基于Well-being理论对风储混合电力系统进行了充裕度评估。所采用的Well-being充裕度指标中考虑了N-1确定性准则,不仅能计及发电系统的充裕性,还能计及输电系统的充裕性,因此更加合理且适用于发输电系统的充裕度评估。以简单的RBTS(Roy ...基于Well-being理论对风储混合电力系统进行了充裕度评估。所采用的Well-being充裕度指标中考虑了N-1确定性准则,不仅能计及发电系统的充裕性,还能计及输电系统的充裕性,因此更加合理且适用于发输电系统的充裕度评估。以简单的RBTS(Roy Billinton test system)以及规模稍大的IEEE-RTS79为算例,仿真分析了风电及电池储能系统(battery energy storage system,BESS)并网对电力系统充裕度的影响。结果表明,与风电场的集中布置相比,分散布置更有利于系统的充裕性;BESS对含风电场的电力系统充裕度提升作用明显,在电力系统瞬时备用控制中具有重要的作用。展开更多
在确定性规划准则和概率性规划准则的基础上,提出一个应用于输电网中长期规划的混合性规划模型。该模型采用静态安全柔性约束、故障发生概率、可靠性成本、投资费用、期望缺供电量(expected energy not served,EENS)以及缺电时间期望(lo...在确定性规划准则和概率性规划准则的基础上,提出一个应用于输电网中长期规划的混合性规划模型。该模型采用静态安全柔性约束、故障发生概率、可靠性成本、投资费用、期望缺供电量(expected energy not served,EENS)以及缺电时间期望(loss of load expectation,LOLE)等因素来计算、评估潜在故障的影响,并通过建立电网成本期望值最小的目标函数、经济性约束和可靠性约束保证电网规划的可靠性和经济性,利用直流潮流模型和最优潮流模型将输电网规划问题转为混合整数线性规划问题,采用商业优化软件CPLEX进行寻优计算,实现了为输电网规划寻找最优规划方案的目的。最后,通过与传统规划模型的比较和实际电网算例验证了模型的合理性、有效性和经济性。展开更多
In this paper,a blind multiband spectrum sensing(BMSS)method requiring no knowledge of noise power,primary signal and wireless channel is proposed based on the K-means clustering(KMC).In this approach,the KMC algorith...In this paper,a blind multiband spectrum sensing(BMSS)method requiring no knowledge of noise power,primary signal and wireless channel is proposed based on the K-means clustering(KMC).In this approach,the KMC algorithm is used to identify the occupied subband set(OSS)and the idle subband set(ISS),and then the location and number information of the occupied channels are obtained according to the elements in the OSS.Compared with the classical BMSS methods based on the information theoretic criteria(ITC),the new method shows more excellent performance especially in the low signal-to-noise ratio(SNR)and the small sampling number scenarios,and more robust detection performance in noise uncertainty or unequal noise variance applications.Meanwhile,the new method performs more stablely than the ITC-based methods when the occupied subband number increases or the primary signals suffer multi-path fading.Simulation result verifies the effectiveness of the proposed method.展开更多
The uncertainties of some key influence factors on coal crushing,such as rock strength,pore pressure and magnitude and orientation of three principal stresses,can lead to the uncertainty of coal crushing and make it v...The uncertainties of some key influence factors on coal crushing,such as rock strength,pore pressure and magnitude and orientation of three principal stresses,can lead to the uncertainty of coal crushing and make it very difficult to predict coal crushing under the condition of in-situ reservoir.To account for the uncertainty involved in coal crushing,a deterministic prediction model of coal crushing under the condition of in-situ reservoir was established based on Hoek-Brown criterion.Through this model,key influence factors on coal crushing were selected as random variables and the corresponding probability density functions were determined by combining experiment data and Latin Hypercube method.Then,to analyze the uncertainty of coal crushing,the firstorder second-moment method and the presented model were combined to address the failure probability involved in coal crushing analysis.Using the presented method,the failure probabilities of coal crushing were analyzed for WS5-5 well in Ningwu basin,China,and the relations between failure probability and the influence factors were furthermore discussed.The results show that the failure probabilities of WS5-5 CBM well vary from 0.6 to 1.0; moreover,for the coal seam section at depth of 784.3-785 m,the failure probabilities are equal to 1,which fit well with experiment results; the failure probability of coal crushing presents nonlinear growth relationships with the increase of principal stress difference and the decrease of uniaxial compressive strength.展开更多
