The intelligent sensing system is the digital basis for zero‐carbon park operation. Due to the immaturity of its cost‐benefit accounting method and the lack of a special construction mode evaluation system, there is blindness in its investment and construction process. In order to select the suitable construction mode for the intelligent sensing system scientifically and effectively, this paper first establishes the architecture of the intelligent sensing system, and determines a cost‐benefit accounting method of the intelligent sensing system. Then, this paper establishes a construction mode evaluation index system including techno‐economic indicators, and proposes a fuzzy comprehensive evaluation method combing analytic hierarchy process and the decision‐making trial and evaluation laboratory to select the optimal construction mode. The numerical analyses show that the public‐private‐partnership mode of the intelligent sensing system has achieved ideal implementation results.
{"title":"Construction mode optimal selection method for intelligent sensing system in zero‐carbon parks","authors":"Mengzeng Cheng, Wei Niu, Jingwei Hu, Zongyuan Wang, Jing Chen, Chao Lin","doi":"10.1049/tje2.12326","DOIUrl":"https://doi.org/10.1049/tje2.12326","url":null,"abstract":"The intelligent sensing system is the digital basis for zero‐carbon park operation. Due to the immaturity of its cost‐benefit accounting method and the lack of a special construction mode evaluation system, there is blindness in its investment and construction process. In order to select the suitable construction mode for the intelligent sensing system scientifically and effectively, this paper first establishes the architecture of the intelligent sensing system, and determines a cost‐benefit accounting method of the intelligent sensing system. Then, this paper establishes a construction mode evaluation index system including techno‐economic indicators, and proposes a fuzzy comprehensive evaluation method combing analytic hierarchy process and the decision‐making trial and evaluation laboratory to select the optimal construction mode. The numerical analyses show that the public‐private‐partnership mode of the intelligent sensing system has achieved ideal implementation results.","PeriodicalId":22858,"journal":{"name":"The Journal of Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139297157","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}
Yiming Chen, Dongliang Gao, Yuxuan Yang, Jintang Luo, Yi Li
Secure and reliable electricity supply is a prerequisite for the development of smart cities, and the trustworthy and efficient transmission of electrical data is the foundation for the safe and stable operation of the power grid. This paper introduces a real‐time data transmission blockchain technique based on parallel proof of work algorithm. The new block generation process of proposed blockchain is divided into five subroutines: hash pointer computation, real‐time data pudding, signature value iteration, interruption, block header assembly. The real‐time data pudding and signature value iteration are parallel processed, which brings the effect of decreasing energy loss of blockchain system, and enhances the speed of new block generation and the bandwidth of data storage on blockchain. Computer simulation shows the proposed strategy can be effectively applied in real‐time electrical data transmission application, raising the data transmission reliability with no harm to real‐time data transfer function. This strategy provides a solution to guarantee data transmission safety in the digital conversion of power grid.
{"title":"Research of trusted real‐time electrical data transmission mechanism based on Parallel Proof of Work algorithm","authors":"Yiming Chen, Dongliang Gao, Yuxuan Yang, Jintang Luo, Yi Li","doi":"10.1049/tje2.12332","DOIUrl":"https://doi.org/10.1049/tje2.12332","url":null,"abstract":"Secure and reliable electricity supply is a prerequisite for the development of smart cities, and the trustworthy and efficient transmission of electrical data is the foundation for the safe and stable operation of the power grid. This paper introduces a real‐time data transmission blockchain technique based on parallel proof of work algorithm. The new block generation process of proposed blockchain is divided into five subroutines: hash pointer computation, real‐time data pudding, signature value iteration, interruption, block header assembly. The real‐time data pudding and signature value iteration are parallel processed, which brings the effect of decreasing energy loss of blockchain system, and enhances the speed of new block generation and the bandwidth of data storage on blockchain. Computer simulation shows the proposed strategy can be effectively applied in real‐time electrical data transmission application, raising the data transmission reliability with no harm to real‐time data transfer function. This strategy provides a solution to guarantee data transmission safety in the digital conversion of power grid.","PeriodicalId":22858,"journal":{"name":"The Journal of Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139298878","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}
Abstract When tracking a single manoeuvring target in clutter environment, when the number of effective measurements within the detection threshold is small, it usually has a greater and more obvious impact on target‐tracking results. If the observation data error is large at this time, the tracking position and speed error will be larger. To solve this problem, a target‐tracking algorithm based on improved probabilistic data association is proposed in this paper. By dynamically adjusting the detection threshold, the effective quantity within the detection threshold of each frame is basically stable. Simulation results show that the improved algorithm is more accurate in location and speed than the traditional probabilistic data association method and Kalman filter, and the availability and effectiveness of the algorithm are verified.
