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Estimation of voltage stability index for power system employing artificial neural network technique and TCSC placement

机译:利用人工神经网络技术和TCSC位置估计电力系统电压稳定性指标

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This paper proposes a scheme for online voltage stability monitoring for various load conditions using feed forward back propagation network (FFBPN). A single FFBPN with minimal number of neurons is used to estimate the line voltage stability index for various load conditions. A sequential learning strategy is used to design the FFBPN and the weights in the output layer are determined by using linear optimization. The proposed network is applied on the IEEE 14-bus and the IEEE 30-bus power system and line stability indices are calculated for different loading conditions. Based on the calculated indices, the ranking of weakest lines is done. The optimal location for placement of Thyristor controlled series capacitor (TCSC) has been identified among the weakest lines for improving the voltage stability in the power system. The proposed network can be adapted with changing operating scenario of the power system.
机译:本文提出了一种使用前馈传播网络(FFBPN)在线监测各种负载条件下的电压稳定性的方案。具有最少神经元数量的单个FFBPN用于估计各种负载条件下的线路电压稳定性指标。使用顺序学习策略设计FFBPN,并使用线性优化确定输出层中的权重。拟议的网络应用于IEEE 14总线和IEEE 30总线电力系统,并针对不同的负载条件计算线路稳定性指标。根据计算出的指标,对最弱的线进行排名。在最弱的线路中,已经确定了放置晶闸管控制串联电容器(TCSC)的最佳位置,以改善电力系统中的电压稳定性。所建议的网络可以适应电力系统不断变化的运行情况。

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