Measurement uncertainty plays an important role in laser tracking measurement analyses. In the present work, the guides to the expression of uncertainty in measurement(GUM) uncertainty framework(GUF) and its supplemen...Measurement uncertainty plays an important role in laser tracking measurement analyses. In the present work, the guides to the expression of uncertainty in measurement(GUM) uncertainty framework(GUF) and its supplement, the Monte Carlo method, were used to estimate the uncertainty of task-specific laser tracker measurements. First, the sources of error in laser tracker measurement were analyzed in detail, including instruments, measuring network fusion, measurement strategies, measurement process factors(such as the operator), measurement environment, and task-specific data processing. Second, the GUM and Monte Carlo methods and their application to laser tracker measurement were presented. Finally, a case study involving the uncertainty estimation of a cylindricity measurement process using the GUF and Monte Carlo methods was illustrated. The expanded uncertainty results(at 95% confidence levels) obtained with the Monte Carlo method are 0.069 mm(least-squares criterion) and 0.062 mm(minimum zone criterion), respectively, while with the GUM uncertainty framework, none but the result of least-squares criterion can be got, which is 0.071 mm. Thus, the GUM uncertainty framework slightly underestimates the overall uncertainty by 10%. The results demonstrate that the two methods have different characteristics in task-specific uncertainty evaluations of laser tracker measurements. The results indicate that the Monte Carlo method is a practical tool for applying the principle of propagation of distributions and does not depend on the assumptions and limitations required by the law of propagation of uncertainties(GUF). These features of the Monte Carlo method reduce the risk of an unreliable measurement of uncertainty estimation, particularly in cases of complicated measurement models, without the need to evaluate partial derivatives. In addition, the impact of sampling strategy and evaluation method on the uncertainty of the measurement results can also be taken into account with Monte Carlo method, which plays a guiding role in measurement planning.展开更多
文摘基于Well-being理论对风储混合电力系统进行了充裕度评估。所采用的Well-being充裕度指标中考虑了N-1确定性准则,不仅能计及发电系统的充裕性,还能计及输电系统的充裕性,因此更加合理且适用于发输电系统的充裕度评估。以简单的RBTS(Roy Billinton test system)以及规模稍大的IEEE-RTS79为算例,仿真分析了风电及电池储能系统(battery energy storage system,BESS)并网对电力系统充裕度的影响。结果表明,与风电场的集中布置相比,分散布置更有利于系统的充裕性;BESS对含风电场的电力系统充裕度提升作用明显,在电力系统瞬时备用控制中具有重要的作用。
文摘在确定性规划准则和概率性规划准则的基础上,提出一个应用于输电网中长期规划的混合性规划模型。该模型采用静态安全柔性约束、故障发生概率、可靠性成本、投资费用、期望缺供电量(expected energy not served,EENS)以及缺电时间期望(loss of load expectation,LOLE)等因素来计算、评估潜在故障的影响,并通过建立电网成本期望值最小的目标函数、经济性约束和可靠性约束保证电网规划的可靠性和经济性,利用直流潮流模型和最优潮流模型将输电网规划问题转为混合整数线性规划问题,采用商业优化软件CPLEX进行寻优计算,实现了为输电网规划寻找最优规划方案的目的。最后,通过与传统规划模型的比较和实际电网算例验证了模型的合理性、有效性和经济性。
基金Projects(61362018,61861019)supported by the National Natural Science Foundation of ChinaProject(1402041B)supported by the Jiangsu Province Postdoctoral Scientific Research Project,China+1 种基金Project(16A174)supported by the Scientific Research Fund of Hunan Provincial Education Department,ChinaProject([2016]283)supported by the Research Study and Innovative Experiment Project of College Students,China
文摘In this paper,a blind multiband spectrum sensing(BMSS)method requiring no knowledge of noise power,primary signal and wireless channel is proposed based on the K-means clustering(KMC).In this approach,the KMC algorithm is used to identify the occupied subband set(OSS)and the idle subband set(ISS),and then the location and number information of the occupied channels are obtained according to the elements in the OSS.Compared with the classical BMSS methods based on the information theoretic criteria(ITC),the new method shows more excellent performance especially in the low signal-to-noise ratio(SNR)and the small sampling number scenarios,and more robust detection performance in noise uncertainty or unequal noise variance applications.Meanwhile,the new method performs more stablely than the ITC-based methods when the occupied subband number increases or the primary signals suffer multi-path fading.Simulation result verifies the effectiveness of the proposed method.
