According to the characteristics of the correlation of multiple wind farm output, this paper put forwards a modeling method based on fuzzy c-means clustering and the copula function, and correlation wind farms are ins...According to the characteristics of the correlation of multiple wind farm output, this paper put forwards a modeling method based on fuzzy c-means clustering and the copula function, and correlation wind farms are inserted into IEEE-RTS79 reliability system for risk assessment. By the probabilistic load flow calculated by Monte Carlo simulation method, the probability of the accident is derived, and bus voltage and branch power flow overload risk index are defined in this paper. The results show that this method can realize the modeling of the correlation of wind power output, and the risk index can identify the weakness of the system, which can provide reference for the operation and maintenance personnel.展开更多
Wind speed dependences on different areas in a wind farm have influences on security and economic operation in power system.In order to simulate the correlation of wind speed series between different positions,this pa...Wind speed dependences on different areas in a wind farm have influences on security and economic operation in power system.In order to simulate the correlation of wind speed series between different positions,this paper applies Copula function and rank correlation matrix methods to measure the coherence of wind speed in a wind farm.The correlated wind sample space is established.According to active power output characteristics of wind turbines,the polymerization model in a wind farm can be achieved.Monte Carlo optimal power flow is applied to IEEE-30 and IEEE-300 bus systems based on the principle of energy saving dispatching.The study shows that the accuracy of outputs is improved,thus reducing the fluctuation ranges in unit generating costs and power flow in branches while considering wind speed polymerization.This approach provides a new method to improve the effectiveness of energy saving dispatching and system operation arrangement.Results have been tested to be effective.展开更多
A mature mathematical technique called copula joint function is introduced in this paper, which is commonly used in the financial risk analysis to estimate uncertainty. The joint function is generalized to the n-dimen...A mature mathematical technique called copula joint function is introduced in this paper, which is commonly used in the financial risk analysis to estimate uncertainty. The joint function is generalized to the n-dimensional Frank’s copula. In addition, we adopt two attenuation models proposed by YU and Boore et al, respectively, and construct a two-dimensional copula joint probabilistic function as an example to illustrate the uncertainty treatment at low probability. The results show that copula joint function gives us a better prediction of peak ground motion than that resultant from the simple linear weight technique which is commonly used in the traditional logic-tree treatment of model uncertainties. In light of widespread application in the risk analysis from financial investment to insurance assessment, we believe that the copula-based technique will have a potential application in the seismic hazard analysis.展开更多
Test selection design(TSD)is an important technique for improving product maintainability,reliability and reducing lifecycle costs.In recent years,although some researchers have addressed the design problem of test se...Test selection design(TSD)is an important technique for improving product maintainability,reliability and reducing lifecycle costs.In recent years,although some researchers have addressed the design problem of test selection,the correlation between test outcomes has not been sufficiently considered in test metrics modeling.This study proposes a new approach that combines copula and D-Vine copula to address the correlation issue in TSD.First,the copula is utilized to model FIR on the joint distribution.Furthermore,the D-Vine copula is applied to model the FDR and FAR.Then,a particle swarm optimization is employed to select the optimal testing scheme.Finally,the efficacy of the proposed method is validated through experimentation on a negative feedback circuit.展开更多
Interval prediction of wind power,which features the upper and lower limits of wind power at a given confidence level,plays a significant role in accurate prediction and stability of the power grid integrated with win...Interval prediction of wind power,which features the upper and lower limits of wind power at a given confidence level,plays a significant role in accurate prediction and stability of the power grid integrated with wind power.However,the conventional methods of interval prediction are commonly based on a hypothetic probability distribution function,which neglects the correlations among various variables,leading to the decrease of prediction accuracy.Therefore,we improve the multi-objective interval prediction based on the conditional copula function,through which we can fully utilize the correlations among variables to improve prediction