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首页> 外文期刊>Electric power systems research >Power flow state estimator using two-layer neural network structure
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Power flow state estimator using two-layer neural network structure

机译：基于两层神经网络结构的潮流状态估计器

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This paper is devoted to the development of a neural network structure which implements the line power flow state estimator algorithm for solving the set of nonlinear equations of power system automatic generation control analysis. The principal context is that of on-line network analysis in energy management systems with particular reference to the automatic generation control function. The author shows that the complete line power flow state estimator formulation maps into an array of two-layer neural networks. The development starts from a formulation for solving as a minimization the equation system to which the state estimator sequence leads at the each iteration. A neural network structure is given which implements the steepest descent method for minimizing the objective function. It is shown that some of the input values of neural networks are formed from power flow measurements as on-line. A principal feature of the extensive parallel processing capability of the architecture developed is that the computing time of state estimator analysis is independent of the number of nodes in a power network for which analysis is carried out.
机译：本文致力于神经网络结构的开发，该结构实现了线潮流状态估计算法，用于求解电力系统自动发电控制分析的非线性方程组。主要内容是能源管理系统中的在线网络分析，尤其要参考自动发电控制功能。作者表明，完整的线潮流状态估计器公式映射到两层神经网络的阵列中。开发从一个公式开始，该公式用于求解方程式系统，该方程式系统在每次迭代时都将状态估计器引向该方程式。给出了一种神经网络结构，该结构实现了用于使目标函数最小化的最速下降方法。结果表明，神经网络的某些输入值是通过在线的潮流测量形成的。所开发的体系结构广泛的并行处理能力的主要特征是，状态估计器分析的计算时间与电网中进行分析的节点数无关。

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