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首页> 外文期刊>Science, Measurement & Technology, IET >Phasor estimation in power systems using a neural network with online training for numerical relays purposes
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Phasor estimation in power systems using a neural network with online training for numerical relays purposes

机译：使用神经网络和数字继电器在线培训的电力系统相量估计

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摘要

There are a few components of the current signal that may lead to inaccurate current measurement in power systems, and therefore, may cause malfunction on numerical protective relays and control devices. Some of these components include harmonics, the decaying DC offset, and noises. In this study, a phasor estimation method based on artificial neural networks is proposed, which will provide fast response time and accuracy. The method uses the multilayer perceptron structure to precisely estimate the amplitude and phase angle of the current waveform by determining its input weights during an online training process. The proposed algorithm is tested and compared with other reliable and well-known methods for a performance evaluation.
机译：电流信号中的一些分量可能会导致电力系统中电流测量不准确，因此可能导致数字保护继电器和控制设备出现故障。其中一些成分包括谐波，衰减的DC偏移和噪声。本文提出了一种基于人工神经网络的相量估计方法，该方法将提供快速的响应时间和准确度。该方法使用多层感知器结构通过在在线训练过程中确定其输入权重来精确估计电流波形的幅度和相位角。测试了所提出的算法，并将其与其他可靠且众所周知的方法进行性能评估。

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