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首页> 外文期刊>IEEE Transactions on Energy Conversion >Application of an inverse input/output mapped ANN as a power system stabilizer
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Application of an inverse input/output mapped ANN as a power system stabilizer

机译：逆输入/输出映射的神经网络在电力系统稳定器中的应用

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摘要

An artificial neural network (ANN), trained as an inverse of the controlled plant, to function as a power system stabilizer (PSS) is presented in this paper. In order to make the proposed ANN PSS work properly, it was trained over the full working range of the generating unit with a large variety of disturbances. Data used to train the ANN PSS consisted of the control input and the synchronous machine response with an adaptive PSS (APSS) controlling the generator. During training, the ANN was required to memorize the reverse input/output mapping of the synchronous machine. After the training, the output of the synchronous machine was applied as the input of the ANN PSS and the output of the ANN PSS was used as the control signal. Simulation results show that the proposed ANN PSS can provide good damping of the power system over a wide operating range and significantly improve the system performance.
机译：本文提出了一种人工神经网络（ANN），其被训练为受控设备的逆函数，以充当电力系统稳定器（PSS）。为了使拟议的ANN PSS正常工作，在发电机组的整个工作范围内对它进行了培训，并具有多种干扰。用于训练ANN PSS的数据由控制输入和同步电机响应组成，其中自适应PSS（APSS）控制发电机。在训练期间，需要ANN来记住同步机的反向输入/输出映射。训练后，将同步机的输出用作ANN PSS的输入，并将ANN PSS的输出用作控制信号。仿真结果表明，所提出的人工神经网络PSS可以在较大的工作范围内为电力系统提供良好的阻尼，并显着改善系统性能。

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