indicating the maximum rate of reaction achievable under the given experimental conditions. Additionally, the K_M (0.28), representing the substrate concentration at which the reaction rate is half of V_max. This study demonstrates the potential of agricultural waste, as efficient biofuel feedstocks, achieving high glucose yields through optimized enzymatic hydrolysis, crucial for advancing sustainable bioenergy production.</div></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"4 ","pages":"Article 100026"},"PeriodicalIF":0.0,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706673","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}