Measurement Sensors最新文献

英文中文

Application of edge computing technology in smart grid data security

Q4 Engineering

Measurement Sensors

Pub Date : 2025-02-01 DOI: 10.1016/j.measen.2024.101412

Zhuo Cheng, Jiangxin Li, Jianjun Zhang, Chen Wang, Hui Wang, Juyin Wu

In order to solve the problem that the two-way flow of power and information between user nodes and service nodes in the smart grid poses a huge threat to the privacy and security of user data, and at the same time, the limitation of the power bureau's computing resources also brings users response delay, service quality degradation and other problems, the author proposes the application of edge computing technology in smart grid data security. Combining with edge computing technology, the author proposes a proxy blind signcryption scheme based on certificateless without bilinear mapping. By blinding the power and information, the signcrypter can not know the specific power consumption information of the user, so as to ensure the data privacy and security of the user. Implement forward security using proxy key update mechanism and perform batch verification of user signature ciphertext. The experimental results indicate that: The total running time required for executing proxy authorization and verification, proxy key generation, signature and decryption algorithms in this scheme is 5.617 ms, with a ciphertext length of 80 Bytes. Compared with other existing literature, the maximum reduction is 85.6 % and 86 %, respectively.

Conclusion

This scheme is more suitable for protecting data security and privacy in the data transmission process of smart grids due to its lower running time and communication cost.

{"title":"Application of edge computing technology in smart grid data security","authors":"Zhuo Cheng, Jiangxin Li, Jianjun Zhang, Chen Wang, Hui Wang, Juyin Wu","doi":"10.1016/j.measen.2024.101412","DOIUrl":"10.1016/j.measen.2024.101412","url":null,"abstract":"<div><div>In order to solve the problem that the two-way flow of power and information between user nodes and service nodes in the smart grid poses a huge threat to the privacy and security of user data, and at the same time, the limitation of the power bureau's computing resources also brings users response delay, service quality degradation and other problems, the author proposes the application of edge computing technology in smart grid data security. Combining with edge computing technology, the author proposes a proxy blind signcryption scheme based on certificateless without bilinear mapping. By blinding the power and information, the signcrypter can not know the specific power consumption information of the user, so as to ensure the data privacy and security of the user. Implement forward security using proxy key update mechanism and perform batch verification of user signature ciphertext. The experimental results indicate that: The total running time required for executing proxy authorization and verification, proxy key generation, signature and decryption algorithms in this scheme is 5.617 ms, with a ciphertext length of 80 Bytes. Compared with other existing literature, the maximum reduction is 85.6 % and 86 %, respectively.</div></div><div><h3>Conclusion</h3><div>This scheme is more suitable for protecting data security and privacy in the data transmission process of smart grids due to its lower running time and communication cost.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101412"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143145249","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}

引用次数: 0

Collaboration of clustering and classification techniques for better prediction of severity of heart stroke using deep learning

Q4 Engineering

Measurement Sensors

Pub Date : 2025-02-01 DOI: 10.1016/j.measen.2024.101405

T. Swathi Priyadarshini, Mohd Abdul Hameed

Our research aims to present a comprehensive study of machine learning algorithms and deep learning advancements in medical field systems for decision making. Present study examines the idea of extracting most important risk factors from given medical data, which has major impact in the increase of severity condition of heart stroke. Three experimental prediction models are developed when k-means clustering is collaborated with classification which includes machine learning algorithms like Naïve Bayes, Decision Tree and a deep learning algorithm Artificial Neural Network. A detailed comparison analysis is done by evaluating performance metrics like sensitivity, specificity, accuracy, and AUC-ROC scores. Out of the three, k-means with Artificial Neural Network model outperformed with sensitivity 0.89, specificity 0.89, and accuracy of 0.90 in comparison with machine learning classifiers. The challenges of perfect balancing of sensitivity and specificity is achieved by AUC-ROC score of 0.96, which is the best possible result till now.

