Classification of multi-dimension time series(MTS) plays an important role in knowledge discovery of time series. Many methods for MTS classification have been presented. However, most of these methods did not conside...Classification of multi-dimension time series(MTS) plays an important role in knowledge discovery of time series. Many methods for MTS classification have been presented. However, most of these methods did not consider the kind of MTS whose discriminative subsequence was not restricted to one dimension and dynamic. In order to solve the above problem, a method to extract new features with extended shapelet transformation is proposed in this study. First, key features is extracted to replace k shapelets to calculate distance, which are extracted from candidate shapelets with one class for all dimensions. Second, feature of similarity numbers as a new feature is proposed to enhance the reliability of classification. Third, because of the time-consuming searching and clustering of shapelets, distance matrix is used to reduce the computing complexity. Experiments are carried out on public dataset and the results illustrate the effectiveness of the proposed method. Moreover, anode current signals(ACS) in the aluminum reduction cell are the aforementioned MTS, and the proposed method is successfully applied to the classification of ACS.展开更多
建设智能教育平台是推动教育智能化的一个重要过程,但智能教育平台依赖的人工智能模型在训练过程中会消耗大量电力,因此,开展短期电力负荷预测对建设智能教育平台具有重要意义.针对在考虑多个属性开展短期电力负荷预测时,由于部分属性...建设智能教育平台是推动教育智能化的一个重要过程,但智能教育平台依赖的人工智能模型在训练过程中会消耗大量电力,因此,开展短期电力负荷预测对建设智能教育平台具有重要意义.针对在考虑多个属性开展短期电力负荷预测时,由于部分属性与电力负荷数据的相关性不强并且Transformer无法捕捉电力负荷数据的时间相关性,而导致电力负荷预测不够准确的问题,基于SR(Székely and Rizzo)距离相关系数、融合时间定位编码和Transformer,提出了一种短期电力负荷预测模型SF-Transformer.SF-Transformer通过SR距离相关系数对影响电力负荷数据的属性进行筛选,选择与电力负荷数据之间SR距离相关系数较大的属性.SF-Transformer采用一种全局时间编码与局部位置编码相结合的融合时间定位编码,有助于模型全面获取电力负荷数据的时间定位信息.在数据集上开展了实验,实验结果表明SF-Transformer与其他模型相比,在两种时长上进行电力负荷预测具有更低的均方根误差和平均绝对误差.展开更多
海上风电交流送出场景下,长距离交流海缆并网使得背景谐波放大问题突出,为此研究了基于静止无功发生器(static var generator,SVG)附加控制的长距离交流海缆送出海上风电场群(offshore wind farms with long-distance ac submarine cabl...海上风电交流送出场景下,长距离交流海缆并网使得背景谐波放大问题突出,为此研究了基于静止无功发生器(static var generator,SVG)附加控制的长距离交流海缆送出海上风电场群(offshore wind farms with long-distance ac submarine cable,OWFs-LACSC)背景谐波抑制策略。首先,分析了OWFs-LACSC背景谐波放大的原理,论述了背景谐波放大与谐振的区别;然后,建立了含SVG和直驱风机的OWFs-LACSC阻抗模型,提出利用Park变换的基频偏移特性可实现单通道抑制两种背景谐波,进而结合这一特性和阻性有源滤波原理,在SVG控制环路中附加控制器,并分析了附加控制的参数可行域;最后,利用国内某OWFs-LACSC模型开展仿真,验证了所提策略的可行性与鲁棒性。展开更多
基金Projects(61773405,61725306,61533020)supported by the National Natural Science Foundation of ChinaProject(2018zzts583)supported by the Fundamental Research Funds for the Central Universities,China
文摘Classification of multi-dimension time series(MTS) plays an important role in knowledge discovery of time series. Many methods for MTS classification have been presented. However, most of these methods did not consider the kind of MTS whose discriminative subsequence was not restricted to one dimension and dynamic. In order to solve the above problem, a method to extract new features with extended shapelet transformation is proposed in this study. First, key features is extracted to replace k shapelets to calculate distance, which are extracted from candidate shapelets with one class for all dimensions. Second, feature of similarity numbers as a new feature is proposed to enhance the reliability of classification. Third, because of the time-consuming searching and clustering of shapelets, distance matrix is used to reduce the computing complexity. Experiments are carried out on public dataset and the results illustrate the effectiveness of the proposed method. Moreover, anode current signals(ACS) in the aluminum reduction cell are the aforementioned MTS, and the proposed method is successfully applied to the classification of ACS.
文摘建设智能教育平台是推动教育智能化的一个重要过程,但智能教育平台依赖的人工智能模型在训练过程中会消耗大量电力,因此,开展短期电力负荷预测对建设智能教育平台具有重要意义.针对在考虑多个属性开展短期电力负荷预测时,由于部分属性与电力负荷数据的相关性不强并且Transformer无法捕捉电力负荷数据的时间相关性,而导致电力负荷预测不够准确的问题,基于SR(Székely and Rizzo)距离相关系数、融合时间定位编码和Transformer,提出了一种短期电力负荷预测模型SF-Transformer.SF-Transformer通过SR距离相关系数对影响电力负荷数据的属性进行筛选,选择与电力负荷数据之间SR距离相关系数较大的属性.SF-Transformer采用一种全局时间编码与局部位置编码相结合的融合时间定位编码,有助于模型全面获取电力负荷数据的时间定位信息.在数据集上开展了实验,实验结果表明SF-Transformer与其他模型相比,在两种时长上进行电力负荷预测具有更低的均方根误差和平均绝对误差.
文摘海上风电交流送出场景下,长距离交流海缆并网使得背景谐波放大问题突出,为此研究了基于静止无功发生器(static var generator,SVG)附加控制的长距离交流海缆送出海上风电场群(offshore wind farms with long-distance ac submarine cable,OWFs-LACSC)背景谐波抑制策略。首先,分析了OWFs-LACSC背景谐波放大的原理,论述了背景谐波放大与谐振的区别;然后,建立了含SVG和直驱风机的OWFs-LACSC阻抗模型,提出利用Park变换的基频偏移特性可实现单通道抑制两种背景谐波,进而结合这一特性和阻性有源滤波原理,在SVG控制环路中附加控制器,并分析了附加控制的参数可行域;最后,利用国内某OWFs-LACSC模型开展仿真,验证了所提策略的可行性与鲁棒性。
