Yiming Xu, Ziheng Ding, Wang Li, Kai Zhang, Le Tong
In the process of steel production, the defects on the surface of steel will adversely affect the subsequent processing of a product. Accurate detection of such defects is the key to improve production efficiency and economic benefits. In this paper, an end-to-end steel surface defect detection and size measurement system based on the YOLOv5 model is designed. Firstly, in consideration of the defect location and direction correlation in the production process, a coordinate attention mechanism is added at the head of YOLOv5 to strengthen the spatial correlation of the steel surface and an adaptive anchor box generation method based on defect shape difference feature is proposed, which realizes the detection of three main types of defects on the Pytorch deep learning framework. Secondly, BiFPN is used to strengthen the feature fusion and a transformer encoder is added to improve the performance of detecting small defects. Thirdly, calculate the conversion ratio between the pixel and the actual size according to the standard reference specimen and obtain the actual size through the pixel statistics of the defect area to achieve pixel level size measurement. Finally, the steel surface defect detection and size measurement system are designed in this paper, which consist of various hardware, related measurement, and detection algorithms. According to the experimental results, the comprehensive defect detection accuracy of this method reaches 93.6%, of which the scratch detection accuracy reaches 95.7%. The detection speed reaches 133 fps and the defect size measurement accuracy reaches 0.5 mm. Experimental result shows that the defect detection and size measurement system designed in this paper can accurately detect and measure various industrial production defects and can be applied to the actual production process.
{"title":"The Steel Surface Multiple Defect Detection and Size Measurement System Based on Improved YOLOv5","authors":"Yiming Xu, Ziheng Ding, Wang Li, Kai Zhang, Le Tong","doi":"10.1155/2023/5399616","DOIUrl":"https://doi.org/10.1155/2023/5399616","url":null,"abstract":"In the process of steel production, the defects on the surface of steel will adversely affect the subsequent processing of a product. Accurate detection of such defects is the key to improve production efficiency and economic benefits. In this paper, an end-to-end steel surface defect detection and size measurement system based on the YOLOv5 model is designed. Firstly, in consideration of the defect location and direction correlation in the production process, a coordinate attention mechanism is added at the head of YOLOv5 to strengthen the spatial correlation of the steel surface and an adaptive anchor box generation method based on defect shape difference feature is proposed, which realizes the detection of three main types of defects on the Pytorch deep learning framework. Secondly, BiFPN is used to strengthen the feature fusion and a transformer encoder is added to improve the performance of detecting small defects. Thirdly, calculate the conversion ratio between the pixel and the actual size according to the standard reference specimen and obtain the actual size through the pixel statistics of the defect area to achieve pixel level size measurement. Finally, the steel surface defect detection and size measurement system are designed in this paper, which consist of various hardware, related measurement, and detection algorithms. According to the experimental results, the comprehensive defect detection accuracy of this method reaches 93.6%, of which the scratch detection accuracy reaches 95.7%. The detection speed reaches 133 fps and the defect size measurement accuracy reaches 0.5 mm. Experimental result shows that the defect detection and size measurement system designed in this paper can accurately detect and measure various industrial production defects and can be applied to the actual production process.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73249385","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}
Ahmed Hamed Ahmed Adam, Jiawei Chen, S. Kamel, H. Z. Meymand
This study offers a thorough examination of the zero voltage switching (ZVS) operation range and deadband conditions for a bidirectional DC-DC converter with phase shift control, featuring dual H-bridge. The analysis considers the soft switching range of the DAB converter, accounting for the impact of the headband and the ZVS capacitor. By applying the differential equation of the circuit during deadband time, a sufficient constraint for the input and output bridges can be calculated. The findings indicate that as the output voltage increases, the minimum phase shift value required to achieve ZVS decreases, and expanding the phase shift value will expand the ZVS range and reduce switching losses. The study provides simulation results for various operating conditions, validating the theoretical analysis of the proposed system. In addition, the results furnish information about the circuit behavior during the deadband and waveforms. Finally, MATLAB/SIMULINK verifies the simulation results for different operating stages.
