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State estimation for power systems with multilayer perceptron neural networks

机译:具有多层感知器神经网络的电力系统状态估计

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The Static state estimation is widely used in power systems for real time monitoring and analysis. Standard methods, such as the weighted least squares (WLS) algorithm, require the computation of bus admittance and Jacobian matrices and the solution is found in an iterative process. This paper presents an alternative for the classic state estimation (SE) algorithms, which uses a multilayer perceptron for the state estimator. Results are presented for the IEEE 14 bus system.
机译:静态估计广泛用于电力系统中的实时监视和分析。标准方法,例如加权最小二乘(WLS)算法,需要计算总线导纳和Jacobian矩阵,并且需要在迭代过程中找到解决方案。本文提出了经典状态估计(SE)算法的替代方法,该算法使用多层感知器作为状态估计器。给出了IEEE 14总线系统的结果。

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