A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a n...A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments,which helps to evaluate whether or not the product variant can satisfy the customers' individual requirements.The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance.Then,these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data.The performance values of a newly configured product can be predicted by means of the trained SVM models.This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately,even under the small sample conditions.The applicability of the proposed method was verified on a family of plate electrostatic precipitators.展开更多
This paper proposes a single-ended fault detection scheme for long transmission lines using support vector machine(SVM)for multi-terminal direct current systems based on modular multilevel converter(MMC-MTDC).The sche...This paper proposes a single-ended fault detection scheme for long transmission lines using support vector machine(SVM)for multi-terminal direct current systems based on modular multilevel converter(MMC-MTDC).The scheme overcomes existing detection difficulties in the protection of long transmission lines resulting from high grounding resistance and attenuation,and also avoids the sophisticated process of threshold value selection.The high-frequency components in the measured voltage extracted by a wavelet transform and the amplitude of the zero-mode set of the positive-sequence voltage are the inputs to a trained SVM.The output of the SVM determines the fault type.A model of a four-terminal DC power grid with overhead transmission lines is built in PSCAD/EMTDC.Simulation results of EMTDC confirm that the proposed scheme achieves 100%accuracy in detecting short-circuit faults with high resistance on long transmission lines.The proposed scheme eliminates mal-operation of DC circuit breakers when faced with power order changes or AC-side faults.Its robustness and time delay are also assessed and shown to have no perceptible effect on the speed and accuracy of the detection scheme,thus ensuring its reliability and stability.展开更多
针对模块化多电平换流器(modular multi-level converter,MMC)子模块开路故障特点,提出一种基于无监督学习-最小二乘互信息谱聚类和整体最小二乘支持向量机(total least square support vector machines,TLS-SVM)的故障诊断方法,前者用...针对模块化多电平换流器(modular multi-level converter,MMC)子模块开路故障特点,提出一种基于无监督学习-最小二乘互信息谱聚类和整体最小二乘支持向量机(total least square support vector machines,TLS-SVM)的故障诊断方法,前者用于故障特征信息提取,后者用于故障分类识别。在MATLAB/Simulink环境下,搭建可进行故障设置的201电平MMC仿真系统。对采集到的换流器正常和故障运行时的三相电流信号通过滤波去噪处理后,进行Hilbert包络分解得到包络均值,使用最小二乘互信息谱聚类对包络均值进行二分类并获得标签集,然后将标签集和数据集作为基于整体最小二乘支持向量机的训练集并获得分类模型,最后对MMC故障进行分类和识别。仿真实验结果表明,该方法能有效识别高电平MMC的开路故障,并能实现智能决策。展开更多
基金Project(9140A18010210KG01) supported by the Departmental Pre-Research Fund of China
文摘A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments,which helps to evaluate whether or not the product variant can satisfy the customers' individual requirements.The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance.Then,these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data.The performance values of a newly configured product can be predicted by means of the trained SVM models.This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately,even under the small sample conditions.The applicability of the proposed method was verified on a family of plate electrostatic precipitators.
文摘This paper proposes a single-ended fault detection scheme for long transmission lines using support vector machine(SVM)for multi-terminal direct current systems based on modular multilevel converter(MMC-MTDC).The scheme overcomes existing detection difficulties in the protection of long transmission lines resulting from high grounding resistance and attenuation,and also avoids the sophisticated process of threshold value selection.The high-frequency components in the measured voltage extracted by a wavelet transform and the amplitude of the zero-mode set of the positive-sequence voltage are the inputs to a trained SVM.The output of the SVM determines the fault type.A model of a four-terminal DC power grid with overhead transmission lines is built in PSCAD/EMTDC.Simulation results of EMTDC confirm that the proposed scheme achieves 100%accuracy in detecting short-circuit faults with high resistance on long transmission lines.The proposed scheme eliminates mal-operation of DC circuit breakers when faced with power order changes or AC-side faults.Its robustness and time delay are also assessed and shown to have no perceptible effect on the speed and accuracy of the detection scheme,thus ensuring its reliability and stability.
文摘针对模块化多电平换流器(modular multi-level converter,MMC)子模块开路故障特点,提出一种基于无监督学习-最小二乘互信息谱聚类和整体最小二乘支持向量机(total least square support vector machines,TLS-SVM)的故障诊断方法,前者用于故障特征信息提取,后者用于故障分类识别。在MATLAB/Simulink环境下,搭建可进行故障设置的201电平MMC仿真系统。对采集到的换流器正常和故障运行时的三相电流信号通过滤波去噪处理后,进行Hilbert包络分解得到包络均值,使用最小二乘互信息谱聚类对包络均值进行二分类并获得标签集,然后将标签集和数据集作为基于整体最小二乘支持向量机的训练集并获得分类模型,最后对MMC故障进行分类和识别。仿真实验结果表明,该方法能有效识别高电平MMC的开路故障,并能实现智能决策。
