Saeid Eslahi Tatafi, Mohammad Ataei, Mohsen Ekramian
This paper proposes a robust distributed observer for uncertain systems where some unknown, unstructured, and bounded uncertainties exist in system matrices. In distributed observer, each node includes an observer which directly estimates a part of the system states by its limited measurements and further, the estimation of other states indirectly obtains via exchanging information with neighbouring nodes. It is shown that whole states convergence achieves if the LTI system is observable as well as the network graph is strongly connected. Also, in the proposed distributed observer, by applying a new term in dynamic equation, the robust performance is achieved in dealing with uncertainties in system matrices. The observer gains synthesis is then formulated in terms of linear matrix inequalities. Finally, the simulation results are presented to illustrate the effectiveness of the proposed distributed observer to handle system uncertainties.
{"title":"Robust distributed observer for uncertain systems","authors":"Saeid Eslahi Tatafi, Mohammad Ataei, Mohsen Ekramian","doi":"10.1049/tje2.12413","DOIUrl":"https://doi.org/10.1049/tje2.12413","url":null,"abstract":"This paper proposes a robust distributed observer for uncertain systems where some unknown, unstructured, and bounded uncertainties exist in system matrices. In distributed observer, each node includes an observer which directly estimates a part of the system states by its limited measurements and further, the estimation of other states indirectly obtains via exchanging information with neighbouring nodes. It is shown that whole states convergence achieves if the LTI system is observable as well as the network graph is strongly connected. Also, in the proposed distributed observer, by applying a new term in dynamic equation, the robust performance is achieved in dealing with uncertainties in system matrices. The observer gains synthesis is then formulated in terms of linear matrix inequalities. Finally, the simulation results are presented to illustrate the effectiveness of the proposed distributed observer to handle system uncertainties.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141840533","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}
Abdul Basit, H. Manzoor, Muhammad Akram, H. Gelani, Sajjad Hussain
A continuous supply of electricity is necessary to maintain an acceptable standard of life, and the power distribution system's overhead line components play a crucial role in this matter. In Pakistan, identifying defective parts often necessitates human involvement. An unmanned aerial vehicle was used to gather a collection of 10,343 photos to automate this procedure. Using supervised and unsupervised machine learning methods, a number of automated anomaly detection systems were created. Support vector machine, random forest, VGG16, and ResNet50 were used as supervised machine learning models, and a convolutional auto‐encoder was used as the unsupervised machine learning model. VGG16 achieved the best accuracy of 99.00% while random forest achieved the worst accuracy of 72.49%. The convolutional auto‐encoder was successful in distinguishing between normal and abnormal components. The aforementioned machine learning models can be put on unmanned aerial vehicles to immediately identify defective parts.
{"title":"Machine learning‐assisted anomaly detection for power line components: A case study in Pakistan","authors":"Abdul Basit, H. Manzoor, Muhammad Akram, H. Gelani, Sajjad Hussain","doi":"10.1049/tje2.12405","DOIUrl":"https://doi.org/10.1049/tje2.12405","url":null,"abstract":"A continuous supply of electricity is necessary to maintain an acceptable standard of life, and the power distribution system's overhead line components play a crucial role in this matter. In Pakistan, identifying defective parts often necessitates human involvement. An unmanned aerial vehicle was used to gather a collection of 10,343 photos to automate this procedure. Using supervised and unsupervised machine learning methods, a number of automated anomaly detection systems were created. Support vector machine, random forest, VGG16, and ResNet50 were used as supervised machine learning models, and a convolutional auto‐encoder was used as the unsupervised machine learning model. VGG16 achieved the best accuracy of 99.00% while random forest achieved the worst accuracy of 72.49%. The convolutional auto‐encoder was successful in distinguishing between normal and abnormal components. The aforementioned machine learning models can be put on unmanned aerial vehicles to immediately identify defective parts.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"31 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141702331","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}
Xiaobin Li, Ye Li, Feng Wang, Zhiming Huang, Pan Yao, Baoguo Li
In the process of tension wire construction of transmission lines, the key state quantities such as tension, inclination and spatial position of the traction walking plate directly affect the construction safety. At present, the construction personnel use visual observation to obtain the state quantity of traction walking plate in the process of tension wire construction. This monitoring method is not only inefficient, and there are personal subjective factors of judgment errors. For this reason, this paper develops a kind of intelligent traction walking plate for tension wire construction, based on the principle of resistance strain type and gyroscope to obtain the tension and inclination of the traction walking plate, respectively, using two front cameras and two rear cameras to obtain the spatial position of the traction walking plate, and finally completing the real‐time transmission of the monitoring data through the Lora technology. The developed intelligent traction walking plate was applied in Shandong Heze Yongfeng 220 kV transmission line project on 11 October 2020, which improved the construction efficiency by 15.71% while ensuring the construction safety.