引用次数: 0

Enhanced defect sensing technology in turbid water environments using multi-beam sonar

Q4 Engineering

Measurement Sensors

Pub Date : 2025-02-01 DOI: 10.1016/j.measen.2024.101805

Wenhui Wang, Yikai Li, Rufei He, Yao Li

In this paper, we report a novel defect perception technology utilizing multi-beam sonar for applications in turbid water environments. Our goal is to improve the precision and speed of identifying target image defects. We categorize the target image recognition dataset following specific guidelines and devise a target image imaging model customized for the distinct characteristics of turbid water settings. By employing the weighted time average (WMT) algorithm, we calculate the time window for each beam within the water environment. Moreover, we utilize the phase difference sequence method to enhance target image details in turbid water, and leverage the time of arrival (TOA) estimation method to suppress background noise and sidelobes. Through the implementation of a dynamic detection threshold, our technology facilitates defect perception in turbid water environments using multi-beam sonar. Experimental results demonstrate that this method achieves an accuracy of 96.05 % in recognizing image defects in turbid water environments, significantly enhancing both the accuracy and efficiency of defect recognition. It also overcomes the typical challenges of underwater detection in turbid and low-light conditions.

{"title":"Enhanced defect sensing technology in turbid water environments using multi-beam sonar","authors":"Wenhui Wang, Yikai Li, Rufei He, Yao Li","doi":"10.1016/j.measen.2024.101805","DOIUrl":"10.1016/j.measen.2024.101805","url":null,"abstract":"<div><div>In this paper, we report a novel defect perception technology utilizing multi-beam sonar for applications in turbid water environments. Our goal is to improve the precision and speed of identifying target image defects. We categorize the target image recognition dataset following specific guidelines and devise a target image imaging model customized for the distinct characteristics of turbid water settings. By employing the weighted time average (WMT) algorithm, we calculate the time window for each beam within the water environment. Moreover, we utilize the phase difference sequence method to enhance target image details in turbid water, and leverage the time of arrival (TOA) estimation method to suppress background noise and sidelobes. Through the implementation of a dynamic detection threshold, our technology facilitates defect perception in turbid water environments using multi-beam sonar. Experimental results demonstrate that this method achieves an accuracy of 96.05 % in recognizing image defects in turbid water environments, significantly enhancing both the accuracy and efficiency of defect recognition. It also overcomes the typical challenges of underwater detection in turbid and low-light conditions.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101805"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143145245","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}

引用次数: 0

Pneumonia detection from X-ray images using federated learning–An unsupervised learning approach

Q4 Engineering

Measurement Sensors

Pub Date : 2025-02-01 DOI: 10.1016/j.measen.2024.101410

Neeta Rana , Hitesh Marwaha

The emergence of advanced data analysis techniques has revolutionized patient healthcare by enabling the early and efficient detection of diseases. Traditionally, disease identification relied solely on the expertise of medical professionals. However, limitations in physician availability, particularly in resource-constrained regions, can hinder timely diagnosis. Fortunately, data analysis techniques are now widely employed to address a multitude of medical disease detection. This paper presents a novel Pneumonia disease detection model by analyzing the chest X-ray data. The development of robust diagnostic tools faces a critical challenge: the lack of access to large, labeled training datasets. This challenge arises because of privacy concerns about medical data. This research proposes a solution that tackles both data scarcity and privacy concerns. It leverages an unsupervised learning model trained on decentralized datasets. The unsupervised learning approach used is an Autoencoder neural network, and the decentralized learning technique used for model training is Federated Learning. The proposed approach trains the model on data residing at multiple locations, such as healthcare institutions, without compromising patient privacy. The datasets used to train the proposed model consist of chest X-ray images of pneumonia patients and healthy individuals. It offers satisfactory performance when compared to other existing Pneumonia detection models. In similar studies, medical institutions can collaborate and develop efficient medical tools without breaching patients’ data privacy.

