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An artificial neural network based adaptive power system stabilizer

机译:基于人工神经网络的自适应电力系统稳定器

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An artificial neural network (ANN)-based power system stabilizer (PSS) and its application to power systems are presented. The ANN-based PSS combines the advantages of self-optimizing pole shifting adaptive control strategy and the quick response of ANN to introduce a new generation PSS. A popular type of ANN, the multilayer perceptron with error backpropagation training method, is used in this PSS. The ANN was trained by the training data group generated by the adaptive power system stabilizer (APSS). During the training, the ANN was required to memorize and simulate the control strategy of APSS until the differences were within the specified criteria. Results show that the proposed ANN-based PSS can provide good damping of the power system over a wide operating range and significantly improve the dynamic performance of the system.
机译:提出了一种基于人工神经网络(ANN)的电力系统稳定器(PSS)及其在电力系统中的应用。基于ANN的PSS结合了自优化极移自适应控制策略的优点和ANN的快速响应,从而推出了新一代PSS。在此PSS中使用了一种流行的ANN,即带有错误反向传播训练方法的多层感知器。通过自适应电源系统稳定器(APSS)生成的训练数据组对ANN进行了训练。在训练过程中,需要ANN来记忆和模拟APSS的控制策略,直到差异在指定的标准之内。结果表明,所提出的基于ANN的PSS可以在较大的工作范围内为电力系统提供良好的阻尼,并显着改善系统的动态性能。

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