{"title":"Development of intelligent traction walking plate for tension overhead construction","authors":"Xiaobin Li, Ye Li, Feng Wang, Zhiming Huang, Pan Yao, Baoguo Li","doi":"10.1049/tje2.12418","DOIUrl":"https://doi.org/10.1049/tje2.12418","url":null,"abstract":"In the process of tension wire construction of transmission lines, the key state quantities such as tension, inclination and spatial position of the traction walking plate directly affect the construction safety. At present, the construction personnel use visual observation to obtain the state quantity of traction walking plate in the process of tension wire construction. This monitoring method is not only inefficient, and there are personal subjective factors of judgment errors. For this reason, this paper develops a kind of intelligent traction walking plate for tension wire construction, based on the principle of resistance strain type and gyroscope to obtain the tension and inclination of the traction walking plate, respectively, using two front cameras and two rear cameras to obtain the spatial position of the traction walking plate, and finally completing the real‐time transmission of the monitoring data through the Lora technology. The developed intelligent traction walking plate was applied in Shandong Heze Yongfeng 220 kV transmission line project on 11 October 2020, which improved the construction efficiency by 15.71% while ensuring the construction safety.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"33 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141849104","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}
J. Khodaparast, O. B. Fosso, M. Molinas, J. A. Suul
Power system stability characteristics are typically evaluated in terms of small‐ and large‐signal (transient) stability. Access to the time‐varying A‐matrix of a state‐space‐based power systems model during transient conditions can be utilized to apply linear time‐varying system concepts for large‐signal stability analysis. In linear time‐varying system analysis, the differential Riccati equation (DRE) plays a vital role when the power system is subjected to a severe disturbance. The Möbius transformation is proposed in this paper to solve the DRE with singularity issues. It is shown that the solution of the DREs follows a specific mathematical pattern when the power system is stable but does not follow this pattern when the system progresses toward instability. The proposed method can be used in large‐signal stability analysis to predict instability and make the stability analysis more efficient. Additionally, the vector‐DRE is proposed to generalize the index in a large‐scale power system. Results show that analyzing the corresponding Riccati equation's behaviour can help researchers predict a power system's performance and improve the control and management of the system.
电力系统稳定性特征通常以小信号和大信号（暂态）稳定性进行评估。利用基于状态空间的电力系统模型在暂态条件下的时变 A 矩阵，可将线性时变系统概念用于大信号稳定性分析。在线性时变系统分析中，当电力系统受到严重扰动时，微分里卡提方程 (DRE) 起着至关重要的作用。本文提出了莫比乌斯变换来求解具有奇异性问题的 DRE。结果表明，当电力系统稳定时，DRE 的解遵循特定的数学模式，但当系统趋于不稳定时，则不遵循这一模式。所提出的方法可用于大信号稳定性分析，预测不稳定性，使稳定性分析更有效。此外，还提出了矢量-DRE，以在大规模电力系统中推广该指标。结果表明，分析相应的 Riccati 方程行为有助于研究人员预测电力系统的性能，并改善系统的控制和管理。
{"title":"Power system instability prediction from the solution pattern of differential Riccati equations","authors":"J. Khodaparast, O. B. Fosso, M. Molinas, J. A. Suul","doi":"10.1049/tje2.12414","DOIUrl":"https://doi.org/10.1049/tje2.12414","url":null,"abstract":"Power system stability characteristics are typically evaluated in terms of small‐ and large‐signal (transient) stability. Access to the time‐varying A‐matrix of a state‐space‐based power systems model during transient conditions can be utilized to apply linear time‐varying system concepts for large‐signal stability analysis. In linear time‐varying system analysis, the differential Riccati equation (DRE) plays a vital role when the power system is subjected to a severe disturbance. The Möbius transformation is proposed in this paper to solve the DRE with singularity issues. It is shown that the solution of the DREs follows a specific mathematical pattern when the power system is stable but does not follow this pattern when the system progresses toward instability. The proposed method can be used in large‐signal stability analysis to predict instability and make the stability analysis more efficient. Additionally, the vector‐DRE is proposed to generalize the index in a large‐scale power system. Results show that analyzing the corresponding Riccati equation's behaviour can help researchers predict a power system's performance and improve the control and management of the system.