{"title":"Pneumonia detection from X-ray images using federated learning–An unsupervised learning approach","authors":"Neeta Rana , Hitesh Marwaha","doi":"10.1016/j.measen.2024.101410","DOIUrl":"10.1016/j.measen.2024.101410","url":null,"abstract":"<div><div>The emergence of advanced data analysis techniques has revolutionized patient healthcare by enabling the early and efficient detection of diseases. Traditionally, disease identification relied solely on the expertise of medical professionals. However, limitations in physician availability, particularly in resource-constrained regions, can hinder timely diagnosis. Fortunately, data analysis techniques are now widely employed to address a multitude of medical disease detection. This paper presents a novel Pneumonia disease detection model by analyzing the chest X-ray data. The development of robust diagnostic tools faces a critical challenge: the lack of access to large, labeled training datasets. This challenge arises because of privacy concerns about medical data. This research proposes a solution that tackles both data scarcity and privacy concerns. It leverages an unsupervised learning model trained on decentralized datasets. The unsupervised learning approach used is an Autoencoder neural network, and the decentralized learning technique used for model training is Federated Learning. The proposed approach trains the model on data residing at multiple locations, such as healthcare institutions, without compromising patient privacy. The datasets used to train the proposed model consist of chest X-ray images of pneumonia patients and healthy individuals. It offers satisfactory performance when compared to other existing Pneumonia detection models. In similar studies, medical institutions can collaborate and develop efficient medical tools without breaching patients’ data privacy.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101410"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143856","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}

引用次数: 0

Spatiotemporal data modeling and prediction algorithms in intelligent management systems

Q4 Engineering

Measurement Sensors

Pub Date : 2025-02-01 DOI: 10.1016/j.measen.2024.101411

Xin Cao, Chunxiao Mei, Zhiyong Song, Hao Li, Jingtao Chang, Zhihao Feng

In order to solve the problem of difficulty in learning semantic pattern representations between user dynamic interest sequences using path based and knowledge graph based entity embedding methods, the author proposes research on spatiotemporal data modeling and prediction algorithms in intelligent management systems. The author first makes a preliminary analysis of the wireless network data (mainly the data of cellular mobile networks) obtained by Internet service providers, reveals that the data of adjacent base stations have temporal and spatial correlations, then establishes a hybrid deep learning model for spatio-temporal prediction, and proposes a new spatial model training algorithm. Finally, experiments were conducted using wireless network datasets to evaluate the performance of the model. The experimental results show that based on data analysis, it can be seen that the prediction of the system has effectively improved by 99 %.

Conclusion

The spatiotemporal data modeling and prediction algorithm proposed by the author in the intelligent management system significantly improves prediction accuracy.

{"title":"Spatiotemporal data modeling and prediction algorithms in intelligent management systems","authors":"Xin Cao, Chunxiao Mei, Zhiyong Song, Hao Li, Jingtao Chang, Zhihao Feng","doi":"10.1016/j.measen.2024.101411","DOIUrl":"10.1016/j.measen.2024.101411","url":null,"abstract":"<div><div>In order to solve the problem of difficulty in learning semantic pattern representations between user dynamic interest sequences using path based and knowledge graph based entity embedding methods, the author proposes research on spatiotemporal data modeling and prediction algorithms in intelligent management systems. The author first makes a preliminary analysis of the wireless network data (mainly the data of cellular mobile networks) obtained by Internet service providers, reveals that the data of adjacent base stations have temporal and spatial correlations, then establishes a hybrid deep learning model for spatio-temporal prediction, and proposes a new spatial model training algorithm. Finally, experiments were conducted using wireless network datasets to evaluate the performance of the model. The experimental results show that based on data analysis, it can be seen that the prediction of the system has effectively improved by 99 %.</div></div><div><h3>Conclusion</h3><div>The spatiotemporal data modeling and prediction algorithm proposed by the author in the intelligent management system significantly improves prediction accuracy.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101411"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143145241","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}

引用次数: 0

Integration and performance optimization of gallium nitride barrier micro nano electromechanical systems in intelligent agricultural sensor networks

Q4 Engineering

Measurement Sensors

Pub Date : 2025-01-25 DOI: 10.1016/j.measen.2025.101814

Ji Gu

This study aims to explore the application of gallium nitride (GaN) barrier in hardware optimization of micro nano scale electromechanical systems (MEMS), particularly its ability to overcome the limitations of traditional hardware in high-temperature and low-power environments. Through multidimensional experiments such as static testing, dynamic testing, and temperature testing, comprehensively evaluate the performance of gallium nitride barrier based microelectromechanical systems in intelligent agricultural sensor networks. The static test results indicate that with the increase of external force, the electrical response of the system shows a significant improvement, and there is a high linear correlation between external force and electrical response (R² = 0.987). In dynamic testing, as the vibration frequency increased from 1Hz to 1000Hz, the electrical response gradually strengthened, demonstrating the system's good adaptability under high-frequency vibration. However, power consumption also increases with the increase of vibration frequency. The temperature test results show that there is a positive correlation between electrical response and power consumption in the temperature range of −20 °C to 60 °C, indicating that gallium nitride technology can effectively improve the sensitivity, stability, and adaptability of micro nano scale electromechanical systems under different environmental conditions. Research has shown that the application of gallium nitride barrier technology in smart agricultural sensor networks has significant performance optimization potential, especially in extreme temperature and low-power requirements, which can provide more reliable and efficient sensing solutions for smart agriculture.

