In an offshore wind farm connected with a high-voltage direct current(HVDC)transmission system based on modular multilevel converter(MMC),a symmetric fault on the outgoing line at the sending end(SFOLSE)exhibits compl...In an offshore wind farm connected with a high-voltage direct current(HVDC)transmission system based on modular multilevel converter(MMC),a symmetric fault on the outgoing line at the sending end(SFOLSE)exhibits complex controlled characteristics in the fault current,which can undermine the reliability of relay protection.Detailed analysis of the control interaction between the wind farm and the MMC sending station(MMCSS)is conducted to ascertain the fault current characteristics.Considering the constraints imposed by the existence of a stable operating point(SOP)during SFOLSE,the phase angle difference distribution law for short-circuit currents sourced from both the wind farm and MMCSS is analyzed.Furthermore,the influence of control interaction on the reliability of distance protection is discussed.The results show that the additional impedance exhibits specific distribution characteristics under the influence of control interaction.In addition,the setting ratio of the dq-axis current reference for wind farm distance protection is analyzed,and the impact of wind farm control on the adaptability of distance protection under the constraints of the grid-connected guideline is evaluated.The main risk scenarios of misoperation are clarified,and the correctness of the analytical results is validated through PSCAD time-domain simulations.展开更多
With the increasing complexity and dynamic nature of modern power grids,adaptive protection mechanisms have emerged as a promising solution to address the challenges faced by traditional protection systems.These syste...With the increasing complexity and dynamic nature of modern power grids,adaptive protection mechanisms have emerged as a promising solution to address the challenges faced by traditional protection systems.These systems dynamically adjust their settings based on real-time grid conditions,enabling faster fault detection,improved fault isolation,and enhanced system stability.The need for such adaptive protection systems has become more pronounced as power systems evolve with the integration of renewable energy sources,distributed generation,and microgrids.By leveraging advanced technologies such as machine learning,artificial intelligence,smart sensors,and wide-area measurement systems,adaptive protection mechanisms provide a more flexible and resilient approach to grid protection.This paper explores the definition,key features,enabling technologies,and applications of adaptive protection in power systems,with a focus on its role in distribution networks,transmission networks,and the integration of renewable energy sources.The integration of adaptive protection into modern power grids is essential for maintaining grid reliability,minimizing service interruptions,and optimizing the overall performance of power systems.展开更多
Maloperation of conventional relays is becoming prevalent due to ever increase in complexity of conventional power grids.They are dominant during power system contingencies like power swing,load encroachment,voltage i...Maloperation of conventional relays is becoming prevalent due to ever increase in complexity of conventional power grids.They are dominant during power system contingencies like power swing,load encroachment,voltage instability,unbalanced loading,etc.In these situations,adaptive supervised wide-area backup protection(ASWABP)plays a major role in enhancing the power system reliability.A balance between security and dependability of protection is essential to maintain the reliability.This paper proposes multi-phasor measurement units(MPMU)based ASWABP scheme that can function effectively during faults besides power system contingencies.MPMU is an extended version of Phasor Measurement Unit(PMU).It is an intelligent electronic device which estimates the synchronized predominant harmonic phasors(100Hz and 150Hz)along with the fundamental phasors(50Hz)of three phase voltages and currents with high precision.The proposed ASWABP scheme can detect the fault,identify the parent bus,determine the faulty branch and classify the faults using MPMU measurements at System Protection Center(SPC).Based on these MPMU measurements(received at phasor data concentrator(PDC)at SPC)the appropriate relays will be supervised to enhance the overall reliability of the power grid.Numerous case studies are conducted on WSCC-9 bus and IEEE-14 bus systems to illustrate the security and dependability attributes of proposed ASWABP scheme in MATLAB/Simulink environment.Also,comparative studies are performed with the existing conventional distance protection(Mho relays)for corroborating the superiority of the proposed scheme regarding security and dependability.Comparative studies have shown that the proposed scheme can be used as adaptive supervised wide-area backup protection of conventional distance protection.展开更多
Smart networks such as active distribution network(ADN)and microgrid(MG)play an important role in power system operation.The design and implementation of appropriate protection systems for MG and ADN must be addressed...Smart networks such as active distribution network(ADN)and microgrid(MG)play an important role in power system operation.The design and implementation of appropriate protection systems for MG and ADN must be addressed,which imposes new technical challenges.This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks(NNs)and multiple sampling points for ADN and MG.The solution is implemented on an edge device.NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure.Multiple sampling points are used to derive a data-driven model,which allows the generalization considering the implementation in physical systems.The adaptive fault detector model is implemented on a Jetson Nano system,which is a single-board computer(SBC)with a small graphic processing unit(GPU)intended to run machine learning loads at the edge.The proposed method is tested in a physical,real-life,low-voltage network located at Universidad del Norte,Colombia.This testing network is based on the IEEE 13-node test feeder scaled down to 220 V.The validation in a simulation environment shows the accuracy and dependability above 99.6%,while the real-time tests show the accuracy and dependability of 95.5%and 100%,respectively.Without hard-to-derive parameters,the easy-to-implement embedded model highlights the potential for real-life applications.展开更多
基金supported by the National Natural Science Foundation of China(No.51977019).
