Background and Objective It has been proven that copy number gain/or loss (copy number variation CNV) in uences gene expression and result in phenotypic variation by
An improved method for calculating the corona power loss and the ground-level electric field on HVAC transmission lines induced by corona is proposed.Based on a charge simulation method combined with a method of succe...An improved method for calculating the corona power loss and the ground-level electric field on HVAC transmission lines induced by corona is proposed.Based on a charge simulation method combined with a method of successive images,the proposed method has the number and location of the simulated charges not arbitrary.When the surface electric field of a conductor exceeds the onset value,charges are emitted from corona into the space around,and the space ions and the surface charges on each sub-conductor are simulated by using the images of the other sub-conductors.The displacements of the space ions are calculated at every time step during corona periods in both the positive and the negative half cycles.Several examples are calculated by using the proposed method,and the calculated electric field at the ground level and the corona power loss agree well with previous measurements.The results show that simulating 12 charges in each conductor during 600 time steps in one cycle takes less time while guarantees the accuracy.The corona discharge from a 220 kV transmission line enhances slightly(less than 2%) the electric field at the ground level,but this effect is little from a 500 kV line.The improved method is a good compromise between the time cost and the accuracy of calculation.展开更多
配电网线损时间序列受高比例新能源接入的影响,呈现高度的非线性和波动性,面对此种类型的数据,使得常规的预测模型难以捕捉其变化趋势,预测值往往滞后于真实值变化,而模态分解再预测的处理方法能够较好地应对此问题。因此,该文提出了一...配电网线损时间序列受高比例新能源接入的影响,呈现高度的非线性和波动性,面对此种类型的数据,使得常规的预测模型难以捕捉其变化趋势,预测值往往滞后于真实值变化,而模态分解再预测的处理方法能够较好地应对此问题。因此,该文提出了一种基于缎蓝园丁鸟(satin bower birdoptimization algorithm,SBO)算法优化的二次模态分解和卷积双向长短期记忆神经网络的线损预测框架,以合理划分线损分量,并针对各分量设计预测模型开展预测。首先采用改进完全集合经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise,ICEEMDAN)对历史线损数据进行初次分解,得到各ICIMFn分量并计算其样本熵;对样本熵最高的ICIMF1利用经SBO优化的变分模态分解(variational mode decomposition,VMD)对其进一步分解,得到各VIMFn分量。其次,考虑分解后线损各分量受天气负荷等不同因素影响,依据最大互信息系数(maximal information coefficien,MIC),提取对各线损分量产生影响的主要因素,实现特征降维。最后,结合组合模型的各自特点,建立基于卷积双向长短期记忆神经网络(convolutional neural networks-Bidirectional long short term memory,CNN-BiLSTM)的预测模型,使用CNN对分解后的各分量进行特征提取,输入到BiLSTM中,建立时间特征关系,学习历史数据间的正、反向规律,最终输出线损预测结果。与现有方法相比较,所提方法在应对滞后效应的同时,提升了预测效率及精度,为精细化线损管理提供了数据支持。展开更多
基金supported by a grant from the key project of the National Natural Science Foundation of China (to Qinghua ZHOU)(No. 30430300)National Natural Science Foundation of China (to Qinghua ZHOU)(No. 30670922)INTERNATION Scienc and Techniquie COOPRATION PROGRAM OF CHINA (ISCP) (to Qinghua ZHOU)(No.2006DFB32330)
文摘Background and Objective It has been proven that copy number gain/or loss (copy number variation CNV) in uences gene expression and result in phenotypic variation by
基金supported by National Basic Research Program of China(973 Program)(2011CB209404)
文摘An improved method for calculating the corona power loss and the ground-level electric field on HVAC transmission lines induced by corona is proposed.Based on a charge simulation method combined with a method of successive images,the proposed method has the number and location of the simulated charges not arbitrary.When the surface electric field of a conductor exceeds the onset value,charges are emitted from corona into the space around,and the space ions and the surface charges on each sub-conductor are simulated by using the images of the other sub-conductors.The displacements of the space ions are calculated at every time step during corona periods in both the positive and the negative half cycles.Several examples are calculated by using the proposed method,and the calculated electric field at the ground level and the corona power loss agree well with previous measurements.The results show that simulating 12 charges in each conductor during 600 time steps in one cycle takes less time while guarantees the accuracy.The corona discharge from a 220 kV transmission line enhances slightly(less than 2%) the electric field at the ground level,but this effect is little from a 500 kV line.The improved method is a good compromise between the time cost and the accuracy of calculation.
文摘针对传统方法在计算低压台区理论线损率时出现的数据质量要求高、物理结构依赖度高等问题,文章提出一种基于自适应增强(adaptive boosting,AdaBoost)集成学习的低压台区理论线损计算方法。综合考虑低压台区网架结构参数、台区运行方式、用电负荷水平等各类因素,形成影响因素特征指标体系,进一步通过AdaBoost算法训练各分类与回归树(classification and regression tree,CART)决策树个体学习器并计算各个体学习器的权重系数,利用个体学习器的线性组合集成得到最终的理论线损计算法模型,同时在此基础上分析研究了典型重要因素对理论线损的影响规律。结果表明:所提算法具有良好的可靠性与稳定性,可以实现不同因素对台区理论线损影响规律的分析,进一步可以支撑不同因素影响程度的横向比较,可为台区的线损异常治理提供方向性指导。
文摘配电网线损时间序列受高比例新能源接入的影响,呈现高度的非线性和波动性,面对此种类型的数据,使得常规的预测模型难以捕捉其变化趋势,预测值往往滞后于真实值变化,而模态分解再预测的处理方法能够较好地应对此问题。因此,该文提出了一种基于缎蓝园丁鸟(satin bower birdoptimization algorithm,SBO)算法优化的二次模态分解和卷积双向长短期记忆神经网络的线损预测框架,以合理划分线损分量,并针对各分量设计预测模型开展预测。首先采用改进完全集合经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise,ICEEMDAN)对历史线损数据进行初次分解,得到各ICIMFn分量并计算其样本熵;对样本熵最高的ICIMF1利用经SBO优化的变分模态分解(variational mode decomposition,VMD)对其进一步分解,得到各VIMFn分量。其次,考虑分解后线损各分量受天气负荷等不同因素影响,依据最大互信息系数(maximal information coefficien,MIC),提取对各线损分量产生影响的主要因素,实现特征降维。最后,结合组合模型的各自特点,建立基于卷积双向长短期记忆神经网络(convolutional neural networks-Bidirectional long short term memory,CNN-BiLSTM)的预测模型,使用CNN对分解后的各分量进行特征提取,输入到BiLSTM中,建立时间特征关系,学习历史数据间的正、反向规律,最终输出线损预测结果。与现有方法相比较,所提方法在应对滞后效应的同时,提升了预测效率及精度,为精细化线损管理提供了数据支持。
