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A hybrid method for islanding stability detection of distributed generators using wavelet transform and artificial neural networks

机译:基于小波变换和人工神经网络的分布式发电机孤岛稳定性检测的混合方法

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Although in many cases islanding is considered an undesired situation, the intentional islanding can reduce the interruption indices of the distribution power systems. However the distributed synchronous generator must mantain both voltage and frequency in the operational limits of the islanded system. This study proposes the use of artificial neural networks and Wavelet Transform to detect stable islanding. Then, the distributed generator keeps powering the loads in the island. Otherwise the passive anti-islanding protection (frequency or voltage relays) disconnect the distributed generator to prevent any damage to the loads. The islanding stability detection method proposed in this paper perform high success rates on identifying the system's stability, even in conditions of white noise in the voltage and current measurements.
机译:尽管在许多情况下孤岛被认为是不希望的情况,但是故意的孤岛可以减少配电系统的中断指数。但是,分布式同步发电机必须在孤岛系统的运行极限内同时保持电压和频率。这项研究建议使用人工神经网络和小波变换来检测稳定的孤岛。然后,分布式发电机继续为岛上的负载供电。否则,无源防孤岛保护(频率或电压继电器)会断开分布式发电机的连接,以防止对负载造成任何损坏。本文提出的孤岛稳定性检测方法即使在电压和电流测量中存在白噪声的情况下,也能在识别系统稳定性方面取得很高的成功率。

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