{"title":"Comprehensive Analysis of ZVS Operation Range and Deadband Conditions of a Dual H-Bridge Bidirectional DC-DC Converter with Phase Shift Control","authors":"Ahmed Hamed Ahmed Adam, Jiawei Chen, S. Kamel, H. Z. Meymand","doi":"10.1155/2023/8882417","DOIUrl":"https://doi.org/10.1155/2023/8882417","url":null,"abstract":"This study offers a thorough examination of the zero voltage switching (ZVS) operation range and deadband conditions for a bidirectional DC-DC converter with phase shift control, featuring dual H-bridge. The analysis considers the soft switching range of the DAB converter, accounting for the impact of the headband and the ZVS capacitor. By applying the differential equation of the circuit during deadband time, a sufficient constraint for the input and output bridges can be calculated. The findings indicate that as the output voltage increases, the minimum phase shift value required to achieve ZVS decreases, and expanding the phase shift value will expand the ZVS range and reduce switching losses. The study provides simulation results for various operating conditions, validating the theoretical analysis of the proposed system. In addition, the results furnish information about the circuit behavior during the deadband and waveforms. Finally, MATLAB/SIMULINK verifies the simulation results for different operating stages.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73331005","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}
A. O. Triqadafi, T. N. Zafirah, H. Dharmawan, S. Sakti
A frequency counter is essential for resonance-based sensors like quartz crystal microbalance. An electronic nose or tongue using a QCM sensor array requires a multichannel frequency counter to detect the frequency shift of the sensors simultaneously. The frequency counter’s resolution, precision, and sampling speed are important factors. Board size, energy consumption, and rapid deployment are also considered in the design. This work shows the development of an independent multichannel frequency counter using a commercial Xilinx Spartan 6 series XC6SLX9 board module and a microcontroller board. Both modules are general-purpose modules; therefore, there is no need for a printed circuit board design, resulting in a quick implementation: the use of FPGA results in a compact size and low energy consumption. The developed counter is designed based on a reciprocal counter utilizing the internal logic block of the FPGA. The FPGA module has a built-in 50 MHz TCXO clock and is the reference clock. The high-resolution timing of the counter is realized by multiplying the 50 MHz clock by 6 to reach 300 MHz. The multiplication utilizes the PLL modules in the FPGA. The high precision and accuracy of the counter are achieved by calibrating the timing clock to a 10 MHz rubidium oscillator. The data communication to the microcontroller is done via the SPI by implementing the SPI protocol in the FPGA. The resource is optimized by utilizing PLL and DSP blocks for the counter. Only 5% registers and 5% LUTs of the FPGA resource are used to build a four-channel frequency counter. The result shows that the counter can measure the frequency of incoming signals with a resolution of 0.033 Hz at 10 MHz with a sampling time of 1 second. The system has been tested to monitor the frequency changes of a QCM sensor array.
{"title":"Four-Channels High-Resolution Frequency Counter for QCM Sensor Array Using Generic FPGA XC6SLX9 Board","authors":"A. O. Triqadafi, T. N. Zafirah, H. Dharmawan, S. Sakti","doi":"10.1155/2023/5182455","DOIUrl":"https://doi.org/10.1155/2023/5182455","url":null,"abstract":"A frequency counter is essential for resonance-based sensors like quartz crystal microbalance. An electronic nose or tongue using a QCM sensor array requires a multichannel frequency counter to detect the frequency shift of the sensors simultaneously. The frequency counter’s resolution, precision, and sampling speed are important factors. Board size, energy consumption, and rapid deployment are also considered in the design. This work shows the development of an independent multichannel frequency counter using a commercial Xilinx Spartan 6 series XC6SLX9 board module and a microcontroller board. Both modules are general-purpose modules; therefore, there is no need for a printed circuit board design, resulting in a quick implementation: the use of FPGA results in a compact size and low energy consumption. The developed counter is designed based on a reciprocal counter utilizing the internal logic block of the FPGA. The FPGA module has a built-in 50 MHz TCXO clock and is the reference clock. The high-resolution timing of the counter is realized by multiplying the 50 MHz clock by 6 to reach 300 MHz. The multiplication utilizes the PLL modules in the FPGA. The high precision and accuracy of the counter are achieved by calibrating the timing clock to a 10 MHz rubidium oscillator. The data communication to the microcontroller is done via the SPI by implementing the SPI protocol in the FPGA. The resource is optimized by utilizing PLL and DSP blocks for the counter. Only 5% registers and 5% LUTs of the FPGA resource are used to build a four-channel frequency counter. The result shows that the counter can measure the frequency of incoming signals with a resolution of 0.033 Hz at 10 MHz with a sampling time of 1 second. The system has been tested to monitor the frequency changes of a QCM sensor array.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88064817","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}
Aiming at the problems of high cleaning intensity, low efficiency, and hidden safety hazards of high-altitude curtain walls, this study proposes that the image processing method is a kind of image processing technology in human-computer collaborative visual design. The algorithm uses generalized mapping to scramble the picture and then expands and replaces the scrambled pictures one by one through the image processing technical support system. Studies have shown that this calculation method has mixed pixel values, good diffusion performance, and strong resistance performance. The pixel distribution of the processed image is relatively random, and the features of similar loudness are not relevant. It is proved through experiments that the above calculation methods have strong safety performance.
