基于ANFIS方法的电力系统稳定性控制器设计

S. Khuntia, S. Panda
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引用次数: 20

摘要

本文采用基于人工神经网络(ANN)的自适应神经模糊推理系统(ANFIS)方法设计了一种基于晶闸管控制串联补偿器(TCSC)的控制器,以提高电力系统的稳定性。设计目标是提高转子角度稳定性和系统电压分布。该控制器结合了模糊控制器的优点和人工神经网络的快速响应和自适应特性。利用模糊控制器生成的数据库对ANFIS结构进行训练。结果表明,基于tcsc的ANFIS控制器对故障定位和工况变化具有较强的鲁棒性。进一步,将所得结果与传统的超前滞后控制器进行了比较。
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ANFIS approach for TCSC-based controller design for power system stability improvement
In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) method based on the Artificial Neural Network (ANN) is applied to design a Thyristor Controlled Series Compensator (TCSC)-based controller to enhance the power system stability. The design objective is to improve both rotor angle stability and system voltage profile. The proposed ANFIS controller combines the advantages of fuzzy controller and quick response and adaptability nature of ANN. The ANFIS structures were trained using the generated database by fuzzy controllers of TCSC. The results prove that the proposed TCSC-based ANFIS controller is found to be robust to fault location and change in operating conditions. Further, the results obtained are compared with the conventional lead-lag controllers for TCSC.
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