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Probabilistic Neural Network Based Islanding Detection in Distributed Generation

机译：分布式发电中基于概率神经网络的孤岛检测

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摘要

This article proposes a probabilistic neural network based islanding detection technique for distributed generation considering multiple parameters in order to secure the detection of islanding for any possible network topology. This approach utilizes different parameters (features), such as rate of change of frequency, rate of change of voltage, rate of change of power, rate of change of frequency over power, total harmonic distortion (current), and rate of change of power factor, derived at the target distributed generation location and fed to the probabilistic neural network for automatic islanding detection. While comparing with other techniques, such as the radial basis function neural network and decision tree, the probabilistic neural network based technique is found to be highly effective in islanding detection. The proposed algorithm has been tested for simulation model, as well as the realtime digital simulator, and results indicate that the proposed method can reliably detect islanding conditions in the power distribution network with multiple distributed generation interface.
机译：本文提出了一种基于概率神经网络的孤岛检测技术，用于考虑多个参数的分布式发电，以确保对任何可能的网络拓扑均进行孤岛检测。该方法利用不同的参数（特征），例如频率变化率，电压变化率，功率变化率，频率随功率变化率，总谐波失真（电流）和功率变化率因子，从目标分布式发电位置导出，并馈送到概率神经网络以进行自动孤岛检测。与径向基函数神经网络和决策树等其他技术进行比较时，发现基于概率神经网络的技术在孤岛检测中非常有效。对所提算法进行了仿真模型以及实时数字仿真器的测试，结果表明该方法能够可靠地检测具有多个分布式发电接口的配电网孤岛状况。

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