{"title":"Target‐tracking algorithm based on improved probabilistic data association","authors":"Xiaojie Huang, Jiaguo Zhang","doi":"10.1049/tje2.12321","DOIUrl":"https://doi.org/10.1049/tje2.12321","url":null,"abstract":"Abstract When tracking a single manoeuvring target in clutter environment, when the number of effective measurements within the detection threshold is small, it usually has a greater and more obvious impact on target‐tracking results. If the observation data error is large at this time, the tracking position and speed error will be larger. To solve this problem, a target‐tracking algorithm based on improved probabilistic data association is proposed in this paper. By dynamically adjusting the detection threshold, the effective quantity within the detection threshold of each frame is basically stable. Simulation results show that the improved algorithm is more accurate in location and speed than the traditional probabilistic data association method and Kalman filter, and the availability and effectiveness of the algorithm are verified.","PeriodicalId":22858,"journal":{"name":"The Journal of Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135411342","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}
P. Xiao, Yixin Jiang, Zhihong Liang, Hailin Wang, Yunan Zhang
During the operation and maintenance of the power system, power outages and supply‐demand imbalances can disrupt the normal power supply process. This issue must be mitigated or even resolved through the implementation of an appropriate power system risk warning. The article proposes a self‐assessment and early warning strategy for power system hazards based on an enhanced ant colony optimization algorithm (IACO) and a BP neural network. First, a combination of the Analytic Hierarchy Process (AHP) and the Entropy Weighting Method (EWM) is used to assign weights comprehensively to indicators that have a significant impact on the stability and safety of power system operation, thereby avoiding the negative impact of subjective experience or objective factors on the weight allocation results. Secondly, multiple regression analysis is used to calculate the risk assessment results of the selected indicators and weights corresponding to the power system. Training and testing samples for the BP neural network were calculated based on the weight allocation procedure described previously. Then, IACO is employed to global optimize the weights and thresholds of the BP neural network, and an enhanced BP neural network model for independent power system risk assessment is developed. The designed risk assessment and warning strategy was finally evaluated. The results indicate that the proposed power system risk assessment and early warning method can precisely predict the actual operating status of the power system based on weight values, thereby enhancing power supply quality by providing technical personnel with a data reference.
在电力系统的运行和维护过程中，停电和供需不平衡会扰乱正常的电力供应过程。必须通过实施适当的电力系统风险预警来缓解甚至解决这一问题。文章提出了一种基于增强型蚁群优化算法（IACO）和 BP 神经网络的电力系统危险自评估和预警策略。首先，采用层次分析法（AHP）和熵权法（EWM）相结合的方法，对对电力系统运行稳定性和安全性有重大影响的指标进行综合权重分配，从而避免主观经验或客观因素对权重分配结果的负面影响。其次，采用多元回归分析法计算所选指标与权重对应的电力系统风险评估结果。根据前面所述的权重分配程序，计算出 BP 神经网络的训练样本和测试样本。然后，采用 IACO 对 BP 神经网络的权重和阈值进行全局优化，建立了用于独立电力系统风险评估的增强型 BP 神经网络模型。最后对所设计的风险评估和预警策略进行了评估。结果表明，所提出的电力系统风险评估和预警方法可以根据权重值精确预测电力系统的实际运行状态，从而为技术人员提供数据参考，提高供电质量。
{"title":"Power system risk assessment strategy based on weighted comprehensive allocation and improved BP neural network","authors":"P. Xiao, Yixin Jiang, Zhihong Liang, Hailin Wang, Yunan Zhang","doi":"10.1049/tje2.12323","DOIUrl":"https://doi.org/10.1049/tje2.12323","url":null,"abstract":"During the operation and maintenance of the power system, power outages and supply‐demand imbalances can disrupt the normal power supply process. This issue must be mitigated or even resolved through the implementation of an appropriate power system risk warning. The article proposes a self‐assessment and early warning strategy for power system hazards based on an enhanced ant colony optimization algorithm (IACO) and a BP neural network. First, a combination of the Analytic Hierarchy Process (AHP) and the Entropy Weighting Method (EWM) is used to assign weights comprehensively to indicators that have a significant impact on the stability and safety of power system operation, thereby avoiding the negative impact of subjective experience or objective factors on the weight allocation results. Secondly, multiple regression analysis is used to calculate the risk assessment results of the selected indicators and weights corresponding to the power system. Training and testing samples for the BP neural network were calculated based on the weight allocation procedure described previously. Then, IACO is employed to global optimize the weights and thresholds of the BP neural network, and an enhanced BP neural network model for independent power system risk assessment is developed. The designed risk assessment and warning strategy was finally evaluated. The results indicate that the proposed power system risk assessment and early warning method can precisely predict the actual operating status of the power system based on weight values, thereby enhancing power supply quality by providing technical personnel with a data reference.","PeriodicalId":22858,"journal":{"name":"The Journal of Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139293158","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}
