The stable and reliable operation of grid-integrated renewable energy systems requires advanced control and coordination of grid-side converters(GSCs),utilizing the feedback measurements of voltage and current sensors...The stable and reliable operation of grid-integrated renewable energy systems requires advanced control and coordination of grid-side converters(GSCs),utilizing the feedback measurements of voltage and current sensors from both the direct current(DC)and alternating current(AC)sides of the converter.However,the effective operation of the converter is susceptible to sensor failures or divergence from their proper operation.Although sensor fault detection algorithms are usually effective under abrupt faults,the fault propagation effect caused by the physical interconnection between the DC and AC sides of the converter may limit the performance of the sensor fault isolation process in revealing the exact location of a potential faulty sensor.Therefore,this work proposes a robust,model-based fault isolation and accommodation scheme.Specifically,a synergistic sensor fault isolation framework based on adaptive estimation schemes is proposed for both single and multiple faults in the DC voltage and AC current sensors,considering modeling uncertainty and measurement noise.The performance analysis in terms of stability,learning capability,and fault isolability is rigorously examined.An accommodation scheme based on a virtual sensor utilizing dynamic sensor fault estimation with realtime learning capabilities is applied to a GSC.Finally,the performance of the proposed fault isolation and accommodation scheme is evaluated through simulation analysis under several scenarios involving single and multiple sensor faults.展开更多
Renewable energy has penetrated power grid enterprises on a large scale.Due to the intermittency and volatility of renewable energy generation,it is necessary to build new flexible grid-side resources to ensure the sa...Renewable energy has penetrated power grid enterprises on a large scale.Due to the intermittency and volatility of renewable energy generation,it is necessary to build new flexible grid-side resources to ensure the safe and stable operation of the power grid,which will cause great pressure on cost allocation for power grid enterprises.This article considers four types of flexible grid-side resources and constructs a dual-level configuration optimization model for flexible grid-side resources under the penetration of renewable energy.Based on the configuration results,the cost scale of flexible grid-side resources is estimated and an improved ancillary service cost allocation model based on the Shapley value method is proposed to smooth the allocation of ancillary service costs in the cost of flexible grid-side resources between the two main bodies of renewable energy and load.The calculation results show that,when the penetration rate of renewable-energy power is 30%and 35%,respectively,the cost of flexible grid-side resources is 9.606 billion yuan and 21.518 billion yuan,respectively.The proportion of ancillary service costs allocated to load is relatively high-about five times that of the ancillary service costs allocated to renewable energy-and the higher the penetration rate of renewable energy,the higher the proportion of ancillary service costs allocated to renewable energy.展开更多
非侵入式负荷监测(non-intrusive load monitoring,NILM)技术对于实现智慧用电与管理具有重要意义。针对现有的非侵入式负荷监测方法在高噪声环境下对特征相似电器以及微小负荷变化监测精度不足的难题,提出了一种基于单位力操作视觉变...非侵入式负荷监测(non-intrusive load monitoring,NILM)技术对于实现智慧用电与管理具有重要意义。针对现有的非侵入式负荷监测方法在高噪声环境下对特征相似电器以及微小负荷变化监测精度不足的难题,提出了一种基于单位力操作视觉变换器的非侵入式负荷监测(non-intrusive load monitoring based on unit force operated vision transformer,UFONILM)模型的非侵入式负荷监测的深度学习框架。UFONILM模型的单位力操作(unit force operated,UFO)模块通过层归一化和一系列卷积层有效地提取和利用了多尺度的时间序列数据,特征。在标准的UK-DALE数据集上进行的实验显示,UFONILM模型在准确性和F1得分上均优于现有方法,特别是在细粒度的负荷监测场景中。研制了基于UFONILM模型的嵌入式系统,实现了边缘计算的非侵入式负荷监测,可实时监测和响应电网中的异常用电行为,如违规充电事件。实验检测证明,UFONILM模型嵌入式非侵入式负荷监测方法在监测效率方面具有显著的提升,具有高效、便捷安装、可扩展等特点。展开更多