{"title":"Design of Image Processing Technology Support System in Human-Computer Collaborative Visual Design Assisted by Artificial Intelligence Technology","authors":"Yanbin Song","doi":"10.1155/2023/9363644","DOIUrl":"https://doi.org/10.1155/2023/9363644","url":null,"abstract":"Aiming at the problems of high cleaning intensity, low efficiency, and hidden safety hazards of high-altitude curtain walls, this study proposes that the image processing method is a kind of image processing technology in human-computer collaborative visual design. The algorithm uses generalized mapping to scramble the picture and then expands and replaces the scrambled pictures one by one through the image processing technical support system. Studies have shown that this calculation method has mixed pixel values, good diffusion performance, and strong resistance performance. The pixel distribution of the processed image is relatively random, and the features of similar loudness are not relevant. It is proved through experiments that the above calculation methods have strong safety performance.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86757850","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}
Mengxuan Li, Jingshan Han, Zhi Yang, Bin Zhao, Peng Liu
Pins are essential connecting components in power transmission lines. Their extensive use yet leads to frequent defects. Given the small size of a pin and many similar components, the detection of such defects is not ideal, which is a technological problem in the identification and diagnosis of power defects. In response to the large size, complex background, and on-site requirements, such as real-time detection, of power transmission lines, this paper proposes a method to detect pin defects based on TPH-MobileNetv3 (Transformer prediction Head Mobilenetv3). This paper modifies and adds a self-attention layer to MobilNetV3-Small to improve the feature extraction capability of small targets after downsampling. A feature fusion structure with layers of self-attention and a convolutional block attention module (CBAM) is added to the neck network, and a transformer prediction head are added to the head network so that different scale characteristics can be fused and focused from space and channels to strengthen the detection of small targets. Compared with the traditional MobileNetV3, the detection accuracy of the algorithm in this paper has been raised by 24%, as shown in the detection results of measured data. Moreover, compared with the mainstream algorithms with the same detection accuracy, this algorithm not only reduces the model size and significantly enhances detection efficiency but also satisfies the requirement of edge image processing of power inspection.
