The dynamic nature of distribution networks raises fresh issues with how such electrical systems function. These networks have some characteristics that indicate the need for better monitoring and control capabilities, including dispersed generation employing renewable resources, changing load profiles, and rising reliability requirements. Phasor measurement units (PMUs) offer simultaneous voltage and current phasor measurements at various places and offer a variety of options for gauging the condition and health of the power distribution network. In this regard, a cost‐optimized PMU with some unique features for distribution systems is presented in this work. These features include a fuzzy inference system to determine the root cause of potential electrical disturbances and methods to estimate electrical parameters through measured field data, which are necessities. This study takes into account the modelling of PMUs, utilizing a process for fault detection and classification with a fuzzy inference network. The 9‐bus distribution network's dependability model is built once the components and their functions are first outlined. The proposed model is then used to calculate the availability of the presented model, which has been examined to provide an analogous reliability model for PMUs. Depending on the specific manufacturer, the PMU's design and specs will change. To extract phase and size measurement features for the proposed model adaptive neural‐fuzzy inference system network's training, two PMU structures and associated reliability models are described here. When merging input data for fuzzy neural network prediction using MATLAB software, fuzzy sets are taken into account for error classification analysis.
{"title":"Diagnosis and classification of disturbances in the power distribution network by phasor measurement unit based on fuzzy intelligent system","authors":"Marzieh Khosravi, Mohammad Trik, Alireza Ansari","doi":"10.1049/tje2.12322","DOIUrl":"https://doi.org/10.1049/tje2.12322","url":null,"abstract":"The dynamic nature of distribution networks raises fresh issues with how such electrical systems function. These networks have some characteristics that indicate the need for better monitoring and control capabilities, including dispersed generation employing renewable resources, changing load profiles, and rising reliability requirements. Phasor measurement units (PMUs) offer simultaneous voltage and current phasor measurements at various places and offer a variety of options for gauging the condition and health of the power distribution network. In this regard, a cost‐optimized PMU with some unique features for distribution systems is presented in this work. These features include a fuzzy inference system to determine the root cause of potential electrical disturbances and methods to estimate electrical parameters through measured field data, which are necessities. This study takes into account the modelling of PMUs, utilizing a process for fault detection and classification with a fuzzy inference network. The 9‐bus distribution network's dependability model is built once the components and their functions are first outlined. The proposed model is then used to calculate the availability of the presented model, which has been examined to provide an analogous reliability model for PMUs. Depending on the specific manufacturer, the PMU's design and specs will change. To extract phase and size measurement features for the proposed model adaptive neural‐fuzzy inference system network's training, two PMU structures and associated reliability models are described here. When merging input data for fuzzy neural network prediction using MATLAB software, fuzzy sets are taken into account for error classification analysis.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"25 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139395078","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}
An operation site safety detection method based on point cloud data and improved YOLO algorithm under the power Internet of Things architecture is proposed to address the complex environment of power construction sites and the poor effectiveness of most existing object detection methods. Firstly, an operation site safety supervision system was designed based on the power Internet of Things architecture, and efficient image processing was achieved through cloud edge collaboration. Then, point cloud data and on‐site monitoring information are used on the edge side to extract the accessible area, ensuring that the target is located in a safe area. Finally, the YOLO algorithm is improved in the cloud by using clustering algorithms, network structure optimization, and other methods, and used to detect targets and determine whether their behaviour meets the safety requirements of the operation site. Based on the PyTorch deep learning framework, the proposed method was experimentally demonstrated, and the results showed that its average detection accuracy and time were 94.53% and 68 ms, respectively, providing technical support for achieving remote monitoring of power operation sites.
{"title":"An operation site security detection method based on point cloud data and improved YOLO algorithm under the architecture of the power internet of things","authors":"Shibo Yang, Yu Wang, Shuai Guo, Shijie Feng","doi":"10.1049/tje2.12344","DOIUrl":"https://doi.org/10.1049/tje2.12344","url":null,"abstract":"An operation site safety detection method based on point cloud data and improved YOLO algorithm under the power Internet of Things architecture is proposed to address the complex environment of power construction sites and the poor effectiveness of most existing object detection methods. Firstly, an operation site safety supervision system was designed based on the power Internet of Things architecture, and efficient image processing was achieved through cloud edge collaboration. Then, point cloud data and on‐site monitoring information are used on the edge side to extract the accessible area, ensuring that the target is located in a safe area. Finally, the YOLO algorithm is improved in the cloud by using clustering algorithms, network structure optimization, and other methods, and used to detect targets and determine whether their behaviour meets the safety requirements of the operation site. Based on the PyTorch deep learning framework, the proposed method was experimentally demonstrated, and the results showed that its average detection accuracy and time were 94.53% and 68 ms, respectively, providing technical support for achieving remote monitoring of power operation sites.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"58 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139395550","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}
He Wang, Bowen Zhou, Fabin Li, Xingming Ma, Yujie Gao, Hao Yang
In view of the existing literature, only the optimization operation of hydrogen energy–wind/light new energy is considered, and the research problems such as comprehensive utilization of water resources and load control strategies are ignored. Based on the analysis of load characteristics of offshore wind power, this paper has established an optimal operation model of power‐fresh water energy system based on “wind‐hydrogen‐water‐electricity” interaction. Meanwhile, an electrolytic hydrogen individual load control strategy is proposed to match wind power fluctuations from the perspective of internal load regulation of electrolytic hydrogen system. From the economic characteristics, operation characteristics, accommodation situation and other simulation analysis, it can reduce the total operating cost by about 3.1%, improve the utilization rate of electrolytic cell capacity, and meet the water demand of coastal users. It shows that the individual control strategy and optimal operation model have advantages, which is of great significance for realizing low‐carbon, safe, and economical operation of power grid in the future.