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"34 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141838963","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 this study, efforts were made to incorporate the influence of discontinuities and failure modes of rock into the classification of rock masses. The past tectonic activities may create microfractures in the rock body therefore the failure moods have been determined carefully under uniaxial compression. The results of the discontinuity analysis, conducted through kinematic study, highlighted the significant impact of wedge failure on the failure of the rock mass. In correlating the geological strength index with rock mass rating, it was observed that joint volume played a negative role, whereas compressive strength played a positive role. These correlations are particularly applicable for a certain rock type, as the compressive strength is inherently dependent on the type of rock. The analysis of failure modes under uniaxial compression reveals that the dissipation energy coefficient initially undergoes rapid increase before reaching its minimum value at the failure stage. The microstructures of the rock effect significantly the elastic and dissipation energy characteristics. Specifically, the axial splitting failure mode emerges as predominant. Given the area's past tectonic activity, these results emphasize the impact of microfractures within the rock body. Relating the failure criteria with the chemical composition of rock types reveals that rocks abundant in SiO2, such as gabbronorite, tend to exhibit brittle failure. Additionally, a dominance of Al2O3 over Fe2O3 suggests a predisposition towards brittle failure, while an increased ratio of CaO to MgO implies increased susceptibility to compression.
在这项研究中，我们努力将不连续性和岩石破坏模式的影响纳入岩体分类中。过去的构造活动可能会在岩体中产生微裂缝，因此在单轴压缩条件下仔细确定了破坏模式。通过运动学研究进行的不连续性分析结果表明，楔形破坏对岩体破坏具有重要影响。在将地质强度指数与岩体等级相关联时，发现节理体积起负作用，而抗压强度起正作用。这些相关性尤其适用于某种岩石类型，因为抗压强度本质上取决于岩石类型。对单轴压缩下破坏模式的分析表明，耗散能量系数最初会迅速增加，然后在破坏阶段达到最小值。岩石的微观结构对弹性和耗能特性有显著影响。具体而言，轴向劈裂破坏模式占主导地位。鉴于该地区过去的构造活动，这些结果强调了岩体内部微裂缝的影响。将破坏标准与岩石类型的化学成分联系起来可以发现，二氧化硅含量丰富的岩石（如辉绿岩）倾向于表现出脆性破坏。此外，Al2O3 多于 Fe2O3 表明岩石易发生脆性破坏，而 CaO 与 MgO 的比率增加则意味着岩石更易受到挤压。
{"title":"Influence of chemical composition and discontinuities on energy transformation and rock mass behaviour: Insights into geological dynamic","authors":"Naeem Abbas, Kegang Li, Yewuhalashet Fissha, Zemicael Gebrehiwot, Hajime Ikeda, Mujahid Ali, Hisatoshi Toriya, Tsuyoshi Adachi, Youhei Kawamura","doi":"10.1049/tje2.12388","DOIUrl":"https://doi.org/10.1049/tje2.12388","url":null,"abstract":"In this study, efforts were made to incorporate the influence of discontinuities and failure modes of rock into the classification of rock masses. The past tectonic activities may create microfractures in the rock body therefore the failure moods have been determined carefully under uniaxial compression. The results of the discontinuity analysis, conducted through kinematic study, highlighted the significant impact of wedge failure on the failure of the rock mass. In correlating the geological strength index with rock mass rating, it was observed that joint volume played a negative role, whereas compressive strength played a positive role. These correlations are particularly applicable for a certain rock type, as the compressive strength is inherently dependent on the type of rock. The analysis of failure modes under uniaxial compression reveals that the dissipation energy coefficient initially undergoes rapid increase before reaching its minimum value at the failure stage. The microstructures of the rock effect significantly the elastic and dissipation energy characteristics. Specifically, the axial splitting failure mode emerges as predominant. Given the area's past tectonic activity, these results emphasize the impact of microfractures within the rock body. Relating the failure criteria with the chemical composition of rock types reveals that rocks abundant in SiO2, such as gabbronorite, tend to exhibit brittle failure. Additionally, a dominance of Al2O3 over Fe2O3 suggests a predisposition towards brittle failure, while an increased ratio of CaO to MgO implies increased susceptibility to compression.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"50 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141036729","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 large consumption of computational resources and insufficient data feature extraction in the current partial discharge fault identification process of GIS equipment, a partial discharge fault identification method of GIS equipment based on improved deep learning is proposed. Firstly, the audio information of GIS equipment is filtered by a simple power normalised cepstral coefficient (SPNCC). Secondly, the spatial correlation between audio data streams is obtained by a convolutional neural network, the temporal correlation of audio is obtained and the next time slice data stream is predicted by using bi‐directional long short‐term memory (BiLSTM) network, and the attention mechanism is designed to extract deeper data features. Finally, the partial discharge fault identification model of GIS equipment based on improved SPNCC‐CNN‐BiLSTM‐Multi‐att is established, which improves the accuracy of the partial discharge identification method of GIS equipment. Experiments show that when the number of iterations is 100, the accuracy, recall, and F1 value of the proposed GIS equipment partial discharge fault recognition method on the dataset are 0.876, 0.812, and 0.843, respectively.