引脚是输电线路中必不可少的连接部件。它们的广泛使用导致了频繁的缺陷。由于引脚尺寸小，同类元件多，对此类缺陷的检测并不理想，这是电源缺陷识别与诊断中的技术难题。针对输电线路规模大、背景复杂、实时检测等现场要求，本文提出了一种基于TPH-MobileNetv3 (Transformer prediction Head Mobilenetv3)的管脚缺陷检测方法。本文对MobilNetV3-Small进行了修改，增加了自关注层，提高了下采样后小目标的特征提取能力。在颈部网络中加入具有多层自注意和卷积块注意模块(CBAM)的特征融合结构，在头部网络中加入变压器预测头，从空间和通道上融合和聚焦不同尺度特征，加强对小目标的检测。与传统的MobileNetV3相比，本文算法的检测精度提高了24%，如实测数据的检测结果所示。此外，与具有相同检测精度的主流算法相比，该算法不仅减小了模型尺寸，显著提高了检测效率，而且满足了电力检测边缘图像处理的要求。
{"title":"Detection of the Pin Defects of Power Transmission Lines Based on Improved TPH-MobileNetv3","authors":"Mengxuan Li, Jingshan Han, Zhi Yang, Bin Zhao, Peng Liu","doi":"10.1155/2023/7192814","DOIUrl":"https://doi.org/10.1155/2023/7192814","url":null,"abstract":"Pins are essential connecting components in power transmission lines. Their extensive use yet leads to frequent defects. Given the small size of a pin and many similar components, the detection of such defects is not ideal, which is a technological problem in the identification and diagnosis of power defects. In response to the large size, complex background, and on-site requirements, such as real-time detection, of power transmission lines, this paper proposes a method to detect pin defects based on TPH-MobileNetv3 (Transformer prediction Head Mobilenetv3). This paper modifies and adds a self-attention layer to MobilNetV3-Small to improve the feature extraction capability of small targets after downsampling. A feature fusion structure with layers of self-attention and a convolutional block attention module (CBAM) is added to the neck network, and a transformer prediction head are added to the head network so that different scale characteristics can be fused and focused from space and channels to strengthen the detection of small targets. Compared with the traditional MobileNetV3, the detection accuracy of the algorithm in this paper has been raised by 24%, as shown in the detection results of measured data. Moreover, compared with the mainstream algorithms with the same detection accuracy, this algorithm not only reduces the model size and significantly enhances detection efficiency but also satisfies the requirement of edge image processing of power inspection.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78784286","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}
Aiming at the problems of high delay and vulnerable to network attack in the traditional microgrid centralized architecture, a collaborative microgrid security defense method in the edge-computing environment is proposed. First, we build the edge-computing framework for microgrid, deploy the edge-computing server near the equipment terminal to improve the data processing efficiency, and deploy the blockchain in the edge server to ensure the reliability of the system. Then, the fully homomorphic encryption algorithm is used to design the smart contract, and the secure sharing of information is ensured through identity authentication, data encryption call, and so on. Finally, the credibility model is integrated into the election algorithm and is used to build a trusted edge cooperation mechanism to further improve the ability of the system to defend against network attacks. Based on the microgrid model, the experimental demonstration of the proposed method is carried out. The results show that when subjected to a network attack, the current fluctuation range is small and the defense success rate exceeds 95%, which is better than other methods and can better meet the requirements of practical application.
{"title":"A Microgrid Security Defense Method Based on Cooperation in an Edge-Computing Environment","authors":"Jian Shang, Runmin Guan, Changlu Shen","doi":"10.1155/2023/1856876","DOIUrl":"https://doi.org/10.1155/2023/1856876","url":null,"abstract":"Aiming at the problems of high delay and vulnerable to network attack in the traditional microgrid centralized architecture, a collaborative microgrid security defense method in the edge-computing environment is proposed. First, we build the edge-computing framework for microgrid, deploy the edge-computing server near the equipment terminal to improve the data processing efficiency, and deploy the blockchain in the edge server to ensure the reliability of the system. Then, the fully homomorphic encryption algorithm is used to design the smart contract, and the secure sharing of information is ensured through identity authentication, data encryption call, and so on. Finally, the credibility model is integrated into the election algorithm and is used to build a trusted edge cooperation mechanism to further improve the ability of the system to defend against network attacks. Based on the microgrid model, the experimental demonstration of the proposed method is carried out. The results show that when subjected to a network attack, the current fluctuation range is small and the defense success rate exceeds 95%, which is better than other methods and can better meet the requirements of practical application.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74324261","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}
Wenwen Chang, Wenchao Nie, Yueting Yuan, Yuchan Zhang, Renjie Lv, Lei Zheng, Guanghui Yan
Based on the brain signals, decoding and analyzing the gait features to make a reliable prediction of action intention are the core issues in the brain computer interface (BCI)-based hybrid rehabilitation and intelligent walking aid robot system. In order to realize the classification and recognition of the most basic gait processes such as standing, sitting, and quiet, this paper proposes a feature representation method based on the signal complexity and entropy of each brain region. Through the statistical analysis of these parameters between different conditions, these characteristics which sensitive to different actions are determined as a feature vector, and the classification and recognition of these actions are completed by combing support vector machine, linear discriminant analysis, and logistic regression. Experimental results show the proposed method can better realize the recognition of the aforementioned action intention. The recognition accuracy of standing, sitting, and quiet of 13 subjects is higher than 80.9%, and the highest one can reach 86.8%. Directed dynamic brain network analysis of the 8 brain regions shows that the occurrence of lower limb movement will weaken the dependence between brain regions, resulting in the weakening of network topological connection. The result has significant value for understanding human’s brain cognitive characteristics in the process of lower limb movement and carrying out the study of BCI based strategy and system for lower limb rehabilitation.
