General neural network inverse adaptive controller has two flaws: the first is the slow convergence speed; the second is the invalidation to the non-minimum phase system. These defects limit the scope in which the neu...General neural network inverse adaptive controller has two flaws: the first is the slow convergence speed; the second is the invalidation to the non-minimum phase system. These defects limit the scope in which the neural network inverse adaptive controller is used. We employ Davidon least squares in training the multi-layer feedforward neural network used in approximating the inverse model of plant to expedite the convergence, and then through constructing the pseudo-plant, a neural network inverse adaptive controller is put forward which is still effective to the nonlinear non-minimum phase system. The simulation results show the validity of this scheme.展开更多
经典低阶频率响应模型可快速计算各项频率指标,但由于高比例新能源系统扰动类型多样,运行方式复杂多变,难以准确获取系统参数和扰动功率大小,同时模型本身线性化会引起较大误差,导致频率预测值和实际值存在较大差异。为使频率响应模型...经典低阶频率响应模型可快速计算各项频率指标,但由于高比例新能源系统扰动类型多样,运行方式复杂多变,难以准确获取系统参数和扰动功率大小,同时模型本身线性化会引起较大误差,导致频率预测值和实际值存在较大差异。为使频率响应模型适应实际应用场景中高精度的要求,该文提出了模型-数据融合驱动的频率稳定智能增强判别方法(model-data driven intelligent enhanced method for frequency stability discrimination,MD-IEFSD),利用扰动初期频率响应数据对模型关键参数进行辨识,建立结合卷积神经网络和注意力机制的CNN-Attention频率参数预测模型,构建了融合参数预测误差和频率响应曲线预测误差的损失函数,引入了参数的敏感性和学习速率的分析,实现了频率稳定性的准确判别。最后以中国电科院万节点测试系统为算例,验证所提方法的可行性和有效性。展开更多
基金Tianjin Natural Science Foundation !983602011National 863/CIMS Research Foundation !863-511-945-010
文摘General neural network inverse adaptive controller has two flaws: the first is the slow convergence speed; the second is the invalidation to the non-minimum phase system. These defects limit the scope in which the neural network inverse adaptive controller is used. We employ Davidon least squares in training the multi-layer feedforward neural network used in approximating the inverse model of plant to expedite the convergence, and then through constructing the pseudo-plant, a neural network inverse adaptive controller is put forward which is still effective to the nonlinear non-minimum phase system. The simulation results show the validity of this scheme.
文摘经典低阶频率响应模型可快速计算各项频率指标,但由于高比例新能源系统扰动类型多样,运行方式复杂多变,难以准确获取系统参数和扰动功率大小,同时模型本身线性化会引起较大误差,导致频率预测值和实际值存在较大差异。为使频率响应模型适应实际应用场景中高精度的要求,该文提出了模型-数据融合驱动的频率稳定智能增强判别方法(model-data driven intelligent enhanced method for frequency stability discrimination,MD-IEFSD),利用扰动初期频率响应数据对模型关键参数进行辨识,建立结合卷积神经网络和注意力机制的CNN-Attention频率参数预测模型,构建了融合参数预测误差和频率响应曲线预测误差的损失函数,引入了参数的敏感性和学习速率的分析,实现了频率稳定性的准确判别。最后以中国电科院万节点测试系统为算例,验证所提方法的可行性和有效性。