{"title":"Partial discharge fault identification method for GIS equipment based on improved deep learning","authors":"Weitao Hu, Jianpeng Li, Xiaofei Liu, Guang Li","doi":"10.1049/tje2.12386","DOIUrl":"https://doi.org/10.1049/tje2.12386","url":null,"abstract":"Aiming at the problems of large consumption of computational resources and insufficient data feature extraction in the current partial discharge fault identification process of GIS equipment, a partial discharge fault identification method of GIS equipment based on improved deep learning is proposed. Firstly, the audio information of GIS equipment is filtered by a simple power normalised cepstral coefficient (SPNCC). Secondly, the spatial correlation between audio data streams is obtained by a convolutional neural network, the temporal correlation of audio is obtained and the next time slice data stream is predicted by using bi‐directional long short‐term memory (BiLSTM) network, and the attention mechanism is designed to extract deeper data features. Finally, the partial discharge fault identification model of GIS equipment based on improved SPNCC‐CNN‐BiLSTM‐Multi‐att is established, which improves the accuracy of the partial discharge identification method of GIS equipment. Experiments show that when the number of iterations is 100, the accuracy, recall, and F1 value of the proposed GIS equipment partial discharge fault recognition method on the dataset are 0.876, 0.812, and 0.843, respectively.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"1973 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141027797","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}
Debayani Mishra, M. K. Maharana, Manoj Kumar Kar, Anurekha Nayak, Md. Minarul Islam, T. Ustun
Modern electrical networks, particularly microgrids, have seen a sharp rise in acquiring non‐conventional sources. The inertia of the microgrid decreases drastically because RESs have been used in place of traditional synchronous generators. The reduced inertia negatively impacts the dynamics and performance of the microgrid with RESs, which decreases the microgrid's stability, especially in operation on an island. The primary purpose of this research study is to enhance the dynamic security of an island microgrid by merging an electric vehicle with a frequency control technique based on virtual inertia control. A Proportional Integral Derivative Filter Constant (PIDFN) controller optimally created using the Modified Differential Evolution (MDE) method served as the foundation for control in the virtual inertia control loop. The effectiveness of the MDE‐based PIDFN controller was examined considering the diverse operational scenarios are compared and contrasted with those of traditional methods Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO)‐based PIDFN controllers. Real‐time wind and solar power statistics and random load fluctuations were incorporated to provide realistic simulation settings. The outcomes demonstrate that the MDE‐based PIDFN controller performs better in reference frequency tracking and reducing frequency disturbances than the other optimization strategies.
