为了获得更准确的静止无功发生器(static var generator,SVG)模型参数以满足风电并网系统安全稳定运行的要求,提出一种计及风电场随机特性的SVG模型参数智能辨识方法。首先,通过分析SVG的动作特性建立其数学模型。然后,研究了风电场随...为了获得更准确的静止无功发生器(static var generator,SVG)模型参数以满足风电并网系统安全稳定运行的要求,提出一种计及风电场随机特性的SVG模型参数智能辨识方法。首先,通过分析SVG的动作特性建立其数学模型。然后,研究了风电场随机特性对辨识结果的影响途径和机理。最后,针对风电场随机特性引起的辨识结果不准确问题,提出一种考虑风电场随机特性的SVG模型参数多方式混合辨识方法,为准确辨识风电场SVG模型参数提供了新的方法。参数辨识仿真实验结果验证了所提方法的可行性。展开更多
In order to effectively solve the problems of low accuracy and large amount of calculation of current human behavior recognition,a behavior recognition algorithm based on squeeze-and-excitation network(SENet) combined...In order to effectively solve the problems of low accuracy and large amount of calculation of current human behavior recognition,a behavior recognition algorithm based on squeeze-and-excitation network(SENet) combined with 3 D Inception network(I3 D) and gated recurrent unit(GRU) network is proposed.The algorithm first expands the Inception module to three-dimensional,and builds a network based on the three-dimensional module,and expands SENet to three-dimensional,making it an attention mechanism that can pay attention to the three-dimensional channel.Then SENet is introduced into the 13 D network,named SE-I3 D,and SENet is introduced into the CRU network,named SE-GRU.And,SE-13 D and SE-GRU are merged,named SE-13 D-GRU.Finally,the network uses Softmax to classify the results in the UCF-101 dataset.The experimental results show that the SE-I3 D-GRU network achieves a recognition rate of 93.2% on the UCF-101 dataset.展开更多
文摘为了获得更准确的静止无功发生器(static var generator,SVG)模型参数以满足风电并网系统安全稳定运行的要求,提出一种计及风电场随机特性的SVG模型参数智能辨识方法。首先,通过分析SVG的动作特性建立其数学模型。然后,研究了风电场随机特性对辨识结果的影响途径和机理。最后,针对风电场随机特性引起的辨识结果不准确问题,提出一种考虑风电场随机特性的SVG模型参数多方式混合辨识方法,为准确辨识风电场SVG模型参数提供了新的方法。参数辨识仿真实验结果验证了所提方法的可行性。
基金Supported by the Shaanxi Province Key Research and Development Project(No.2021 GY-280)the Natural Science Foundation of Shaanxi Province(No.2021JM-459)the National Natural Science Foundation of China(No.61772417,61634004,61602377).
文摘In order to effectively solve the problems of low accuracy and large amount of calculation of current human behavior recognition,a behavior recognition algorithm based on squeeze-and-excitation network(SENet) combined with 3 D Inception network(I3 D) and gated recurrent unit(GRU) network is proposed.The algorithm first expands the Inception module to three-dimensional,and builds a network based on the three-dimensional module,and expands SENet to three-dimensional,making it an attention mechanism that can pay attention to the three-dimensional channel.Then SENet is introduced into the 13 D network,named SE-I3 D,and SENet is introduced into the CRU network,named SE-GRU.And,SE-13 D and SE-GRU are merged,named SE-13 D-GRU.Finally,the network uses Softmax to classify the results in the UCF-101 dataset.The experimental results show that the SE-I3 D-GRU network achieves a recognition rate of 93.2% on the UCF-101 dataset.
