To address the issue of transient low-voltage instability in AC-DC hybrid power systems following large disturbances,conventional voltage assessment and control strategies typically adopt a sequential“assess-then-act...To address the issue of transient low-voltage instability in AC-DC hybrid power systems following large disturbances,conventional voltage assessment and control strategies typically adopt a sequential“assess-then-act”paradigm,which struggles to simultaneously meet the requirements for both high accuracy and rapid response.This paper proposes a transient voltage assessment and control method based on a hybrid neural network incorporated with an improved snow ablation optimization(ISAO)algorithm.The core innovation of the proposed method lies in constructing an intelligent“physics-informed and neural network-integrated”framework,which achieves the integration of stability assessment and control strategy generation.Firstly,to construct a highly correlated input set,response characteristics reflecting the system’s voltage stable/unstable states are screened.Simultaneously,the transient voltage severity index(TVSI)is introduced as a comprehensive metric to quantify the system’s post-disturbance transient voltage performance.Furthermore,the load bus voltage sensitivity index(LVSI)is defined as the ratio of the voltage change magnitude at a load node(or bus)to the change in the system-level TVSI,thereby pinpointing the response characteristics of critical load nodes.Secondly,both the transient voltage stability assessment result and its corresponding under-voltage load shedding(UVLS)control amount are jointly utilized as the outputs of the response-driven model.Subsequently,the snow ablation optimization(SAO)algorithm is enhanced using a good point set strategy and a Gaussian mutation strategy.This improved algorithm is then employed to optimize the key hyperparameters of the hybrid neural network.Finally,the superiority of the proposed method is validated on a modified CEPRI-36 system and an actual power grid case.Comparisons with various artificial intelligence methods demonstrate its significant advantages in model speed and accuracy.Additionally,when compared to traditional emergency control schemes and UVLS strategies,the proposed method exhibits exceptional rapidness and real-time capability in control decision-making.展开更多
在两级式AC-DC变换器中,前级功率因数校正(power factor correction,PFC)固有的瞬时功率波动特性会造成母线电压存在二倍频纹波,影响后级CLLLC谐振变换器的输出电压质量。针对以上问题,该文提出了基于二阶广义积分(second order general...在两级式AC-DC变换器中,前级功率因数校正(power factor correction,PFC)固有的瞬时功率波动特性会造成母线电压存在二倍频纹波,影响后级CLLLC谐振变换器的输出电压质量。针对以上问题,该文提出了基于二阶广义积分(second order generalized integral,SOGI)的可变增益母线电压纹波前馈控制方法。采用SOGI提取母线电压纹波信息,基于品质因数Q与电压增益的关系和母线电压纹波对归一化频率的影响,解析了母线电压纹波对CLLLC谐振变换器输出电压的影响机理,得到Q值与前馈增益系数Ka的关系,采用仿真寻优加数据拟合的方法得到前馈可变增益系数曲线。仿真和实验结果表明,相比于无前馈控制,所提控制方法对CLLLC谐振变换器的输出电压纹波具有较好的抑制效果,输出电压纹波降低了72%,验证了所提算法的有效性。展开更多
基金supported by the State Grid Shanxi Electric Power Company science and technology project“Research on Key Technologies for Voltage Stability Analysis and Control of UHV Transmission Sending-End Grid with Large-Scale Integration of Wind-Solar-Storage Systems”(520530240026).
文摘To address the issue of transient low-voltage instability in AC-DC hybrid power systems following large disturbances,conventional voltage assessment and control strategies typically adopt a sequential“assess-then-act”paradigm,which struggles to simultaneously meet the requirements for both high accuracy and rapid response.This paper proposes a transient voltage assessment and control method based on a hybrid neural network incorporated with an improved snow ablation optimization(ISAO)algorithm.The core innovation of the proposed method lies in constructing an intelligent“physics-informed and neural network-integrated”framework,which achieves the integration of stability assessment and control strategy generation.Firstly,to construct a highly correlated input set,response characteristics reflecting the system’s voltage stable/unstable states are screened.Simultaneously,the transient voltage severity index(TVSI)is introduced as a comprehensive metric to quantify the system’s post-disturbance transient voltage performance.Furthermore,the load bus voltage sensitivity index(LVSI)is defined as the ratio of the voltage change magnitude at a load node(or bus)to the change in the system-level TVSI,thereby pinpointing the response characteristics of critical load nodes.Secondly,both the transient voltage stability assessment result and its corresponding under-voltage load shedding(UVLS)control amount are jointly utilized as the outputs of the response-driven model.Subsequently,the snow ablation optimization(SAO)algorithm is enhanced using a good point set strategy and a Gaussian mutation strategy.This improved algorithm is then employed to optimize the key hyperparameters of the hybrid neural network.Finally,the superiority of the proposed method is validated on a modified CEPRI-36 system and an actual power grid case.Comparisons with various artificial intelligence methods demonstrate its significant advantages in model speed and accuracy.Additionally,when compared to traditional emergency control schemes and UVLS strategies,the proposed method exhibits exceptional rapidness and real-time capability in control decision-making.
