The rapid development of wind energy in the power sectors raises the question about the reliability of wind turbines for power system planning and operation.The electrical subsystem of wind turbines(ESWT),which is one...The rapid development of wind energy in the power sectors raises the question about the reliability of wind turbines for power system planning and operation.The electrical subsystem of wind turbines(ESWT),which is one of the most vulnerable parts of the wind turbine,is investigated in this paper.The hygrothermal aging of power electronic devices(PEDs)is modeled for the first time in the comprehensive reliability evaluation of ESWT,by using a novel stationary“circuit-like”approach.First,the failure mechanism of the hygrothermal aging,which includes the solder layer fatigue damage and packaging material performance degradation,is explained.Then,a moisture diffusion resistance concept and a hygrothermal equivalent circuit are proposed to quantitate the hygrothermal aging behavior.A conditional probability function is developed to calculate the time-varying failure rate of PEDs.At last,the stochastic renewal process is simulated to evaluate the reliability for ESWT through the sequential Monte Carlo simulation,in which failure,repair,and replacement states of devices are all included.The effectiveness of our proposed reliability evaluation method is verified on an ESWT in a 2 MW wind turbine use time series data collected from a wind farm in China.展开更多
To address the stochasticity and nonlinearity of solar collector power systems,a soft sensor prediction model with a hybrid convolutional neural network(CNN)and long short-term memory network(LSTM)was constructed,and ...To address the stochasticity and nonlinearity of solar collector power systems,a soft sensor prediction model with a hybrid convolutional neural network(CNN)and long short-term memory network(LSTM)was constructed,and the hyperparameter optimization of the hybrid neural network(CNN-LSTM)was carried out by using the sparrow search algorithm(SSA).The model utilized the powerful feature extraction and non-linear mapping capabilities of deep learning to effectively handle the complex relationship between input and target variables.The batch normalization technique was used to speed up the training and improve the stability of the soft-sensing model,and the random discard technique was used to prevent the soft-sensing model from overfitting.Finally,the mean absolute error(MAE)was used to assess the accuracy of the soft sensor model predictions.This study compared the proposed model with soft sensor prediction models like Bp,Elman,CNN,LSTM,and CNN-LSTM,using dynamic thermal performance data from the solar collector field of the molten salt linear Fresnel photovoltaic demonstration power plant.The deep learning-based soft sensor model outperformed the other models according to the experimental data.Its coefficients of determination(namely R^(2))are higher by 6.35%,8.42%,5.69%,6.90%,and 3.67%,respectively.The accuracy and robustness have been significantly improved.展开更多
Quantum error-correcting codes are essential for fault-tolerant quantum computing,as they effectively detect and correct noise-induced errors by distributing information across multiple physical qubits.The subsystem s...Quantum error-correcting codes are essential for fault-tolerant quantum computing,as they effectively detect and correct noise-induced errors by distributing information across multiple physical qubits.The subsystem surface code with three-qubit check operators demonstrates significant application potential due to its simplified measurement operations and low logical error rates.However,the existing minimum-weight perfect matching(MWPM)algorithm exhibits high computational complexity and lacks flexibility in large-scale systems.Therefore,this paper proposes a decoder based on a graph