{"title":"Integration and performance optimization of gallium nitride barrier micro nano electromechanical systems in intelligent agricultural sensor networks","authors":"Ji Gu","doi":"10.1016/j.measen.2025.101814","DOIUrl":"10.1016/j.measen.2025.101814","url":null,"abstract":"<div><div>This study aims to explore the application of gallium nitride (GaN) barrier in hardware optimization of micro nano scale electromechanical systems (MEMS), particularly its ability to overcome the limitations of traditional hardware in high-temperature and low-power environments. Through multidimensional experiments such as static testing, dynamic testing, and temperature testing, comprehensively evaluate the performance of gallium nitride barrier based microelectromechanical systems in intelligent agricultural sensor networks. The static test results indicate that with the increase of external force, the electrical response of the system shows a significant improvement, and there is a high linear correlation between external force and electrical response (R<sup>2</sup> = 0.987). In dynamic testing, as the vibration frequency increased from 1Hz to 1000Hz, the electrical response gradually strengthened, demonstrating the system's good adaptability under high-frequency vibration. However, power consumption also increases with the increase of vibration frequency. The temperature test results show that there is a positive correlation between electrical response and power consumption in the temperature range of −20 °C to 60 °C, indicating that gallium nitride technology can effectively improve the sensitivity, stability, and adaptability of micro nano scale electromechanical systems under different environmental conditions. Research has shown that the application of gallium nitride barrier technology in smart agricultural sensor networks has significant performance optimization potential, especially in extreme temperature and low-power requirements, which can provide more reliable and efficient sensing solutions for smart agriculture.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101814"},"PeriodicalIF":0.0,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471560","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}

引用次数: 0

Research on parameter identification and fault prediction method of hydraulic system in intelligent sensing agriculture

Q4 Engineering

Measurement Sensors

Pub Date : 2025-01-25 DOI: 10.1016/j.measen.2025.101813

Wenbo Liu, Jiaheng Zheng, Guangdong Shi, Qingshu Yuan, Yongping Lu

This study aims to explore the application of deep learning techniques, particularly optimized long short-term memory networks (LSTM), in the diagnosis of hydraulic system faults and parameter recognition in intelligent sensing agriculture. Firstly, the hydraulic system was modeled and the key parameters and state variables in the model were identified. Next, the LSTM network is introduced to optimize the model through its unique internal structure. LSTM can effectively capture long-term dependencies in time series data, making it an ideal choice for handling hydraulic systems involving dynamic behavior. To evaluate the performance of the model, 2000 data points were collected and preprocessed, of which 1897 data points were used for experiments. Based on these data, model performance was tested under different operating conditions. The research results show that the optimized LSTM model performs well in parameter recognition and fault diagnosis, especially under standard operating conditions, with a relative error rate of only 1.5 %. Considering different operating conditions and fault modes, the proposed model demonstrates good robustness and practicality in hydraulic system fault diagnosis, especially with an accuracy of over 90 % in leakage fault diagnosis, and remains stable under various operating conditions. This study provides strong support for the application of deep learning technology in hydraulic system fault diagnosis, and valuable insights for the performance optimization and application expansion of future models.