文摘In an offshore wind farm connected with a high-voltage direct current(HVDC)transmission system based on modular multilevel converter(MMC),a symmetric fault on the outgoing line at the sending end(SFOLSE)exhibits complex controlled characteristics in the fault current,which can undermine the reliability of relay protection.Detailed analysis of the control interaction between the wind farm and the MMC sending station(MMCSS)is conducted to ascertain the fault current characteristics.Considering the constraints imposed by the existence of a stable operating point(SOP)during SFOLSE,the phase angle difference distribution law for short-circuit currents sourced from both the wind farm and MMCSS is analyzed.Furthermore,the influence of control interaction on the reliability of distance protection is discussed.The results show that the additional impedance exhibits specific distribution characteristics under the influence of control interaction.In addition,the setting ratio of the dq-axis current reference for wind farm distance protection is analyzed,and the impact of wind farm control on the adaptability of distance protection under the constraints of the grid-connected guideline is evaluated.The main risk scenarios of misoperation are clarified,and the correctness of the analytical results is validated through PSCAD time-domain simulations.
文摘With the increasing complexity and dynamic nature of modern power grids,adaptive protection mechanisms have emerged as a promising solution to address the challenges faced by traditional protection systems.These systems dynamically adjust their settings based on real-time grid conditions,enabling faster fault detection,improved fault isolation,and enhanced system stability.The need for such adaptive protection systems has become more pronounced as power systems evolve with the integration of renewable energy sources,distributed generation,and microgrids.By leveraging advanced technologies such as machine learning,artificial intelligence,smart sensors,and wide-area measurement systems,adaptive protection mechanisms provide a more flexible and resilient approach to grid protection.This paper explores the definition,key features,enabling technologies,and applications of adaptive protection in power systems,with a focus on its role in distribution networks,transmission networks,and the integration of renewable energy sources.The integration of adaptive protection into modern power grids is essential for maintaining grid reliability,minimizing service interruptions,and optimizing the overall performance of power systems.
文摘Maloperation of conventional relays is becoming prevalent due to ever increase in complexity of conventional power grids.They are dominant during power system contingencies like power swing,load encroachment,voltage instability,unbalanced loading,etc.In these situations,adaptive supervised wide-area backup protection(ASWABP)plays a major role in enhancing the power system reliability.A balance between security and dependability of protection is essential to maintain the reliability.This paper proposes multi-phasor measurement units(MPMU)based ASWABP scheme that can function effectively during faults besides power system contingencies.MPMU is an extended version of Phasor Measurement Unit(PMU).It is an intelligent electronic device which estimates the synchronized predominant harmonic phasors(100Hz and 150Hz)along with the fundamental phasors(50Hz)of three phase voltages and currents with high precision.The proposed ASWABP scheme can detect the fault,identify the parent bus,determine the faulty branch and classify the faults using MPMU measurements at System Protection Center(SPC).Based on these MPMU measurements(received at phasor data concentrator(PDC)at SPC)the appropriate relays will be supervised to enhance the overall reliability of the power grid.Numerous case studies are conducted on WSCC-9 bus and IEEE-14 bus systems to illustrate the security and dependability attributes of proposed ASWABP scheme in MATLAB/Simulink environment.Also,comparative studies are performed with the existing conventional distance protection(Mho relays)for corroborating the superiority of the proposed scheme regarding security and dependability.Comparative studies have shown that the proposed scheme can be used as adaptive supervised wide-area backup protection of conventional distance protection.
基金supported by Universidad del Norte,Fondo Nacional de Financiamiento para la Ciencia,la Tecnología e Innovación FCTEI del sistema general de regalías SGR,and Departamento Administrativo de Ciencia,Tecnología e Innovación-COLCIENCIAS(now Colombian Ministry of Science,Technology,and Innovation-Minciencias)by call contest“Convocatoria 757 de 2017”and“Convocatoria 852-Conectando conocimiento de 2019”-Project Integra2023,code 111085271060,contract 80740-774-2020.
文摘Smart networks such as active distribution network(ADN)and microgrid(MG)play an important role in power system operation.The design and implementation of appropriate protection systems for MG and ADN must be addressed,which imposes new technical challenges.This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks(NNs)and multiple sampling points for ADN and MG.The solution is implemented on an edge device.NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure.Multiple sampling points are used to derive a data-driven model,which allows the generalization considering the implementation in physical systems.The adaptive fault detector model is implemented on a Jetson Nano system,which is a single-board computer(SBC)with a small graphic processing unit(GPU)intended to run machine learning loads at the edge.The proposed method is tested in a physical,real-life,low-voltage network located at Universidad del Norte,Colombia.This testing network is based on the IEEE 13-node test feeder scaled down to 220 V.The validation in a simulation environment shows the accuracy and dependability above 99.6%,while the real-time tests show the accuracy and dependability of 95.5%and 100%,respectively.Without hard-to-derive parameters,the easy-to-implement embedded model highlights the potential for real-life applications.