attention network(GAT),representing error syndromes as undirected graphs with edge weights,and employing a multihead attention mechanism to efficiently aggregate node features and enable parallel computation.Compared to MWPM,the GAT decoder exhibits linear growth in computational complexity,adapts to different quantum code structures,and demonstrates stronger robustness under high physical error rates.The experimental results demonstrate that the proposed decoder achieves an overall accuracy of 89.95%under various small code lattice sizes(L=2,3,4,5),with the logical error rate threshold increasing to 0.0078,representing an improvement of approximately 13.04%compared to the MWPM decoder.This result significantly outperforms traditional methods,showcasing superior performance under small code lattice sizes and providing a more efficient decoding solution for large-scale quantum error correction.展开更多
针对新型电力系统协同调控对传输网络提出了超大带宽、多业务感知承载、确定性传输、绿色低碳的要求,围绕细粒度光传送网(fine-grained Optical Transport Network,fgOTN)组网策略展开研究,通过对比分析,明确了fgOTN传输网中多类业务的...针对新型电力系统协同调控对传输网络提出了超大带宽、多业务感知承载、确定性传输、绿色低碳的要求,围绕细粒度光传送网(fine-grained Optical Transport Network,fgOTN)组网策略展开研究,通过对比分析,明确了fgOTN传输网中多类业务的承载平面,并分别提出了从同步数字体系传输网和光传送网向fgOTN传输网平滑演进的可行性方案,并对fgOTN传输网的建设方案及光路子系统配置提出了推荐性建议,对电力系统骨干传输网向fgOTN的演进具有一定的指导意义。展开更多
针对电力语音通信系统架构老化、协议异构、运维割裂等问题,提出基于IP多媒体子系统(IP Multimedia Subsystem,IMS)的语音接入网融合改造方案,构建统一信令控制、媒体处理与服务质量(Quality of Service,QoS)保障体系。设计端到端架构,...针对电力语音通信系统架构老化、协议异构、运维割裂等问题,提出基于IP多媒体子系统(IP Multimedia Subsystem,IMS)的语音接入网融合改造方案,构建统一信令控制、媒体处理与服务质量(Quality of Service,QoS)保障体系。设计端到端架构,完成多协议信令映射、安全与QoS协同机制,实现控制面集中化与接口能力开放。通过构建实验环境开展高并发性能测试,验证系统在会话控制、媒体传输与资源调度方面的提升效果,结果表明改造方案具备工程可行性与部署价值。展开更多
电信运营商拥有大规模的语音用户数,但是随着OTT(Over The Top)业务的广泛使用,给运营商基础语音业务受到了冲击,每用户通话时长(Minutes Of Usage,MOU)持续降低且收入逐年下降。5G增强通话是基于5G的新一代通话产品,提供超高清、智能...电信运营商拥有大规模的语音用户数,但是随着OTT(Over The Top)业务的广泛使用,给运营商基础语音业务受到了冲击,每用户通话时长(Minutes Of Usage,MOU)持续降低且收入逐年下降。5G增强通话是基于5G的新一代通话产品,提供超高清、智能化、全交互三大能力,以期增加用户黏性并为基础通话业务带来新的潜在增长空间。介绍5G增强通话的业务类型和发展现状,然后从建设原则、网络架构、数据通道信令功能(Data Channel Signaling Function,DCSF)网元建设方案、资源池建设方案、网络安全及可靠性等方面探讨5G增强通话控制面网元DCSF的建设实施方案。展开更多
基金supported by the National Natural Science Foundation of China under Grant 52022016China Postdoctoral Science Foundation under grant 2021M693711Fundamental Research Funds for the Central Universities under grant 2021CDJQY-037.
文摘The rapid development of wind energy in the power sectors raises the question about the reliability of wind turbines for power system planning and operation.The electrical subsystem of wind turbines(ESWT),which is one of the most vulnerable parts of the wind turbine,is investigated in this paper.The hygrothermal aging of power electronic devices(PEDs)is modeled for the first time in the comprehensive reliability evaluation of ESWT,by using a novel stationary“circuit-like”approach.First,the failure mechanism of the hygrothermal aging,which includes the solder layer fatigue damage and packaging material performance degradation,is explained.Then,a moisture diffusion resistance concept and a hygrothermal equivalent circuit are proposed to quantitate the hygrothermal aging behavior.A conditional probability function is developed to calculate the time-varying failure rate of PEDs.At last,the stochastic renewal process is simulated to evaluate the reliability for ESWT through the sequential Monte Carlo simulation,in which failure,repair,and replacement states of devices are all included.The effectiveness of our proposed reliability evaluation method is verified on an ESWT in a 2 MW wind turbine use time series data collected from a wind farm in China.
基金supported by National Natural Science Foundation of China(No.52266012)Gansu Province College Industry Support Plan Project(No.2022CYZC-34)+1 种基金Gansu Province Major Science and Technology Special Project(Nos.20ZD7GF011,22ZD6GA063)Jiuquan City Science and Technology Programme Project(No.2023CA3058).