{"title":"A metaheuristic algorithm for regulating virtual inertia of a standalone microgrid incorporating electric vehicles","authors":"Debayani Mishra, M. K. Maharana, Manoj Kumar Kar, Anurekha Nayak, Md. Minarul Islam, T. Ustun","doi":"10.1049/tje2.12383","DOIUrl":"https://doi.org/10.1049/tje2.12383","url":null,"abstract":"Modern electrical networks, particularly microgrids, have seen a sharp rise in acquiring non‐conventional sources. The inertia of the microgrid decreases drastically because RESs have been used in place of traditional synchronous generators. The reduced inertia negatively impacts the dynamics and performance of the microgrid with RESs, which decreases the microgrid's stability, especially in operation on an island. The primary purpose of this research study is to enhance the dynamic security of an island microgrid by merging an electric vehicle with a frequency control technique based on virtual inertia control. A Proportional Integral Derivative Filter Constant (PIDFN) controller optimally created using the Modified Differential Evolution (MDE) method served as the foundation for control in the virtual inertia control loop. The effectiveness of the MDE‐based PIDFN controller was examined considering the diverse operational scenarios are compared and contrasted with those of traditional methods Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO)‐based PIDFN controllers. Real‐time wind and solar power statistics and random load fluctuations were incorporated to provide realistic simulation settings. The outcomes demonstrate that the MDE‐based PIDFN controller performs better in reference frequency tracking and reducing frequency disturbances than the other optimization strategies.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141026850","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}
Zhun Cheng, Mai Xu, Wenjing Yi, Dan Li, Bing Luo, Yang Zhang
A switching‐angle‐based hybrid modulation method of full wind speed and low carrier wave ratio for direct‐drive permanent‐magnet wind power generation system is proposed here, which order to improve the control performance of wind turbines in the full wind speed range. The combination manner of the proposed method is based on the harmonic distortion rate of the stator current, a modulation mode with a higher carrier wave ratio is adopted within the maximum switching frequency allowed by the system, the switching angle from optimized synchronous modulation also is introduced. In order to solve the problem of current impact caused by switching different modulation modes, the switching strategy between different modulation modes is developed from the perspective of harmonic current. And the sectors are redivided for the problem of sudden change in carrier wave ratio during switching, which improve the control effect of permanent‐magnet synchronous wind generator when changing the modulations modes. Finally, the feasibility and correctness of the proposed method are verified by theoretical analysis and simulation.
{"title":"Switching‐angle‐based hybrid modulation method of full wind speed and low carrier wave ratio for direct‐drive permanent‐magnet wind power generation system","authors":"Zhun Cheng, Mai Xu, Wenjing Yi, Dan Li, Bing Luo, Yang Zhang","doi":"10.1049/tje2.12385","DOIUrl":"https://doi.org/10.1049/tje2.12385","url":null,"abstract":"A switching‐angle‐based hybrid modulation method of full wind speed and low carrier wave ratio for direct‐drive permanent‐magnet wind power generation system is proposed here, which order to improve the control performance of wind turbines in the full wind speed range. The combination manner of the proposed method is based on the harmonic distortion rate of the stator current, a modulation mode with a higher carrier wave ratio is adopted within the maximum switching frequency allowed by the system, the switching angle from optimized synchronous modulation also is introduced. In order to solve the problem of current impact caused by switching different modulation modes, the switching strategy between different modulation modes is developed from the perspective of harmonic current. And the sectors are redivided for the problem of sudden change in carrier wave ratio during switching, which improve the control effect of permanent‐magnet synchronous wind generator when changing the modulations modes. Finally, the feasibility and correctness of the proposed method are verified by theoretical analysis and simulation.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"56 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141046068","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. Shigrekar, Jiangkai Peng, Temitayo O. Olowu, Fernando Gallego Dias, Tyler Westover
Traditionally, nuclear power plants in the U.S. provide baseload power to the power grid because they have less flexibility for ramping their output power than natural gas peaking plants. However, achieving climate goals to reduce the consumption of fossil‐based natural gas places pressure on nuclear power plants and other power generators to ramp up their power output to balance grid generation with demand. This paper presents the modelling and performance analysis of a nuclear reactor system (NRS) coupled to a liquid‐metal battery (LMB) to improve its dynamic response and enable its black start capability. The NRS and LMB thermal behaviour are modelled in Dymola, while the electrical dynamics of the LMB and power grid are modelled in RTDS‐RSCAD. Both simulation platforms are coupled and share their thermal and electrical data using a Transmission Control Protocol/Internet Protocol (TCP/IP) communication protocol. The dynamic performance of the NRS‐LMB integration is tested on the IEEE 9 bus, which demonstrates its ability to respond and provide frequency and voltage regulation. The black start capability of the NRS‐LMB is also evaluated by simulating a grid outage and using the LMB to supply the auxiliary loads required to bring the NRS back online as soon as possible. The results show that coupling an NRS to an LMB improves the system dynamic performance and enables it to black start after being disconnected from the grid for several days.