{"title":"Research on parameter identification and fault prediction method of hydraulic system in intelligent sensing agriculture","authors":"Wenbo Liu, Jiaheng Zheng, Guangdong Shi, Qingshu Yuan, Yongping Lu","doi":"10.1016/j.measen.2025.101813","DOIUrl":"10.1016/j.measen.2025.101813","url":null,"abstract":"<div><div>This study aims to explore the application of deep learning techniques, particularly optimized long short-term memory networks (LSTM), in the diagnosis of hydraulic system faults and parameter recognition in intelligent sensing agriculture. Firstly, the hydraulic system was modeled and the key parameters and state variables in the model were identified. Next, the LSTM network is introduced to optimize the model through its unique internal structure. LSTM can effectively capture long-term dependencies in time series data, making it an ideal choice for handling hydraulic systems involving dynamic behavior. To evaluate the performance of the model, 2000 data points were collected and preprocessed, of which 1897 data points were used for experiments. Based on these data, model performance was tested under different operating conditions. The research results show that the optimized LSTM model performs well in parameter recognition and fault diagnosis, especially under standard operating conditions, with a relative error rate of only 1.5 %. Considering different operating conditions and fault modes, the proposed model demonstrates good robustness and practicality in hydraulic system fault diagnosis, especially with an accuracy of over 90 % in leakage fault diagnosis, and remains stable under various operating conditions. This study provides strong support for the application of deep learning technology in hydraulic system fault diagnosis, and valuable insights for the performance optimization and application expansion of future models.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101813"},"PeriodicalIF":0.0,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471581","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}

引用次数: 0

Research on the application of intelligent sensors based on the Internet of Things in fault diagnosis of mechanical and electrical equipment

Q4 Engineering

Measurement Sensors

Pub Date : 2025-01-17 DOI: 10.1016/j.measen.2025.101811

Lingli Yao

The purpose of this work to do is to solve the fault diagnosis of agricultural mechanical and electrical equipment and guarantee the smooth operation of production line and industrial process. The research begins by collecting operational data from electromechanical equipment based on Internet of Things (IoT) technology and utilizes Narrowband Internet of Things (NB-IoT) modules to achieve communication for terminal electromechanical devices. Subsequently, the Kernel Extreme Learning Machine (KELM) is introduced and combined with the Whale Optimization algorithm to construct a fault diagnosis model based on the Whale Optimization Kernel Extreme Learning Machine (WOKELM). Finally, the performance of the model is experimentally evaluated. The results indicate that, compared to other baseline algorithms, the proposed model algorithm achieves Accuracy values exceeding 90 %, with at least a 3.85 % improvement over the KELM baseline algorithm. Additionally, in the training a.nd testing sets, the F1 values of the proposed model algorithm reach 91.24 % and 85.85 %, respectively, which is at least 2.98 % higher than other model algorithms. Furthermore, an analysis of fault diagnosis error rates reveals that the Root Mean Squared Error (RMSE) for fault diagnosis is below 4.13 %. Therefore, the proposed fault diagnosis model demonstrates excellent performance in terms of accuracy and precision, providing robust support for improving the intelligence and accuracy of fault diagnosis in electromechanical equipment.

{"title":"Research on the application of intelligent sensors based on the Internet of Things in fault diagnosis of mechanical and electrical equipment","authors":"Lingli Yao","doi":"10.1016/j.measen.2025.101811","DOIUrl":"10.1016/j.measen.2025.101811","url":null,"abstract":"<div><div>The purpose of this work to do is to solve the fault diagnosis of agricultural mechanical and electrical equipment and guarantee the smooth operation of production line and industrial process. The research begins by collecting operational data from electromechanical equipment based on Internet of Things (IoT) technology and utilizes Narrowband Internet of Things (NB-IoT) modules to achieve communication for terminal electromechanical devices. Subsequently, the Kernel Extreme Learning Machine (KELM) is introduced and combined with the Whale Optimization algorithm to construct a fault diagnosis model based on the Whale Optimization Kernel Extreme Learning Machine (WOKELM). Finally, the performance of the model is experimentally evaluated. The results indicate that, compared to other baseline algorithms, the proposed model algorithm achieves Accuracy values exceeding 90 %, with at least a 3.85 % improvement over the KELM baseline algorithm. Additionally, in the training a.nd testing sets, the F1 values of the proposed model algorithm reach 91.24 % and 85.85 %, respectively, which is at least 2.98 % higher than other model algorithms. Furthermore, an analysis of fault diagnosis error rates reveals that the Root Mean Squared Error (RMSE) for fault diagnosis is below 4.13 %. Therefore, the proposed fault diagnosis model demonstrates excellent performance in terms of accuracy and precision, providing robust support for improving the intelligence and accuracy of fault diagnosis in electromechanical equipment.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101811"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422744","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}