{"title":"Sitting and Standing Intention Detection Based on Dynamical Region Connectivity and Entropy of EEG","authors":"Wenwen Chang, Wenchao Nie, Yueting Yuan, Yuchan Zhang, Renjie Lv, Lei Zheng, Guanghui Yan","doi":"10.1155/2023/1587725","DOIUrl":"https://doi.org/10.1155/2023/1587725","url":null,"abstract":"Based on the brain signals, decoding and analyzing the gait features to make a reliable prediction of action intention are the core issues in the brain computer interface (BCI)-based hybrid rehabilitation and intelligent walking aid robot system. In order to realize the classification and recognition of the most basic gait processes such as standing, sitting, and quiet, this paper proposes a feature representation method based on the signal complexity and entropy of each brain region. Through the statistical analysis of these parameters between different conditions, these characteristics which sensitive to different actions are determined as a feature vector, and the classification and recognition of these actions are completed by combing support vector machine, linear discriminant analysis, and logistic regression. Experimental results show the proposed method can better realize the recognition of the aforementioned action intention. The recognition accuracy of standing, sitting, and quiet of 13 subjects is higher than 80.9%, and the highest one can reach 86.8%. Directed dynamic brain network analysis of the 8 brain regions shows that the occurrence of lower limb movement will weaken the dependence between brain regions, resulting in the weakening of network topological connection. The result has significant value for understanding human’s brain cognitive characteristics in the process of lower limb movement and carrying out the study of BCI based strategy and system for lower limb rehabilitation.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77704217","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}
Yuhong Ouyang, Man Li, Wenqian Kang, Xiangbei Che, Ruixian Ye
Distribution network security situation awareness refers to the process of perception, understanding, and state projection of systems, elements, and environmental factors within a certain space-time volume. Security situation awareness is an important part of the security assessment of the distribution network. In response to the rapid and accurate distribution network security situation awareness requirements, this paper proposes a distribution network security situation awareness method based the distribution network topology layered model. First, a hierarchical model of the distribution network topology under the premise of optimizing the location of the synchronous phasor measuring device is constructed. This model can quickly capture the system security situation elements. Then, a support vector data description algorithm fused with information entropy is used to realize the identification and understanding of abnormal information in the security situation elements of the distribution network. The long- and short-term memory network is then used to predict the operation trend of the distribution network under normal operation and fault disturbance. Finally, a simulation is established, and the IEEE-33 distribution network model is used to verify the effectiveness of the method proposed in this paper. The results show that the method of this paper improves the speed and accuracy of obtaining the security situation elements of the distribution network, shortens the identification time of the security situation elements, and realizes the security situation awareness of the nodes of the distribution network.