{"title":"Optimal operation of electric–freshwater energy system considering load regulation strategy of individual hydrogen electrolyzer","authors":"He Wang, Bowen Zhou, Fabin Li, Xingming Ma, Yujie Gao, Hao Yang","doi":"10.1049/tje2.12345","DOIUrl":"https://doi.org/10.1049/tje2.12345","url":null,"abstract":"In view of the existing literature, only the optimization operation of hydrogen energy–wind/light new energy is considered, and the research problems such as comprehensive utilization of water resources and load control strategies are ignored. Based on the analysis of load characteristics of offshore wind power, this paper has established an optimal operation model of power‐fresh water energy system based on “wind‐hydrogen‐water‐electricity” interaction. Meanwhile, an electrolytic hydrogen individual load control strategy is proposed to match wind power fluctuations from the perspective of internal load regulation of electrolytic hydrogen system. From the economic characteristics, operation characteristics, accommodation situation and other simulation analysis, it can reduce the total operating cost by about 3.1%, improve the utilization rate of electrolytic cell capacity, and meet the water demand of coastal users. It shows that the individual control strategy and optimal operation model have advantages, which is of great significance for realizing low‐carbon, safe, and economical operation of power grid in the future.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"126 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453572","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}
Using a syringe pump setup, the authors conducted water flow experiments through porous reduced graphene oxide (rGO). A variety of anions and cations were added to the water to study its effect on energy harvesting. More specifically, the authors performed tests to study effect of: (1) ion concentration in water, (2) type of anion used, (3) type of cation used, and (4) effect of flow rate. The test data indicates that water flow through rGO networks can directly induce drift of charge carriers in graphene and thus generate electricity. Graphene is ideally suited for this application, since it possesses high mobility charge carriers that are ready to be coupled to moving ions present in the flowing fluid. The proposed rGO material could enable harvesting of the ubiquitous, abundant, and renewable mechanical energy of moving water directly to electrical energy. Unlike traditional schemes, the graphene material directly converts the flow energy into electrical energy without the need for moving parts. Such graphene coatings could potentially replace conventional batteries (which are environmentally hazardous) in low‐power, low‐voltage, and long service‐life applications. Once scaled up, this concept offers a potentially transformative approach to energy harvesting, as opposed to incremental advances in current technologies.
{"title":"Energy harvesting from water flow through porous reduced graphene oxide networks","authors":"R. A. Panchal, Nikhil Koratkar","doi":"10.1049/tje2.12338","DOIUrl":"https://doi.org/10.1049/tje2.12338","url":null,"abstract":"Using a syringe pump setup, the authors conducted water flow experiments through porous reduced graphene oxide (rGO). A variety of anions and cations were added to the water to study its effect on energy harvesting. More specifically, the authors performed tests to study effect of: (1) ion concentration in water, (2) type of anion used, (3) type of cation used, and (4) effect of flow rate. The test data indicates that water flow through rGO networks can directly induce drift of charge carriers in graphene and thus generate electricity. Graphene is ideally suited for this application, since it possesses high mobility charge carriers that are ready to be coupled to moving ions present in the flowing fluid. The proposed rGO material could enable harvesting of the ubiquitous, abundant, and renewable mechanical energy of moving water directly to electrical energy. Unlike traditional schemes, the graphene material directly converts the flow energy into electrical energy without the need for moving parts. Such graphene coatings could potentially replace conventional batteries (which are environmentally hazardous) in low‐power, low‐voltage, and long service‐life applications. Once scaled up, this concept offers a potentially transformative approach to energy harvesting, as opposed to incremental advances in current technologies.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139540248","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}
Mohammad Hemayati, Abdolreza Nematollahi, E. Nikooee, G. Habibagahi, Ali Niazi
As the world's population grows, there is an increasing need for soil improvement techniques to accommodate construction demands. Current methods, most often, suffer from a high CO2 footprint, leading researchers to resort to biological methods of soil improvement through microbially induced carbonate precipitation (MICP). Commonly used ureolytic microbial carbonate precipitation produces ammonium ions, which can be environmentally concerning. The present study, therefore, addresses the use of non‐ureolytic MICP for soil improvement. The process of non‐ureolytic MICP relies on the use of heterotrophic bacteria to catalyze the oxidation reaction of organic compounds, eventually calcium carbonate precipitation. In this study, heterotrophic bacteria, such as Bacillus subtilis and Bacillus amyloliquefaciens, have been investigated as a solution for soil improvement via an ammonium‐free MICP. Calcium formate and calcium acetate are used as both calcium and carbon sources. This study, furthermore, examines the impact of MICP treatment on sandy soil and the effect of compaction level on treated samples. The findings indicate that the non‐ureolytic MICP method is an effective approach for stabilizing sand. The Calcium Formate‐B.Subtilis composition is shown to be the most effective compound for improving the unconfined compressive strength of sandy soils, while the Calcium Acetate‐B.Amyloliquefaciens composition is the least effective.
