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Short-term distribution system state forecast based on optimal synchrophasor sensor placement and extreme learning machine

机译：基于最优同步相量传感器布置和极限学习机的短期配电系统状态预测

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This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vector regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.
机译：本文提出了一种配电系统状态预测的方法，旨在通过基于最优同步相量传感器位置（OSSP）的状态估计器和基于极限学习机（ELM）的预测器来提供准确，高速的状态预测。具体来说，考虑到传感器的安装成本和测量误差，提出了一种OSSP算法，以减少同步相量传感器的数量，并保持整个配电系统的数值和拓扑结构可观察。然后，使用基于加权最小二乘（WLS）的系统状态估计器为建议的预测器生成训练数据。传统上，由于其非线性建模功能，人工神经网络（ANN）和支持向量回归（SVR）被广泛用于预测。但是，人工神经网络的计算量很大，并且很难获得用于SVR的最佳参数。在本文中，克服了这些缺点的ELM用于以历史系统状态来预测将来的系统状态。所提出的方法基于测试结果是有效且准确的。

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