accuracy without an assumed probability distribution function.We use the multi-objective optimization method of nondominated sorting genetic algorithm-II(NSGA-II)to obtain the optimal solution set.The particular best solution is weighted by the prediction interval average width(PIAW)and prediction interval coverage probability(PICP)to pick the optimized solution in practical examples.Finally,we apply the proposed method to three wind power plants in different cities in China as examples forvalidation and obtain higher prediction accuracy compared with other methods,i.e.,relevance vector machine(RVM),artificial neural network(ANN),and particle swarm optimization kernel extreme learning machine(PSO-KELM).These results demonstrate the superiority and practicability of this method in interval prediction of wind power.展开更多
为准确选取模拟节理岩体结构面产状互相关性的Copula函数,提出了不同拟合指标下模拟节理岩体结构面产状的Copula函数方法,通过采用最小平方欧式、AIC(Akaike information criterion)信息准则、BIC(Bayesian information criterion)信息...为准确选取模拟节理岩体结构面产状互相关性的Copula函数,提出了不同拟合指标下模拟节理岩体结构面产状的Copula函数方法,通过采用最小平方欧式、AIC(Akaike information criterion)信息准则、BIC(Bayesian information criterion)信息准则这3种拟合指标确定各自的最优Copula函数并通过MATLAB确定实测产状数据的最优边缘分布,建立倾角和倾向的二维联合分布函数。同时结合蒙特卡洛抽样法自动生成模拟数据,将数据导入Dips软件中进行可视化处理,得到产状的赤平投影图,对比实测的倾角和倾向数据和不同拟合指标下确定的Copula函数模拟数据间的差异。最后,基于工程案例检验方法的有效性。结果表明:不同的拟合指标会产生不同的Copula函数,对模拟产状的有效性也会有较大差异,若是选择不当的拟合指标可能导致选择不准确的Copula函数,从而使模型无法准确地捕捉数据的相关结构和特征;不适当的拟合指标可能导致拟合模型与真实数据之间存在较大的误差,使得模型的预测能力和解释能力下降,就本文案例表明在最小平方欧式值拟合指标下选择的Gaussian Copula函数拟合实测数据效果最好。此研究将有助在应用Coupla函数时选用恰当的拟合指标。展开更多
在林业研究中,胸径-树高二元联合分布多由相同边缘分布构造,而林分的胸径与树高的实际分布状况可能有所差异。为降低这种差异带来的影响,依据佳木斯市孟家岗林场的115块长白落叶松人工林数据,选择适用条件低、适应范围广的Copula函数方...在林业研究中,胸径-树高二元联合分布多由相同边缘分布构造,而林分的胸径与树高的实际分布状况可能有所差异。为降低这种差异带来的影响,依据佳木斯市孟家岗林场的115块长白落叶松人工林数据,选择适用条件低、适应范围广的Copula函数方法拟合落叶松胸径-树高二元联合分布模型。首先选择威布尔(Weibull)、广义威布尔(G-Weibull)、逻辑斯蒂(Logistic)、轻量逻辑斯蒂(Logit-Logistic)、伽马(Gamma)、对数正态(Log-Normal)6个分布函数作为备选基础模型,根据K-S(kolmogorov smirnov test)检验与半参数估计结果筛选并构建Copula胸径-树高二元联合分布模型,再通过负对数似然(negative log-likelihood,NLL)、Sn拟合优度统计量和似然比检验(likelihood ratio test,LRT)与二元对数logistic分布函数和二元Weibull分布函数进行比较,最后使用雷诺误差指数(error index of Reynolds,EI)对模型预测能力进行评估。结果表明,基于Copula函数的二元分拟合结果与模型(EI=0.3184)预估能力皆优于二元Weibull分布(EI=0.6381)和二元对数Logistic分布(EI=0.9490),说明此方法构建胸径-树高二元联合Copula分布模型能够很好地描述落叶松人工林胸径树高联合分布,以Copula方法构建树高-胸径联合分布是可行的。展开更多
文摘According to the characteristics of the correlation of multiple wind farm output, this paper put forwards a modeling method based on fuzzy c-means clustering and the copula function, and correlation wind farms are inserted into IEEE-RTS79 reliability system for risk assessment. By the probabilistic load flow calculated by Monte Carlo simulation method, the probability of the accident is derived, and bus voltage and branch power flow overload risk index are defined in this paper. The results show that this method can realize the modeling of the correlation of wind power output, and the risk index can identify the weakness of the system, which can provide reference for the operation and maintenance personnel.
文摘Wind speed dependences on different areas in a wind farm have influences on security and economic operation in power system.In order to simulate the correlation of wind speed series between different positions,this paper applies Copula function and rank correlation matrix methods to measure the coherence of wind speed in a wind farm.The correlated wind sample space is established.According to active power output characteristics of wind turbines,the polymerization model in a wind farm can be achieved.Monte Carlo optimal power flow is applied to IEEE-30 and IEEE-300 bus systems based on the principle of energy saving dispatching.The study shows that the accuracy of outputs is improved,thus reducing the fluctuation ranges in unit generating costs and power flow in branches while considering wind speed polymerization.This approach provides a new method to improve the effectiveness of energy saving dispatching and system operation arrangement.Results have been tested to be effective.
基金Project of Institute of Crustal Dynamics, China Earthquake Administration (ZDJ2007-1)One Hundred Individual Program of Chinese Academy of Sciences (99M2009M02) National Natural Science Foundation of China (40574022)
文摘A mature mathematical technique called copula joint function is introduced in this paper, which is commonly used in the financial risk analysis to estimate uncertainty. The joint function is generalized to the n-dimensional Frank’s copula. In addition, we adopt two attenuation models proposed by YU and Boore et al, respectively, and construct a two-dimensional copula joint probabilistic function as an example to illustrate the uncertainty treatment at low probability. The results show that copula joint function gives us a better prediction of peak ground motion than that resultant from the simple linear weight technique which is commonly used in the traditional logic-tree treatment of model uncertainties. In light of widespread application in the risk analysis from financial investment to insurance assessment, we believe that the copula-based technique will have a potential application in the seismic hazard analysis.