{"title":"Modelling and analysis of nuclear reactor system coupled with a liquid metal battery","authors":"A. Shigrekar, Jiangkai Peng, Temitayo O. Olowu, Fernando Gallego Dias, Tyler Westover","doi":"10.1049/tje2.12382","DOIUrl":"https://doi.org/10.1049/tje2.12382","url":null,"abstract":"Traditionally, nuclear power plants in the U.S. provide baseload power to the power grid because they have less flexibility for ramping their output power than natural gas peaking plants. However, achieving climate goals to reduce the consumption of fossil‐based natural gas places pressure on nuclear power plants and other power generators to ramp up their power output to balance grid generation with demand. This paper presents the modelling and performance analysis of a nuclear reactor system (NRS) coupled to a liquid‐metal battery (LMB) to improve its dynamic response and enable its black start capability. The NRS and LMB thermal behaviour are modelled in Dymola, while the electrical dynamics of the LMB and power grid are modelled in RTDS‐RSCAD. Both simulation platforms are coupled and share their thermal and electrical data using a Transmission Control Protocol/Internet Protocol (TCP/IP) communication protocol. The dynamic performance of the NRS‐LMB integration is tested on the IEEE 9 bus, which demonstrates its ability to respond and provide frequency and voltage regulation. The black start capability of the NRS‐LMB is also evaluated by simulating a grid outage and using the LMB to supply the auxiliary loads required to bring the NRS back online as soon as possible. The results show that coupling an NRS to an LMB improves the system dynamic performance and enables it to black start after being disconnected from the grid for several days.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"226 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141056201","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 road multi‐object detection algorithm is one of the core algorithms for intelligent road cleaning robots relying on machine vision. Most existing object detection algorithms analyse all image regions and finally calculate the category and location of each object. However, it is not necessary to analyse all areas of the image when detecting objects on the road surface where the background changes little, and the number of objects is small because there will be a lot of invalid calculations. If we can perform targeted local analysis on images instead of analysing all image regions, it will improve the detection efficiency. Therefore, this paper proposes a multi‐object detection method using a binocular camera and a convolutional neural network (CNN) that effectively reduces invalid calculations during the detection and improves detection efficiency. In the developed method, the binocular vision image acquired by the binocular camera is stereo matched and equalized, while linear regression and coordinate transformation eliminate the angle of the camera pair concerning the road surface. Then, the coordinates of the regions of interest (ROI) is calculated in the left vision image and the features within the ROI is extracted from the corresponding CNN's feature map. Next, ROI pooling resizes the extracted feature maps of different sizes to the same size, which are then input to the fully connected layers to output the results. The proposed binocular network and faster R‐CNN (VGG16) are trained and tested on a dataset involving 1000 road waste images. The experimental results demonstrate that the developed binocular network improves the detection accuracy and speed by 28.56% and 78.39%, respectively, compared with faster R‐CNN (VGG16), providing a reliable basis for a machine vision‐based intelligent road cleaning robot.
{"title":"Multi‐object road waste detection and classification based on binocular vision","authors":"He Guo, Lumin Chen","doi":"10.1049/tje2.12389","DOIUrl":"https://doi.org/10.1049/tje2.12389","url":null,"abstract":"A road multi‐object detection algorithm is one of the core algorithms for intelligent road cleaning robots relying on machine vision. Most existing object detection algorithms analyse all image regions and finally calculate the category and location of each object. However, it is not necessary to analyse all areas of the image when detecting objects on the road surface where the background changes little, and the number of objects is small because there will be a lot of invalid calculations. If we can perform targeted local analysis on images instead of analysing all image regions, it will improve the detection efficiency. Therefore, this paper proposes a multi‐object detection method using a binocular camera and a convolutional neural network (CNN) that effectively reduces invalid calculations during the detection and improves detection efficiency. In the developed method, the binocular vision image acquired by the binocular camera is stereo matched and equalized, while linear regression and coordinate transformation eliminate the angle of the camera pair concerning the road surface. Then, the coordinates of the regions of interest (ROI) is calculated in the left vision image and the features within the ROI is extracted from the corresponding CNN's feature map. Next, ROI pooling resizes the extracted feature maps of different sizes to the same size, which are then input to the fully connected layers to output the results. The proposed binocular network and faster R‐CNN (VGG16) are trained and tested on a dataset involving 1000 road waste images. The experimental results demonstrate that the developed binocular network improves the detection accuracy and speed by 28.56% and 78.39%, respectively, compared with faster R‐CNN (VGG16), providing a reliable basis for a machine vision‐based intelligent road cleaning robot.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"28 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141046487","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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