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Adaptive scheme for local prediction of post-contingency power system frequency

机译:应急后电力系统频率局部预测的自适应方案

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摘要

The power system frequency always should be kept upper than a minimum threshold determined by the limitations of system equipments such as synchronous generators. In this paper a new method is proposed for local prediction of maximum post-contingency deviation of power system frequency using Artificial Neural Network (ANN) and Support Vector Regression (SVR) learning machines. Due to change of network oscillation modes under different contingencies, the proposed predictors adjust the data sampling time for improving the performance. For ANN and SVR training, a comprehensive list of scenarios is created considering all credible disturbances. The performance of the proposed algorithm is simulated and verified over a dynamic test system.
机译:电力系统频率应始终保持高于由系统设备(如同步发电机)的限制所确定的最小阈值。本文提出了一种新的方法,可以使用人工神经网络(ANN)和支持向量回归(SVR)学习机对电力系统频率的最大事后应变偏差进行局部预测。由于网络振荡模式在不同情况下的变化,所提出的预测器调整数据采样时间以提高性能。对于ANN和SVR培训,将考虑所有可信干扰,创建一个完整的方案清单。通过动态测试系统对所提出算法的性能进行了仿真和验证。

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