Ibrahim Haruna Shanono, Nor Rul Hasma Abdullah, Hamdan Daniyal, Aisha Muhammad
Abstract Multi‐level inverters are widely used in high‐voltage and high‐power applications due to the increasing demand for renewable energy. This study proposes a novel single‐phase reduced switch multi‐level inverter topology that generates 11 levels of output voltage steps, operates in asymmetric mode, and uses fewer power electronic switches with efficient switching control. To optimize the inverter's performance, Moth Flame Optimization (MFO), Particle Swarm Optimization (PSO), and Whale Optimization Technique (WOA) are utilized to apply the selective harmonic elimination technique. The proposed circuit is implemented in PSIM software using optimized switching angles, and the fitness functions and switching angles for the three optimizers are evaluated and reported. The inverter's performance at optimal modulation points for the three optimizers is computed and analyzed, with the Total Harmonic Distortion (THD) measured at 0.82 modulation point before and after filtering. Results show that MFO outperforms PSO and WOA with the lowest THD values of 0.85% and 0.78%, respectively; therefore, complying with the IEEE 519 standard. The experimental validation of MFO's superiority is performed using the Typhoon HIL‐402 hardware device. This study provides a promising solution for the design and optimization of multi‐level inverters, paving the way for more efficient and reliable renewable energy systems.
{"title":"Optimizing performance of a reduced switch multi‐level inverter with moth‐flame algorithm and SHE‐PWM","authors":"Ibrahim Haruna Shanono, Nor Rul Hasma Abdullah, Hamdan Daniyal, Aisha Muhammad","doi":"10.1049/tje2.12281","DOIUrl":"https://doi.org/10.1049/tje2.12281","url":null,"abstract":"Abstract Multi‐level inverters are widely used in high‐voltage and high‐power applications due to the increasing demand for renewable energy. This study proposes a novel single‐phase reduced switch multi‐level inverter topology that generates 11 levels of output voltage steps, operates in asymmetric mode, and uses fewer power electronic switches with efficient switching control. To optimize the inverter's performance, Moth Flame Optimization (MFO), Particle Swarm Optimization (PSO), and Whale Optimization Technique (WOA) are utilized to apply the selective harmonic elimination technique. The proposed circuit is implemented in PSIM software using optimized switching angles, and the fitness functions and switching angles for the three optimizers are evaluated and reported. The inverter's performance at optimal modulation points for the three optimizers is computed and analyzed, with the Total Harmonic Distortion (THD) measured at 0.82 modulation point before and after filtering. Results show that MFO outperforms PSO and WOA with the lowest THD values of 0.85% and 0.78%, respectively; therefore, complying with the IEEE 519 standard. The experimental validation of MFO's superiority is performed using the Typhoon HIL‐402 hardware device. This study provides a promising solution for the design and optimization of multi‐level inverters, paving the way for more efficient and reliable renewable energy systems.","PeriodicalId":22858,"journal":{"name":"The Journal of Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135412490","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}
Mohamed K. M. Fadul, Donald R. Reising, Lakmali P. Weerasena
Abstract Increasing Internet of Things (IoT) deployments present a growing surface over which villainous actors can carry out attacks. This disturbing revelation is amplified by the fact that most IoT devices use weak or no encryption. Specific Emitter Identification (SEI) is an approach intended to address this IoT security weakness. This work provides the first Deep Learning (DL) driven SEI approach that upsamples the signals after collection to improve performance while reducing the hardware requirements of the IoT devices that collect them. DL‐driven upsampling results in superior SEI performance versus two traditional upsampling approaches and a convolutional neural network‐only approach.