基金supported in part by the Republic of Cyprus through the Research and Innovation Foundation under Project CULTURE/AWARD-YR/0322B/0003(INVERGE)in part by the Horizon Europe Research and Innovation Programme under Grant Agreement No. 101075747 (TRANSIT)supported by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 739551 (KIOS CoE) and from the Republic of Cyprus through the Deputy Ministry of Research,Innovation and Digital Policy
文摘The stable and reliable operation of grid-integrated renewable energy systems requires advanced control and coordination of grid-side converters(GSCs),utilizing the feedback measurements of voltage and current sensors from both the direct current(DC)and alternating current(AC)sides of the converter.However,the effective operation of the converter is susceptible to sensor failures or divergence from their proper operation.Although sensor fault detection algorithms are usually effective under abrupt faults,the fault propagation effect caused by the physical interconnection between the DC and AC sides of the converter may limit the performance of the sensor fault isolation process in revealing the exact location of a potential faulty sensor.Therefore,this work proposes a robust,model-based fault isolation and accommodation scheme.Specifically,a synergistic sensor fault isolation framework based on adaptive estimation schemes is proposed for both single and multiple faults in the DC voltage and AC current sensors,considering modeling uncertainty and measurement noise.The performance analysis in terms of stability,learning capability,and fault isolability is rigorously examined.An accommodation scheme based on a virtual sensor utilizing dynamic sensor fault estimation with realtime learning capabilities is applied to a GSC.Finally,the performance of the proposed fault isolation and accommodation scheme is evaluated through simulation analysis under several scenarios involving single and multiple sensor faults.
基金funded by the Science and Technology Project of State Grid Ningxia Electric Power Co.,Ltd(Research on the influence analysis model of multidimensional penetration mode of new energy on incremental cost of power grid and electricity price grooming mechanism,5229JY230006).
文摘Renewable energy has penetrated power grid enterprises on a large scale.Due to the intermittency and volatility of renewable energy generation,it is necessary to build new flexible grid-side resources to ensure the safe and stable operation of the power grid,which will cause great pressure on cost allocation for power grid enterprises.This article considers four types of flexible grid-side resources and constructs a dual-level configuration optimization model for flexible grid-side resources under the penetration of renewable energy.Based on the configuration results,the cost scale of flexible grid-side resources is estimated and an improved ancillary service cost allocation model based on the Shapley value method is proposed to smooth the allocation of ancillary service costs in the cost of flexible grid-side resources between the two main bodies of renewable energy and load.The calculation results show that,when the penetration rate of renewable-energy power is 30%and 35%,respectively,the cost of flexible grid-side resources is 9.606 billion yuan and 21.518 billion yuan,respectively.The proportion of ancillary service costs allocated to load is relatively high-about five times that of the ancillary service costs allocated to renewable energy-and the higher the penetration rate of renewable energy,the higher the proportion of ancillary service costs allocated to renewable energy.
文摘非侵入式负荷监测(non-intrusive load monitoring,NILM)技术对于实现智慧用电与管理具有重要意义。针对现有的非侵入式负荷监测方法在高噪声环境下对特征相似电器以及微小负荷变化监测精度不足的难题,提出了一种基于单位力操作视觉变换器的非侵入式负荷监测(non-intrusive load monitoring based on unit force operated vision transformer,UFONILM)模型的非侵入式负荷监测的深度学习框架。UFONILM模型的单位力操作(unit force operated,UFO)模块通过层归一化和一系列卷积层有效地提取和利用了多尺度的时间序列数据,特征。在标准的UK-DALE数据集上进行的实验显示,UFONILM模型在准确性和F1得分上均优于现有方法,特别是在细粒度的负荷监测场景中。研制了基于UFONILM模型的嵌入式系统,实现了边缘计算的非侵入式负荷监测,可实时监测和响应电网中的异常用电行为,如违规充电事件。实验检测证明,UFONILM模型嵌入式非侵入式负荷监测方法在监测效率方面具有显著的提升,具有高效、便捷安装、可扩展等特点。