基金Project(51204201)supported by the National Natural Science Foundation of ChinaProjects(2011ZX05036-001,2011ZX05037-004)supported by the National Science and Technology Major Program of China+1 种基金Project(2010CB226706)supported by the National Basic Research Program of ChinaProject(11CX04050A)supported by the Fundamental Research Funds for the Central Universities of China
文摘The uncertainties of some key influence factors on coal crushing,such as rock strength,pore pressure and magnitude and orientation of three principal stresses,can lead to the uncertainty of coal crushing and make it very difficult to predict coal crushing under the condition of in-situ reservoir.To account for the uncertainty involved in coal crushing,a deterministic prediction model of coal crushing under the condition of in-situ reservoir was established based on Hoek-Brown criterion.Through this model,key influence factors on coal crushing were selected as random variables and the corresponding probability density functions were determined by combining experiment data and Latin Hypercube method.Then,to analyze the uncertainty of coal crushing,the firstorder second-moment method and the presented model were combined to address the failure probability involved in coal crushing analysis.Using the presented method,the failure probabilities of coal crushing were analyzed for WS5-5 well in Ningwu basin,China,and the relations between failure probability and the influence factors were furthermore discussed.The results show that the failure probabilities of WS5-5 CBM well vary from 0.6 to 1.0; moreover,for the coal seam section at depth of 784.3-785 m,the failure probabilities are equal to 1,which fit well with experiment results; the failure probability of coal crushing presents nonlinear growth relationships with the increase of principal stress difference and the decrease of uniaxial compressive strength.
基金Project(51318010402)supported by General Armament Department Pre-Research Program of China
文摘Measurement uncertainty plays an important role in laser tracking measurement analyses. In the present work, the guides to the expression of uncertainty in measurement(GUM) uncertainty framework(GUF) and its supplement, the Monte Carlo method, were used to estimate the uncertainty of task-specific laser tracker measurements. First, the sources of error in laser tracker measurement were analyzed in detail, including instruments, measuring network fusion, measurement strategies, measurement process factors(such as the operator), measurement environment, and task-specific data processing. Second, the GUM and Monte Carlo methods and their application to laser tracker measurement were presented. Finally, a case study involving the uncertainty estimation of a cylindricity measurement process using the GUF and Monte Carlo methods was illustrated. The expanded uncertainty results(at 95% confidence levels) obtained with the Monte Carlo method are 0.069 mm(least-squares criterion) and 0.062 mm(minimum zone criterion), respectively, while with the GUM uncertainty framework, none but the result of least-squares criterion can be got, which is 0.071 mm. Thus, the GUM uncertainty framework slightly underestimates the overall uncertainty by 10%. The results demonstrate that the two methods have different characteristics in task-specific uncertainty evaluations of laser tracker measurements. The results indicate that the Monte Carlo method is a practical tool for applying the principle of propagation of distributions and does not depend on the assumptions and limitations required by the law of propagation of uncertainties(GUF). These features of the Monte Carlo method reduce the risk of an unreliable measurement of uncertainty estimation, particularly in cases of complicated measurement models, without the need to evaluate partial derivatives. In addition, the impact of sampling strategy and evaluation method on the uncertainty of the measurement results can also be taken into account with Monte Carlo method, which plays a guiding role in measurement planning.
