摘要：【中文】一种考虑新一代调相机影响的直流静态功率极限值计算方法，包括(1)建立交、直流系统间的关联模型；(2)计算调相机进相运行和滞相运行发出极限值；(3)计算额定条件下直流的换相角和与逆变侧关联参数；(4)求解等值交流系统的电势和直流母线电压功角；(5)以直流电流为变量，求解各相关电气量；(6)判断计算所得调相机所提供的无功是否在其允许发出的无功功率范围内；若在范围内，则以直流电流为为独立变量，求解直流功率；否则进入步骤(7)；(7)将计算所得的调相机发出无功功率值用调相机能发出的无功功率极限值代替，返回步骤(5)。本发明使得直流传输功率极限值与实际更加吻合，为实际运行中提升直流传输能力提供重要参考。 【EN】A direct current static power limit value calculation method considering the influence of a new generation phase modulator comprises the steps of (1) establishing a correlation model between overpasses and direct current systems; (2) calculating the phase-in operation and phase-lag operation sending limit values of the phase modifier; (3) calculating a direct current commutation angle and parameters related to an inversion side under a rated condition; (4) solving the potential of the equivalent alternating current system and the direct current bus voltage power angle; (5) solving each related electrical quantity by taking the direct current as a variable; (6) judging whether the reactive power provided by the phase modulator is in the range of the reactive power allowed to be sent out by the phase modulator; if the direct current is within the range, the direct current is taken as an independent variable, and the direct current power is solved; otherwise, entering the step (7); (7) and (5) replacing the reactive power value sent by the phase modulator by the reactive power limit value which can be sent by the phase modulator, and returning to the step. The invention makes the limit value of the direct current transmission power more consistent with the actual value, and provides an important reference for improving the direct current transmission capability in the actual operation.

摘要：【中文】本发明提供一种基于卷积神经网络的中文签名笔迹鉴定方法，首先采集本人重复签名和他人代签手写图片，得到签名图像，对签名图像两两组合形成数据集D和以及与数据集D对应的标签集T，其次对签名图像进行处理，包括改变签名图像的大小、灰度化处理、二值化处理和去噪处理后形成数据集D′，将数据集D′划分为训练集和测试集，然后构造多层卷积神经网络，通过构造适合识别签名笔迹差异的损失函数，利用数据集D′、标签集T、损失函数对多层卷积神经网络进行训练得到多层卷积神经网络模型，最终利用所述多层卷积神经网络模型实现对中文签名笔迹的鉴定。本发明可用来鉴定签名是否为本人笔迹，方法简单实用，可为电网行业各种工作票签名鉴定提供依据。 【EN】The invention provides a Chinese signature handwriting identification method based on a convolutional neural network, which comprises the steps of firstly collecting repeated signature of a person and handwritten pictures signed by others instead to obtain a signature image, the signature images are combined pairwise to form a data set D and a tag set T corresponding to the data set D, secondly, processing the signature image, including changing the size of the signature image, carrying out graying processing, binarization processing and denoising processing to form a data set D ', dividing the data set D' into a training set and a test set, and then constructing a multilayer convolutional neural network, training the multilayer convolutional neural network by utilizing the data set D', the tag set T and the loss function to obtain a multilayer convolutional neural network model by constructing a loss function suitable for identifying the difference of the signature handwriting, and finally identifying the Chinese signature handwriting by utilizing the multilayer convolutional neural network model. The method can be used for identifying whether the signature is the handwriting of the person, is simple and practical, and can provide a basis for identifying various working ticket signatures in the power grid industry.