基金supported by the National Natural Science Foundation of China(No.62303293,62303414)the China Postdoctoral Science Foundation(No.2023M732176,2023M741821)the Zhejiang Province Postdoctoral Selected Foundation(No.ZJ2023143).
文摘Test selection design(TSD)is an important technique for improving product maintainability,reliability and reducing lifecycle costs.In recent years,although some researchers have addressed the design problem of test selection,the correlation between test outcomes has not been sufficiently considered in test metrics modeling.This study proposes a new approach that combines copula and D-Vine copula to address the correlation issue in TSD.First,the copula is utilized to model FIR on the joint distribution.Furthermore,the D-Vine copula is applied to model the FDR and FAR.Then,a particle swarm optimization is employed to select the optimal testing scheme.Finally,the efficacy of the proposed method is validated through experimentation on a negative feedback circuit.
基金supported by the National Natural Science Foundation of China(No.51507141)Key research and development plan of Shaanxi Province(No.2018ZDCXL-GY-10-04)+1 种基金the National Key Research and Development Program of China(No.2016YFC0401409)the Shaanxi provincial education office fund(No.17JK0547).
文摘Interval prediction of wind power,which features the upper and lower limits of wind power at a given confidence level,plays a significant role in accurate prediction and stability of the power grid integrated with wind power.However,the conventional methods of interval prediction are commonly based on a hypothetic probability distribution function,which neglects the correlations among various variables,leading to the decrease of prediction accuracy.Therefore,we improve the multi-objective interval prediction based on the conditional copula function,through which we can fully utilize the correlations among variables to improve prediction accuracy without an assumed probability distribution function.We use the multi-objective optimization method of nondominated sorting genetic algorithm-II(NSGA-II)to obtain the optimal solution set.The particular best solution is weighted by the prediction interval average width(PIAW)and prediction interval coverage probability(PICP)to pick the optimized solution in practical examples.Finally,we apply the proposed method to three wind power plants in different cities in China as examples forvalidation and obtain higher prediction accuracy compared with other methods,i.e.,relevance vector machine(RVM),artificial neural network(ANN),and particle swarm optimization kernel extreme learning machine(PSO-KELM).These results demonstrate the superiority and practicability of this method in interval prediction of wind power.
文摘为准确选取模拟节理岩体结构面产状互相关性的Copula函数,提出了不同拟合指标下模拟节理岩体结构面产状的Copula函数方法,通过采用最小平方欧式、AIC(Akaike information criterion)信息准则、BIC(Bayesian information criterion)信息准则这3种拟合指标确定各自的最优Copula函数并通过MATLAB确定实测产状数据的最优边缘分布,建立倾角和倾向的二维联合分布函数。同时结合蒙特卡洛抽样法自动生成模拟数据,将数据导入Dips软件中进行可视化处理,得到产状的赤平投影图,对比实测的倾角和倾向数据和不同拟合指标下确定的Copula函数模拟数据间的差异。最后,基于工程案例检验方法的有效性。结果表明:不同的拟合指标会产生不同的Copula函数,对模拟产状的有效性也会有较大差异,若是选择不当的拟合指标可能导致选择不准确的Copula函数,从而使模型无法准确地捕捉数据的相关结构和特征;不适当的拟合指标可能导致拟合模型与真实数据之间存在较大的误差,使得模型的预测能力和解释能力下降,就本文案例表明在最小平方欧式值拟合指标下选择的Gaussian Copula函数拟合实测数据效果最好。此研究将有助在应用Coupla函数时选用恰当的拟合指标。
文摘在林业研究中,胸径-树高二元联合分布多由相同边缘分布构造,而林分的胸径与树高的实际分布状况可能有所差异。为降低这种差异带来的影响,依据佳木斯市孟家岗林场的115块长白落叶松人工林数据,选择适用条件低、适应范围广的Copula函数方法拟合落叶松胸径-树高二元联合分布模型。首先选择威布尔(Weibull)、广义威布尔(G-Weibull)、逻辑斯蒂(Logistic)、轻量逻辑斯蒂(Logit-Logistic)、伽马(Gamma)、对数正态(Log-Normal)6个分布函数作为备选基础模型,根据K-S(kolmogorov smirnov test)检验与半参数估计结果筛选并构建Copula胸径-树高二元联合分布模型,再通过负对数似然(negative log-likelihood,NLL)、Sn拟合优度统计量和似然比检验(likelihood ratio test,LRT)与二元对数logistic分布函数和二元Weibull分布函数进行比较,最后使用雷诺误差指数(error index of Reynolds,EI)对模型预测能力进行评估。结果表明,基于Copula函数的二元分拟合结果与模型(EI=0.3184)预估能力皆优于二元Weibull分布(EI=0.6381)和二元对数Logistic分布(EI=0.9490),说明此方法构建胸径-树高二元联合Copula分布模型能够很好地描述落叶松人工林胸径树高联合分布,以Copula方法构建树高-胸径联合分布是可行的。