引用次数: 0

Research on performance optimization of agricultural intelligent energy meters driven by intelligent sensors under overload conditions

Q4 Engineering

Measurement Sensors

Pub Date : 2025-01-17 DOI: 10.1016/j.measen.2025.101812

Chengfei Qi, Yan Liu, Yaoyu Wang, Chaoran Bi, Wenwen Li

During the actual operation of smart energy meters used in agriculture, there may be situations where current overload (greater than Imax) occurs. Some smart energy meters used in agriculture may experience power reduction or even reverse operation during overload operation. When the current returns to the measurement range, the energy meter is still in an abnormal state. This article starts from the case of on-site operation failure of intelligent energy meters for agriculture, simulates the overflow effect in ADC filters and metering chips, explains the principles of the above two phenomena, and provides solutions. Meanwhile, the correctness of the solution method was verified through experimental data.Corresponding guidance has been provided to provincial power companies regarding the performance requirements of energy meters after overload.

引用次数: 0

Application of electrical nonlinear load harmonic analysis method integrating intelligent sensor data in intelligent agricultural power management

Q4 Engineering

Measurement Sensors

Pub Date : 2025-01-17 DOI: 10.1016/j.measen.2025.101810

Jilei Qu, Meiying Niu, Qing Lin, Yanyan Li

In intelligent agricultural power management, the impact of harmonics on the power grid and its operating equipment cannot be ignored. The location of harmonic sources and the amplitude of harmonics injected into the power grid have significant randomness and nonlinearity. In order to accurately locate harmonic sources in the power grid, this paper proposes a method for detecting and locating harmonic sources based on nonlinear loads. This method constructs a judgment network by utilizing the load characteristics of each bus connected to the common connection point (PCC) and the characteristics of each type of load when running separately as training samples, and uses this standard to determine the position of the harmonic source, thereby achieving accurate localization of the harmonic source. In the experiment based on Matlab 2014a simulation platform, the results showed that adding the load characteristic data measured at PCC point in real-time operation to the judgment network can effectively determine the position of the harmonic source. Multiple load tests have shown that the judgment network has high accuracy. The experimental results show that among the 10 samples to be tested, only 2 load samples had misjudgments in their bus positions. In summary, the judgment network based on nonlinear loads can accurately detect and locate the location of harmonic sources in the power grid, and by increasing the number of training data sets, the judgment accuracy can be further improved. Therefore, this method, combined with intelligent sensor data, has high engineering application value for detecting and locating harmonic sources in intelligent agricultural power management.

{"title":"Application of electrical nonlinear load harmonic analysis method integrating intelligent sensor data in intelligent agricultural power management","authors":"Jilei Qu, Meiying Niu, Qing Lin, Yanyan Li","doi":"10.1016/j.measen.2025.101810","DOIUrl":"10.1016/j.measen.2025.101810","url":null,"abstract":"<div><div>In intelligent agricultural power management, the impact of harmonics on the power grid and its operating equipment cannot be ignored. The location of harmonic sources and the amplitude of harmonics injected into the power grid have significant randomness and nonlinearity. In order to accurately locate harmonic sources in the power grid, this paper proposes a method for detecting and locating harmonic sources based on nonlinear loads. This method constructs a judgment network by utilizing the load characteristics of each bus connected to the common connection point (PCC) and the characteristics of each type of load when running separately as training samples, and uses this standard to determine the position of the harmonic source, thereby achieving accurate localization of the harmonic source. In the experiment based on Matlab 2014a simulation platform, the results showed that adding the load characteristic data measured at PCC point in real-time operation to the judgment network can effectively determine the position of the harmonic source. Multiple load tests have shown that the judgment network has high accuracy. The experimental results show that among the 10 samples to be tested, only 2 load samples had misjudgments in their bus positions. In summary, the judgment network based on nonlinear loads can accurately detect and locate the location of harmonic sources in the power grid, and by increasing the number of training data sets, the judgment accuracy can be further improved. Therefore, this method, combined with intelligent sensor data, has high engineering application value for detecting and locating harmonic sources in intelligent agricultural power management.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101810"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422745","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}

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Measurement Sensors

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