The strength of sandstone decreases significantly with higher water content attributing to softening effects.This scenario can pose a severe threat to the stability of reservoirs of pumped storage power stations devel...The strength of sandstone decreases significantly with higher water content attributing to softening effects.This scenario can pose a severe threat to the stability of reservoirs of pumped storage power stations developed from abandoned mines,especially when subjected to the cyclic loading condition caused by the repeated drainage and storage of water(fatigue damage).Based on this,it is essential to focus on the fatigue failure characteristics.In this study,the mineral composition of the used sandstone of Ruineng coal mine in Shanxi Province,China,was first tested to elucidate the rock softening mechanism after absorbing water.Next,a numerical model for replicating the mechanical behavior of water-bearing sandstone was established using twodimensional particle flow code(PFC2D)with a novel contact model.Then,16 uniaxial cyclic loading simulations with distinct loading parameters related to reservoir conditions(loading frequency,amplitude level,and maximum stress level)and different water contents were conducted.The numerical results show that all these three loading parameters affect the failure characteristics of sandstone,including irreversible strain,damage evolution,strain behavior,and fatigue life.The influence degree of these three parameters on failure behavior increases in the order of maximum stress level,loading frequency,and amplitude level.However,for the samples with different water contents,their failure characteristics are similar under the same loading conditions.Furthermore,the failure mode is almost unaffected by the loading parameters,while the water content plays a significant role and causing the transformation from the tensile splitting with low water content to the shear failure with higher water content.展开更多
The recently proposed ambient signal-based load modeling approach offers an important and effective idea to study the time-varying and distributed characteristics of power loads.Meanwhile,it also brings new problems.S...The recently proposed ambient signal-based load modeling approach offers an important and effective idea to study the time-varying and distributed characteristics of power loads.Meanwhile,it also brings new problems.Since the load model parameters of power loads can be obtained in real-time for each load bus,the numerous identified parameters make parameter application difficult.In order to obtain the parameters suitable for off-line applications,load model parameter selection(LMPS)is first introduced in this paper.Meanwhile,the convolution neural network(CNN)is adopted to achieve the selection purpose from the perspective of short-term voltage stability.To begin with,the field phasor measurement unit(PMU)data from China Southern Power Grid are obtained for load model parameter identification,and the identification results of different substations during different times indicate the necessity of LMPS.Meanwhile,the simulation case of Guangdong Power Grid shows the process of LMPS,and the results from the CNNbased LMPS confirm its effectiveness.展开更多
The equivalent impedance parameters of loads have been widely used to identify and locate the harmonic sources.However,the existing calculation methods suffer from outliers caused by the zero-crossing of the denominat...The equivalent impedance parameters of loads have been widely used to identify and locate the harmonic sources.However,the existing calculation methods suffer from outliers caused by the zero-crossing of the denominator.These outliers can result in inaccuracy and unreliability of harmonic source location.To address this issue,this paper proposes an innovative method of equivalent impedance parameter calculation of three-phase symmetrical loads that avoid outliers.The correctness and effectiveness of the proposed method are verified by simulations on Simulink using actual monitoring data.The results show that the proposed method is not only simple and easy to implement but also highly accurate.展开更多
基金This work was supported by the National Natural Science Foundation of China(No.52104125)the funding of State Key Laboratory for GeoMechanics and Deep Underground Engineering,China University of Mining&Technology,Beijing(SKLGDUEK2133)+1 种基金the funding of Key Laboratory of Rock Mechanics and Geohazards of Zhejiang Province(No.ZJRMG-2020-02)the Fundamental Research Funds for the Central Universities.
文摘The strength of sandstone decreases significantly with higher water content attributing to softening effects.This scenario can pose a severe threat to the stability of reservoirs of pumped storage power stations developed from abandoned mines,especially when subjected to the cyclic loading condition caused by the repeated drainage and storage of water(fatigue damage).Based on this,it is essential to focus on the fatigue failure characteristics.In this study,the mineral composition of the used sandstone of Ruineng coal mine in Shanxi Province,China,was first tested to elucidate the rock softening mechanism after absorbing water.Next,a numerical model for replicating the mechanical behavior of water-bearing sandstone was established using twodimensional particle flow code(PFC2D)with a novel contact model.Then,16 uniaxial cyclic loading simulations with distinct loading parameters related to reservoir conditions(loading frequency,amplitude level,and maximum stress level)and different water contents were conducted.The numerical results show that all these three loading parameters affect the failure characteristics of sandstone,including irreversible strain,damage evolution,strain behavior,and fatigue life.The influence degree of these three parameters on failure behavior increases in the order of maximum stress level,loading frequency,and amplitude level.However,for the samples with different water contents,their failure characteristics are similar under the same loading conditions.Furthermore,the failure mode is almost unaffected by the loading parameters,while the water content plays a significant role and causing the transformation from the tensile splitting with low water content to the shear failure with higher water content.
基金supported by the National Natural Science Foundation of China(U2066601,U1766214).
文摘The recently proposed ambient signal-based load modeling approach offers an important and effective idea to study the time-varying and distributed characteristics of power loads.Meanwhile,it also brings new problems.Since the load model parameters of power loads can be obtained in real-time for each load bus,the numerous identified parameters make parameter application difficult.In order to obtain the parameters suitable for off-line applications,load model parameter selection(LMPS)is first introduced in this paper.Meanwhile,the convolution neural network(CNN)is adopted to achieve the selection purpose from the perspective of short-term voltage stability.To begin with,the field phasor measurement unit(PMU)data from China Southern Power Grid are obtained for load model parameter identification,and the identification results of different substations during different times indicate the necessity of LMPS.Meanwhile,the simulation case of Guangdong Power Grid shows the process of LMPS,and the results from the CNNbased LMPS confirm its effectiveness.
基金supported by the National Natural Science Foundation of China(No.51777035).
文摘The equivalent impedance parameters of loads have been widely used to identify and locate the harmonic sources.However,the existing calculation methods suffer from outliers caused by the zero-crossing of the denominator.These outliers can result in inaccuracy and unreliability of harmonic source location.To address this issue,this paper proposes an innovative method of equivalent impedance parameter calculation of three-phase symmetrical loads that avoid outliers.The correctness and effectiveness of the proposed method are verified by simulations on Simulink using actual monitoring data.The results show that the proposed method is not only simple and easy to implement but also highly accurate.
文摘分布式电源(distributed generation,DG)出力的间歇性、随机性和波动性,增加了配电网负荷供应能力(loadsupply capacity,LSC)评估的复杂性。针对传统不确定性方法难以全面地体现LSC的客观不确定性和认知不确定性,提出一种基于仿射数学的变步长重复潮流法(theaffinealgorithm based step-varied repeated power flow method,AA-SVRPF)。利用概率函数的分布参数区间建立风速和光照强度的参数化P-box模型。通过AA直接求得LSC的焦元,结合证据理论生成负荷增长百分比的证据结构体形式。针对计算过程中存在多层循环嵌套、计算效率低的问题,提出基于矩阵运算的快速求解策略,提高了AA-SVRPF的计算效率。为体现不同供电水平时的LSC的不确定性,采用静态负荷供应风险概率作为LSC的度量指标。通过算例的对比分析,结果验证了该方法的可行性和有效性。
