In the present work, we are interested in studying the joint distributions of pairs of the monthly maxima of the pollutants used by the environmental authorities in Mexico City to classify the air quality in the metro...In the present work, we are interested in studying the joint distributions of pairs of the monthly maxima of the pollutants used by the environmental authorities in Mexico City to classify the air quality in the metropolitan area. In order to obtain the joint distributions a copula will be considered. Since we are analyzing the monthly maxima, the extreme value distributions of Weibull and Fréchet are taken into account. Using these two distributions as marginal distributions in the copula a Bayesian inference was made in order to estimate the parameters of both distributions and also the association parameters appearing in the copula model. The pollutants taken into account are ozone, nitrogen dioxide, sulphur dioxide, carbon monoxide, and particulate matter with diameters smaller than 10 and 2.5 microns obtained from the Mexico City monitoring network. The estimation was performed by taking samples of the parameters generated through a Markov chain Monte Carlo algorithm implemented using the software OpenBugs. Once the algorithm is implemented it is applied to the pairs of pollutants where one of the coordinates of the pair is ozone and the other varies on the set of the remaining pollutants. Depending on the pollutant and the region where they were collected, different results were obtained. Hence, in some cases we have that the best model is that where we have a Fréchet distribution as the marginal distribution for the measurements of both pollutants and in others the most suitable model is the one assuming a Fréchet for ozone and a Weibull for the other pollutant. Results show that, in the present case, the estimated association parameter is a good representation to the correlation parameters between the pair of pollutants analyzed. Additionally, it is a straightforward task to obtain these correlation parameters from the corresponding association parameters.展开更多
风电机组在齿轮箱油温过高时会导致机组限功率运行,影响机组发电效率。传统应对风机高温降容状态多采用阈值判断,反应迟缓,加剧风机齿轮箱劣化趋势。利用贝叶斯网络对风机高温降容状态进行评估,为提取并准确合理地利用机组数据采集与监...风电机组在齿轮箱油温过高时会导致机组限功率运行,影响机组发电效率。传统应对风机高温降容状态多采用阈值判断,反应迟缓,加剧风机齿轮箱劣化趋势。利用贝叶斯网络对风机高温降容状态进行评估,为提取并准确合理地利用机组数据采集与监视控制系统(supervisory control and data acquisition system,SCADA)各个相关状态参数之间的耦合特性,通过vine-Copula模型对机组各个状态参数进行相关性分析,建立更符合机组实际运行状态的贝叶斯概率图形网络,实现对机组高温降容状态的评估。通过交叉熵算法对模型输出结果进行评价,发现与朴素贝叶斯模型相比,vine-Copula贝叶斯网络评估结果更为精确可靠,所建模型更符合机组实际运行工况,能够为现场的运维人员制定准确合理的运行和维护方案提供参考。展开更多
文摘In the present work, we are interested in studying the joint distributions of pairs of the monthly maxima of the pollutants used by the environmental authorities in Mexico City to classify the air quality in the metropolitan area. In order to obtain the joint distributions a copula will be considered. Since we are analyzing the monthly maxima, the extreme value distributions of Weibull and Fréchet are taken into account. Using these two distributions as marginal distributions in the copula a Bayesian inference was made in order to estimate the parameters of both distributions and also the association parameters appearing in the copula model. The pollutants taken into account are ozone, nitrogen dioxide, sulphur dioxide, carbon monoxide, and particulate matter with diameters smaller than 10 and 2.5 microns obtained from the Mexico City monitoring network. The estimation was performed by taking samples of the parameters generated through a Markov chain Monte Carlo algorithm implemented using the software OpenBugs. Once the algorithm is implemented it is applied to the pairs of pollutants where one of the coordinates of the pair is ozone and the other varies on the set of the remaining pollutants. Depending on the pollutant and the region where they were collected, different results were obtained. Hence, in some cases we have that the best model is that where we have a Fréchet distribution as the marginal distribution for the measurements of both pollutants and in others the most suitable model is the one assuming a Fréchet for ozone and a Weibull for the other pollutant. Results show that, in the present case, the estimated association parameter is a good representation to the correlation parameters between the pair of pollutants analyzed. Additionally, it is a straightforward task to obtain these correlation parameters from the corresponding association parameters.
文摘风电机组在齿轮箱油温过高时会导致机组限功率运行,影响机组发电效率。传统应对风机高温降容状态多采用阈值判断,反应迟缓,加剧风机齿轮箱劣化趋势。利用贝叶斯网络对风机高温降容状态进行评估,为提取并准确合理地利用机组数据采集与监视控制系统(supervisory control and data acquisition system,SCADA)各个相关状态参数之间的耦合特性,通过vine-Copula模型对机组各个状态参数进行相关性分析,建立更符合机组实际运行状态的贝叶斯概率图形网络,实现对机组高温降容状态的评估。通过交叉熵算法对模型输出结果进行评价,发现与朴素贝叶斯模型相比,vine-Copula贝叶斯网络评估结果更为精确可靠,所建模型更符合机组实际运行工况,能够为现场的运维人员制定准确合理的运行和维护方案提供参考。