{"title":"Non‐ureolytic microbially induced carbonate precipitation: Investigating a cleaner biogeotechnical engineering pathway for soil mechanical improvement","authors":"Mohammad Hemayati, Abdolreza Nematollahi, E. Nikooee, G. Habibagahi, Ali Niazi","doi":"10.1049/tje2.12350","DOIUrl":"https://doi.org/10.1049/tje2.12350","url":null,"abstract":"As the world's population grows, there is an increasing need for soil improvement techniques to accommodate construction demands. Current methods, most often, suffer from a high CO2 footprint, leading researchers to resort to biological methods of soil improvement through microbially induced carbonate precipitation (MICP). Commonly used ureolytic microbial carbonate precipitation produces ammonium ions, which can be environmentally concerning. The present study, therefore, addresses the use of non‐ureolytic MICP for soil improvement. The process of non‐ureolytic MICP relies on the use of heterotrophic bacteria to catalyze the oxidation reaction of organic compounds, eventually calcium carbonate precipitation. In this study, heterotrophic bacteria, such as Bacillus subtilis and Bacillus amyloliquefaciens, have been investigated as a solution for soil improvement via an ammonium‐free MICP. Calcium formate and calcium acetate are used as both calcium and carbon sources. This study, furthermore, examines the impact of MICP treatment on sandy soil and the effect of compaction level on treated samples. The findings indicate that the non‐ureolytic MICP method is an effective approach for stabilizing sand. The Calcium Formate‐B.Subtilis composition is shown to be the most effective compound for improving the unconfined compressive strength of sandy soils, while the Calcium Acetate‐B.Amyloliquefaciens composition is the least effective.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140518791","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}
This paper introduces a comprehensive performance evaluation algorithm explicitly designed for secondary equipment in substations, specifically targeting the relay protection system. In contrast to the current evaluation systems, this novel method navigates the complex internal interconnections and mechanisms inherent within secondary system equipment. Such complications have previously impeded the accuracy and breadth of evaluations, thereby limiting the degree of precision and innovation attainable within substations. The proposed approach effectively integrates the improved Analytic Hierarchy Process entropy weight (IAHP‐EW) method with the Learning Vector Quantization (LVQ) neural network. Initially, the IAHP‐EW method identified the comprehensive evaluation indicators and their corresponding weights for relay protection devices. Following weight allocation, these evaluation indicators are scrutinized and computed utilizing the multivariate regression analysis algorithm, resulting in performance evaluation outcomes for the relay protection system. These outcomes are subsequently classified and utilized in training the LVQ neural network, promoting the network's capacity to autonomously evaluate the performance status of the relay protection system. To corroborate the viability and effectiveness of this proposed performance evaluation and prediction algorithm, empirical operating data from a local substation is used. The results suggest a significant improvement in the evaluation accuracy of secondary equipment performance, indicating potential for practical application and a valuable contribution to the field through the introduction of a novel approach to performance assessment of substation relay protection systems.
{"title":"A comprehensive performance evaluation algorithm for substation secondary equipment: An improved analytic hierarchy process entropy weight and learning vector quantization neural network approach","authors":"Wei Wang, Jianfei Zhang, Sai Wang, Xuewei Chen","doi":"10.1049/tje2.12347","DOIUrl":"https://doi.org/10.1049/tje2.12347","url":null,"abstract":"This paper introduces a comprehensive performance evaluation algorithm explicitly designed for secondary equipment in substations, specifically targeting the relay protection system. In contrast to the current evaluation systems, this novel method navigates the complex internal interconnections and mechanisms inherent within secondary system equipment. Such complications have previously impeded the accuracy and breadth of evaluations, thereby limiting the degree of precision and innovation attainable within substations. The proposed approach effectively integrates the improved Analytic Hierarchy Process entropy weight (IAHP‐EW) method with the Learning Vector Quantization (LVQ) neural network. Initially, the IAHP‐EW method identified the comprehensive evaluation indicators and their corresponding weights for relay protection devices. Following weight allocation, these evaluation indicators are scrutinized and computed utilizing the multivariate regression analysis algorithm, resulting in performance evaluation outcomes for the relay protection system. These outcomes are subsequently classified and utilized in training the LVQ neural network, promoting the network's capacity to autonomously evaluate the performance status of the relay protection system. To corroborate the viability and effectiveness of this proposed performance evaluation and prediction algorithm, empirical operating data from a local substation is used. The results suggest a significant improvement in the evaluation accuracy of secondary equipment performance, indicating potential for practical application and a valuable contribution to the field through the introduction of a novel approach to performance assessment of substation relay protection systems.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"18 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139456877","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}
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