{"title":"An investigation into the impacts of deep learning‐based re‐sampling on specific emitter identification performance","authors":"Mohamed K. M. Fadul, Donald R. Reising, Lakmali P. Weerasena","doi":"10.1049/tje2.12327","DOIUrl":"https://doi.org/10.1049/tje2.12327","url":null,"abstract":"Abstract Increasing Internet of Things (IoT) deployments present a growing surface over which villainous actors can carry out attacks. This disturbing revelation is amplified by the fact that most IoT devices use weak or no encryption. Specific Emitter Identification (SEI) is an approach intended to address this IoT security weakness. This work provides the first Deep Learning (DL) driven SEI approach that upsamples the signals after collection to improve performance while reducing the hardware requirements of the IoT devices that collect them. DL‐driven upsampling results in superior SEI performance versus two traditional upsampling approaches and a convolutional neural network‐only approach.","PeriodicalId":22858,"journal":{"name":"The Journal of Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135714570","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}
Camille Franklin Mbey, Vinny Junior Foba Kakeu, Alexandre Teplaira Boum, Felix Ghislain Yem Souhe
Abstract This article proposes a deep learning (DL) model made of Long Short Term Memory (LSTM) and Adaptive Neuro Fuzzy Inference System (ANFIS) to detect fault in smart distribution grid assisted by communication systems using smart meter data. In smart grid, data analysis for fault identification and detection is crucial for grid monitoring. Nowadays, there are several DL techniques developed for smart grid data analysis applications. To solve this problem, a novel data analysis model based on deep learning and Neuro‐fuzzy algorithm is developed for fault location in a smart power grid. First, the LSTM is applied for training the data samples extracted from the smart meters. Then, an ANFIS algorithm is implemented for fault detection and identification from the trained data. Finally, faults are located with the higher accuracy. With this intelligent method proposed, single‐phase, two‐phase and three‐phase faults can be identified using a restricted amount of data. The novelty of the proposed method compared with other methods is the capability of fast training and testing even with large amount of data. To verify the effectiveness of our methodology, an intelligent model of the IEEE 13‐node network is used. The effectiveness and robustness of the proposed model are evaluated using several parameters such as accuracy, precision‐recall, F1‐score, Receiver Operating Characteristic (ROC) curve and complexity time. The obtained results indicate that the proposed deep learning model outperforms existing deep learning methods in the literature for fault detection and classification with 99.99% accuracy.