{"title":"Distribution Network Security Situation Awareness Method Based on the Distribution Network Topology Layered Model","authors":"Yuhong Ouyang, Man Li, Wenqian Kang, Xiangbei Che, Ruixian Ye","doi":"10.1155/2023/6775337","DOIUrl":"https://doi.org/10.1155/2023/6775337","url":null,"abstract":"Distribution network security situation awareness refers to the process of perception, understanding, and state projection of systems, elements, and environmental factors within a certain space-time volume. Security situation awareness is an important part of the security assessment of the distribution network. In response to the rapid and accurate distribution network security situation awareness requirements, this paper proposes a distribution network security situation awareness method based the distribution network topology layered model. First, a hierarchical model of the distribution network topology under the premise of optimizing the location of the synchronous phasor measuring device is constructed. This model can quickly capture the system security situation elements. Then, a support vector data description algorithm fused with information entropy is used to realize the identification and understanding of abnormal information in the security situation elements of the distribution network. The long- and short-term memory network is then used to predict the operation trend of the distribution network under normal operation and fault disturbance. Finally, a simulation is established, and the IEEE-33 distribution network model is used to verify the effectiveness of the method proposed in this paper. The results show that the method of this paper improves the speed and accuracy of obtaining the security situation elements of the distribution network, shortens the identification time of the security situation elements, and realizes the security situation awareness of the nodes of the distribution network.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82211476","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}
In view of the low efficiency of the traditional manual evaluation method of substation equipment status under the background of complex environment, a panoramic evaluation method of substation equipment health status based on multisource monitoring and deep convolution neural network under edge computing architecture is proposed. Firstly, a panoramic sensing system for substation equipment is built based on edge computing, and an edge computing server is deployed in the substation to process the massive data obtained from multisource monitoring nearby. Then, the improved YOLOv4 network is used to detect the equipment state in the substation, in which the Squeeze-and-Excitation attention module and deep separable convolution are used to optimize the YOLOv4 network. Finally, based on the status image of substation equipment, the health status of equipment is evaluated on the panoramic platform of substation combined with the characteristics of multisource data, and four states are divided according to the evaluation criteria. Based on the selected dataset, the experimental analysis of the proposed method is carried out. The results show that the index values of accuracy, recall, and mean precision are 91.53%, 93.07%, and 92.28%, respectively. The overall performance is better than other methods and has certain application value.
{"title":"Panoramic Assessment Method of Substation Equipment Health Status Based on Multisource Monitoring and Deep Convolution Neural Network under Edge Computing Architecture","authors":"Zhu-xing Ma, Li-shuo Zhang, Hao Gu, Zi-zhong Xin, Zhe Kang, Zhao-lei Wang","doi":"10.1155/2023/9194712","DOIUrl":"https://doi.org/10.1155/2023/9194712","url":null,"abstract":"In view of the low efficiency of the traditional manual evaluation method of substation equipment status under the background of complex environment, a panoramic evaluation method of substation equipment health status based on multisource monitoring and deep convolution neural network under edge computing architecture is proposed. Firstly, a panoramic sensing system for substation equipment is built based on edge computing, and an edge computing server is deployed in the substation to process the massive data obtained from multisource monitoring nearby. Then, the improved YOLOv4 network is used to detect the equipment state in the substation, in which the Squeeze-and-Excitation attention module and deep separable convolution are used to optimize the YOLOv4 network. Finally, based on the status image of substation equipment, the health status of equipment is evaluated on the panoramic platform of substation combined with the characteristics of multisource data, and four states are divided according to the evaluation criteria. Based on the selected dataset, the experimental analysis of the proposed method is carried out. The results show that the index values of accuracy, recall, and mean precision are 91.53%, 93.07%, and 92.28%, respectively. The overall performance is better than other methods and has certain application value.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75843352","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}
H. Kumawat, M. Bhadu, Arvind Kumar, O. Mahela, B. Khan, Divya Anand, Jose Breñosa
The load frequency control (LFC) is a most important tool for the frequency regulation mechanism in the widely spread modern power system. The LFC system consists of a communication structure to transmit the measurement and control signal. Usually the controllers in LFC systems are designed and implemented in continuous mode of operation. This article investigates the discrete mode load frequency control (LFC) mechanism, by employing the concept of the periodic output feedback (POF)-based controller with varying input and output sampling frequencies. Both the optimal sampling frequency and the optimal POF controller gain matrix are found by using the particle swarm optimization (PSO) method. The POF-based controller is intended for usage in two-area multisource LFC systems, with varying input and output sampling frequencies. The performance analysis takes into account a variety of scenarios, including those without a conventional stabilizer, with conventional continuous and corresponding discrete mode PSS, and a proposed discrete mode POF controller. Furthermore, the efficacy of the discrete mode POF controller is evaluated on the MATLAB/Simulink platform.
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