| 基于人工神经网络的发电机故障暂态稳定性预测方法研究 |
投稿时间：2021-09-13 修订日期：2021-09-22  点此下载全文 |
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| 中文摘要:提出了一种利用径向基函数神经网络(Radial Basis Function Neural Nework, RBFNN)预测大扰动后发电机转子转角值的方法，来实时判断系统暂态稳定状态，并对相干发电机组的辨识进行了研究。在故障后将相量测量单元（Phasor Measurement Unit, PMU）同步采样的前六个周期的发电机的转子角度和电压等数据作为神经网络的输入，以预测系统未来的状态。该方法还可以实时判断发电机的同步状态。在测试系统上对该方法以不同运行条件进行了评估，实验结果证明所提出的径向基函数神经网络对扰动后的转子转角值具有良好的预测性能，适合于实时应用。 |
| 中文关键词:故障预测 暂态分析 人工神经网络 |
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| Research on fault transient stability prediction method of generator based on artificial neural network |
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| Abstract:A method using radial basis function neural nework (RBFNN) to predict the rotor angle of the generator after large disturbances is proposed to judge the transient and stable state of the system in real time, and to identify the coherent generator set. Research. After a fault, the phasor measurement unit (PMU) synchronously sampled the rotor angle and voltage of the generator in the first six cycles as the input of the neural network to predict the future state of the system. This method can also judge the synchronization status of the generator in real time. The method is evaluated under different operating conditions on the test system. The experimental results prove that the proposed radial basis function neural network has good predictive performance on the rotor angle value after disturbance, and is suitable for real-time applications. |
| keywords:Fault prediction Transient analysis artificial neural network |
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