{"title":"Fault detection and classification using deep learning method and neuro‐fuzzy algorithm in a smart distribution grid","authors":"Camille Franklin Mbey, Vinny Junior Foba Kakeu, Alexandre Teplaira Boum, Felix Ghislain Yem Souhe","doi":"10.1049/tje2.12324","DOIUrl":"https://doi.org/10.1049/tje2.12324","url":null,"abstract":"Abstract This article proposes a deep learning (DL) model made of Long Short Term Memory (LSTM) and Adaptive Neuro Fuzzy Inference System (ANFIS) to detect fault in smart distribution grid assisted by communication systems using smart meter data. In smart grid, data analysis for fault identification and detection is crucial for grid monitoring. Nowadays, there are several DL techniques developed for smart grid data analysis applications. To solve this problem, a novel data analysis model based on deep learning and Neuro‐fuzzy algorithm is developed for fault location in a smart power grid. First, the LSTM is applied for training the data samples extracted from the smart meters. Then, an ANFIS algorithm is implemented for fault detection and identification from the trained data. Finally, faults are located with the higher accuracy. With this intelligent method proposed, single‐phase, two‐phase and three‐phase faults can be identified using a restricted amount of data. The novelty of the proposed method compared with other methods is the capability of fast training and testing even with large amount of data. To verify the effectiveness of our methodology, an intelligent model of the IEEE 13‐node network is used. The effectiveness and robustness of the proposed model are evaluated using several parameters such as accuracy, precision‐recall, F1‐score, Receiver Operating Characteristic (ROC) curve and complexity time. The obtained results indicate that the proposed deep learning model outperforms existing deep learning methods in the literature for fault detection and classification with 99.99% accuracy.","PeriodicalId":22858,"journal":{"name":"The Journal of Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135013372","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}
Mohsen Baniasadi Nejad, Seyyed Morteza Ghamari, Hasan Mollaee
Abstract Adaptive neuro‐fuzzy inference system (ANFIS) approach is designed for a Buck converter. Because DC–DC converters are under the negative impact of different disturbances, a need for a well‐behaved technique is felt to provide higher robustness in various scenarios, including parametric variations, load uncertainty, supply voltage variation, and noise. Therefore, the fuzzy logic‐based controller is adopted for this structure that provides better error detection and correction, more comprehensive range of operating conditions, and is more readily customizable. However, the fuzzy technique suffers from slow dynamics, lack of reliability against broader range of disturbances, and has a huge computational burden. To overcome the weaknesses addressed before, this technique combined with an artificial neural network (ANN) system that can tune the fuzzy part resulting in an adaptive and robust structure. ANFIS method is a promising approach that has two soft‐computing control structures, including a fuzzy logic‐based part consisting of ANN. This combination has provided many significant benefits over fuzzy logic, such as low computational burden with faster dynamics, higher flexibility with adaptable rules, and a simple structure providing ease of practical implantation; also, it does not need a mathematical moulding of the system since the whole system has been considered as a Black‐box system. To better show the superiority of this method, two other control schemes are designed as fuzzy‐based PID technique and PID controller optimized by PSO algorithm. Finally, the ANFIS control strategy is tested in various working cases through simulation and experiment results as a beneficial alternative for practical applications.
{"title":"Adaptive neuro‐fuzzy inference systems controller design on Buck converter","authors":"Mohsen Baniasadi Nejad, Seyyed Morteza Ghamari, Hasan Mollaee","doi":"10.1049/tje2.12316","DOIUrl":"https://doi.org/10.1049/tje2.12316","url":null,"abstract":"Abstract Adaptive neuro‐fuzzy inference system (ANFIS) approach is designed for a Buck converter. Because DC–DC converters are under the negative impact of different disturbances, a need for a well‐behaved technique is felt to provide higher robustness in various scenarios, including parametric variations, load uncertainty, supply voltage variation, and noise. Therefore, the fuzzy logic‐based controller is adopted for this structure that provides better error detection and correction, more comprehensive range of operating conditions, and is more readily customizable. However, the fuzzy technique suffers from slow dynamics, lack of reliability against broader range of disturbances, and has a huge computational burden. To overcome the weaknesses addressed before, this technique combined with an artificial neural network (ANN) system that can tune the fuzzy part resulting in an adaptive and robust structure. ANFIS method is a promising approach that has two soft‐computing control structures, including a fuzzy logic‐based part consisting of ANN. This combination has provided many significant benefits over fuzzy logic, such as low computational burden with faster dynamics, higher flexibility with adaptable rules, and a simple structure providing ease of practical implantation; also, it does not need a mathematical moulding of the system since the whole system has been considered as a Black‐box system. To better show the superiority of this method, two other control schemes are designed as fuzzy‐based PID technique and PID controller optimized by PSO algorithm. Finally, the ANFIS control strategy is tested in various working cases through simulation and experiment results as a beneficial alternative for practical applications.","PeriodicalId":22858,"journal":{"name":"The Journal of Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135707032","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}
Abstract Microstrip patch antennas are broadly deployed low‐profile antennas. Unfortunately, there are some common issues with standard patch antennas, such as narrow bandwidth, low radiation efficiency, and large dimensions for low‐frequency applications. Patch antennas can simultaneously be greatly miniaturized while increasing bandwidth through the use of magneto‐dielectric substrates. However, this too could come with complications—the potential for radiation at unintended frequencies and distortion in the passband. Two patch antennas, one with a standard dielectric substrate and one with a magneto‐dielectric substrate, were fabricated and tested up to 10 dBW input power. The performance of the magneto‐dielectric substrate was compared to that of the standard substrate to test passive intermodulation and harmonic distortion. The magneto‐dielectric substrate proved to have some resilience to harmonic distortion, but not to passive intermodulation distortion.