文摘To address the stochasticity and nonlinearity of solar collector power systems,a soft sensor prediction model with a hybrid convolutional neural network(CNN)and long short-term memory network(LSTM)was constructed,and the hyperparameter optimization of the hybrid neural network(CNN-LSTM)was carried out by using the sparrow search algorithm(SSA).The model utilized the powerful feature extraction and non-linear mapping capabilities of deep learning to effectively handle the complex relationship between input and target variables.The batch normalization technique was used to speed up the training and improve the stability of the soft-sensing model,and the random discard technique was used to prevent the soft-sensing model from overfitting.Finally,the mean absolute error(MAE)was used to assess the accuracy of the soft sensor model predictions.This study compared the proposed model with soft sensor prediction models like Bp,Elman,CNN,LSTM,and CNN-LSTM,using dynamic thermal performance data from the solar collector field of the molten salt linear Fresnel photovoltaic demonstration power plant.The deep learning-based soft sensor model outperformed the other models according to the experimental data.Its coefficients of determination(namely R^(2))are higher by 6.35%,8.42%,5.69%,6.90%,and 3.67%,respectively.The accuracy and robustness have been significantly improved.
基金Project supported by the Natural Science Foundation of Shandong Province,China(Grant No.ZR2021MF049)the Joint Fund of the Natural Science Foundation of Shandong Province,China(Grant Nos.ZR2022LLZ012 and ZR2021LLZ001)the Key Research and Development Program of Shandong Province,China(Grant No.2023CXGC010901)。
文摘Quantum error-correcting codes are essential for fault-tolerant quantum computing,as they effectively detect and correct noise-induced errors by distributing information across multiple physical qubits.The subsystem surface code with three-qubit check operators demonstrates significant application potential due to its simplified measurement operations and low logical error rates.However,the existing minimum-weight perfect matching(MWPM)algorithm exhibits high computational complexity and lacks flexibility in large-scale systems.Therefore,this paper proposes a decoder based on a graph attention network(GAT),representing error syndromes as undirected graphs with edge weights,and employing a multihead attention mechanism to efficiently aggregate node features and enable parallel computation.Compared to MWPM,the GAT decoder exhibits linear growth in computational complexity,adapts to different quantum code structures,and demonstrates stronger robustness under high physical error rates.The experimental results demonstrate that the proposed decoder achieves an overall accuracy of 89.95%under various small code lattice sizes(L=2,3,4,5),with the logical error rate threshold increasing to 0.0078,representing an improvement of approximately 13.04%compared to the MWPM decoder.This result significantly outperforms traditional methods,showcasing superior performance under small code lattice sizes and providing a more efficient decoding solution for large-scale quantum error correction.
文摘针对新型电力系统协同调控对传输网络提出了超大带宽、多业务感知承载、确定性传输、绿色低碳的要求,围绕细粒度光传送网(fine-grained Optical Transport Network,fgOTN)组网策略展开研究,通过对比分析,明确了fgOTN传输网中多类业务的承载平面,并分别提出了从同步数字体系传输网和光传送网向fgOTN传输网平滑演进的可行性方案,并对fgOTN传输网的建设方案及光路子系统配置提出了推荐性建议,对电力系统骨干传输网向fgOTN的演进具有一定的指导意义。
文摘针对电力语音通信系统架构老化、协议异构、运维割裂等问题,提出基于IP多媒体子系统(IP Multimedia Subsystem,IMS)的语音接入网融合改造方案,构建统一信令控制、媒体处理与服务质量(Quality of Service,QoS)保障体系。设计端到端架构,完成多协议信令映射、安全与QoS协同机制,实现控制面集中化与接口能力开放。通过构建实验环境开展高并发性能测试,验证系统在会话控制、媒体传输与资源调度方面的提升效果,结果表明改造方案具备工程可行性与部署价值。
文摘电信运营商拥有大规模的语音用户数,但是随着OTT(Over The Top)业务的广泛使用,给运营商基础语音业务受到了冲击,每用户通话时长(Minutes Of Usage,MOU)持续降低且收入逐年下降。5G增强通话是基于5G的新一代通话产品,提供超高清、智能化、全交互三大能力,以期增加用户黏性并为基础通话业务带来新的潜在增长空间。介绍5G增强通话的业务类型和发展现状,然后从建设原则、网络架构、数据通道信令功能(Data Channel Signaling Function,DCSF)网元建设方案、资源池建设方案、网络安全及可靠性等方面探讨5G增强通话控制面网元DCSF的建设实施方案。
