Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for TDGS.To overcome these,firstly,a si...Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for TDGS.To overcome these,firstly,a simulation-based TDGS model is established,and a surrogate-based model,grid search algorithm-particle swarm optimization-genetic algorithm-multi-output least squares support vector regression,is established.Among them,hyperparameter optimization algorithm’s effectiveness is confirmed through test functions.Subsequently,an adaptive surrogate-based probability density evolution method(PDEM)considering random track geometry irregularity(TGI)is developed.Finally,taking curved train-steel spring floating slab track-U beam as case study,the surrogate-based model trained on simulation datasets not only shows accuracy in both time and frequency domains,but also surpasses existing models.Additionally,the adaptive surrogate-based PDEM shows high accuracy and efficiency,outperforming Monte Carlo simulation and simulation-based PDEM.The reliability assessment shows that the TDGS part peak management indexes,left/right vertical dynamic irregularity,right alignment dynamic irregularity,and track twist,have reliability values of 0.9648,0.9918,0.9978,and 0.9901,respectively.The TDGS mean management index,i.e.,track quality index,has reliability value of 0.9950.These findings show that the proposed framework can accurately and efficiently assess the reliability of curved low-stiffness track-viaducts,providing a theoretical basis for the TGI maintenance.展开更多
保护测量回路是电力系统继电保护的基石,其误差评估对电网安稳运维举足轻重。针对保护测量回路静态隐藏误差可能诱发保护误动/拒动的风险且难以在线监测问题,提出了一种基于递推主元分析和改进灰狼算法优化极限学习机(recursive princip...保护测量回路是电力系统继电保护的基石,其误差评估对电网安稳运维举足轻重。针对保护测量回路静态隐藏误差可能诱发保护误动/拒动的风险且难以在线监测问题,提出了一种基于递推主元分析和改进灰狼算法优化极限学习机(recursive principal component analysis and extreme learning machine optimized by grey wolf optimization,RPCA-GELM)数据驱动的保护测量回路误差评估方法。首先基于电力系统正常运行下历史数据与实时数据,应用RPCA技术在线更新主元特征模型以缩短评估时间,进一步引入4种统计算法生成4类误差监测特征量,构建误差综合评判方法进行特征优选,提升误差评估准确率。然后针对模型评估精度取决于关键参数C、σ,引入国际无限折叠混沌映射策略对灰狼算法进行优化,以提升参数寻优精度和收敛速度,在此基础上结合ELM算法提出了基于GELM的保护测量回路误差评估方法。最后通过多组对比实验验证了所提方法能实现模型性能优化,且相对其他方法有效提升了保护测量回路误差评估准确率与精度。展开更多
基金Project(52072412)supported by the National Natural Science Foundation of China。
文摘Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for TDGS.To overcome these,firstly,a simulation-based TDGS model is established,and a surrogate-based model,grid search algorithm-particle swarm optimization-genetic algorithm-multi-output least squares support vector regression,is established.Among them,hyperparameter optimization algorithm’s effectiveness is confirmed through test functions.Subsequently,an adaptive surrogate-based probability density evolution method(PDEM)considering random track geometry irregularity(TGI)is developed.Finally,taking curved train-steel spring floating slab track-U beam as case study,the surrogate-based model trained on simulation datasets not only shows accuracy in both time and frequency domains,but also surpasses existing models.Additionally,the adaptive surrogate-based PDEM shows high accuracy and efficiency,outperforming Monte Carlo simulation and simulation-based PDEM.The reliability assessment shows that the TDGS part peak management indexes,left/right vertical dynamic irregularity,right alignment dynamic irregularity,and track twist,have reliability values of 0.9648,0.9918,0.9978,and 0.9901,respectively.The TDGS mean management index,i.e.,track quality index,has reliability value of 0.9950.These findings show that the proposed framework can accurately and efficiently assess the reliability of curved low-stiffness track-viaducts,providing a theoretical basis for the TGI maintenance.
文摘保护测量回路是电力系统继电保护的基石,其误差评估对电网安稳运维举足轻重。针对保护测量回路静态隐藏误差可能诱发保护误动/拒动的风险且难以在线监测问题,提出了一种基于递推主元分析和改进灰狼算法优化极限学习机(recursive principal component analysis and extreme learning machine optimized by grey wolf optimization,RPCA-GELM)数据驱动的保护测量回路误差评估方法。首先基于电力系统正常运行下历史数据与实时数据,应用RPCA技术在线更新主元特征模型以缩短评估时间,进一步引入4种统计算法生成4类误差监测特征量,构建误差综合评判方法进行特征优选,提升误差评估准确率。然后针对模型评估精度取决于关键参数C、σ,引入国际无限折叠混沌映射策略对灰狼算法进行优化,以提升参数寻优精度和收敛速度,在此基础上结合ELM算法提出了基于GELM的保护测量回路误差评估方法。最后通过多组对比实验验证了所提方法能实现模型性能优化,且相对其他方法有效提升了保护测量回路误差评估准确率与精度。
文摘滑坡地质灾害易发性评价是防灾减灾的一种重要手段,易发性评价模型的选取和优化至关重要。以思南县为研究区,选取高程、坡度、曲率、地层、土地利用、年平均降雨量等16个评价因子,采用频率比(frequency ratio,FR)模型与支持向量机(support vector machine,SVM)模型和随机森林(random forest,RF)模型相耦合,引入网格搜索方法来获取SVM模型、RF模型及其耦合模型最优参数组合并用于模型训练,最终构建SVM、RF、FR-SVM及FR-RF模型对整个研究区进行滑坡易发性预测,并进行了受试者操作特征(receiver operating characteristics,ROC)曲线验证。结果表明:与单一机器学习模型相比,耦合机器学习有更多的滑坡灾害样本落于高易发区和极高易发区,有更高的准确率。单一模型中,RF模型有较多的滑坡灾害样本落于高易发区和极高易发区,耦合模型中,FR-RF模型有较多的滑坡灾害样本落于高易发区和极高易发区,且FR模型和FR-RF模型中没有滑坡灾害样本落在极低易发区,表明无论是单一模型还是耦合模型,RF模型的性能优于SVM模型。4种模型的ROC预测曲线的曲线下面积(area under the curve,AUC)分别为0.8316、0.8439、0.8644、0.9104,说明FR模型与RF模型结合的耦合模型有更高的准确率,该模型更适用于思南县的滑坡易发性评价研究,评价结果可为当地滑坡地质灾害的防灾减灾提供一定的参考。