{"title":"Power limitations of magneto‐dielectric substrate microstrip antennas","authors":"Clayton Blosser, Hjalti Sigmarsson, Jessica Ruyle","doi":"10.1049/tje2.12305","DOIUrl":"https://doi.org/10.1049/tje2.12305","url":null,"abstract":"Abstract Microstrip patch antennas are broadly deployed low‐profile antennas. Unfortunately, there are some common issues with standard patch antennas, such as narrow bandwidth, low radiation efficiency, and large dimensions for low‐frequency applications. Patch antennas can simultaneously be greatly miniaturized while increasing bandwidth through the use of magneto‐dielectric substrates. However, this too could come with complications—the potential for radiation at unintended frequencies and distortion in the passband. Two patch antennas, one with a standard dielectric substrate and one with a magneto‐dielectric substrate, were fabricated and tested up to 10 dBW input power. The performance of the magneto‐dielectric substrate was compared to that of the standard substrate to test passive intermodulation and harmonic distortion. The magneto‐dielectric substrate proved to have some resilience to harmonic distortion, but not to passive intermodulation distortion.","PeriodicalId":22858,"journal":{"name":"The Journal of Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134977468","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}
Abstract The rapid development of substations has increased the demand for accurate and fast fault prediction systems. In order to achieve rapid localization and autonomous decision‐making of fault modules and types in substations, the article proposes a fault autonomous localization algorithm based on improved ant colony optimization (IACO) and back propagation neural network (BPNN). The fault data of the substation secondary equipment for training and testing the BPNN model is based on the actual operating equipment of the substation, which can significantly improve the reliability of the model results. In addition, the IACO is used to globally optimize the weights and thresholds of BPNN, and the number of hidden layer nodes in BPNN was analyzed to further improve the accuracy of the established fault prediction algorithm. The test results show that the fault prediction accuracy of the BPNN model optimized by IACO is 93.67%, which is significantly improved compared to the traditional BPNN and BPNN with ant colony optimization method (with an accuracy of 82.98% and 91.04%). The above results effectively demonstrate the high accuracy and effectiveness of the established prediction algorithm in processing data and locating faults, which can improve the maintenance and operational efficiency of substations.
{"title":"Effective fault module localization in substation critical equipment: an improved ant colony optimization and back propagation neural network approach","authors":"Wei Wang, Jianfei Zhang, Sai Wang, Xuewei Chen","doi":"10.1049/tje2.12315","DOIUrl":"https://doi.org/10.1049/tje2.12315","url":null,"abstract":"Abstract The rapid development of substations has increased the demand for accurate and fast fault prediction systems. In order to achieve rapid localization and autonomous decision‐making of fault modules and types in substations, the article proposes a fault autonomous localization algorithm based on improved ant colony optimization (IACO) and back propagation neural network (BPNN). The fault data of the substation secondary equipment for training and testing the BPNN model is based on the actual operating equipment of the substation, which can significantly improve the reliability of the model results. In addition, the IACO is used to globally optimize the weights and thresholds of BPNN, and the number of hidden layer nodes in BPNN was analyzed to further improve the accuracy of the established fault prediction algorithm. The test results show that the fault prediction accuracy of the BPNN model optimized by IACO is 93.67%, which is significantly improved compared to the traditional BPNN and BPNN with ant colony optimization method (with an accuracy of 82.98% and 91.04%). The above results effectively demonstrate the high accuracy and effectiveness of the established prediction algorithm in processing data and locating faults, which can improve the maintenance and operational efficiency of substations.","PeriodicalId":22858,"journal":{"name":"The Journal of Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135706128","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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