基于BP-PSO的SVC附加阻尼控制大电网试验

静止无功补偿器是电网的重要设备,参数选择将直接影响其性能。然而,传统参数优化方法只能适用于简单网络,为了在交直流复杂电网中有良好的应用,提出了反向传播神经网络—粒子群算法(BP-PSO)用于全网模型下对SVC阻尼控制器参数的优化。PSO算法的目标函数用训练好的BP神经网络拟合而成的参数曲线替代。利用PSASP构建了全网的机电模型并进行网络划分,在ADPSS上搭建含有SVC及其附近变电站的电磁模型,从而组成一个闭环的、实时的试验平台来验证该算法的准确性。实验结果表明,在SVC投入大电网时,应用本文算法优化后的阻尼控制器能抑制系统低频振荡,线路有功功率阻尼比大致可以提高2%到3%。

基于神经网络法的自锚式悬索桥可靠度研究

自锚式悬索桥造型美观,且不需要庞大的锚碇。其将主缆锚固于加劲梁上,受力发生了极大的变化。因此,研究自锚式悬索桥的可靠性十分必要。采用BP神经网络法拟合可靠度计算的极限状态函数,引入粒子群算法优化神经网络法的初始权值,实现函数拟合的双优化,新算法则利用MATLAB编程实现。极限状态函数显化后,结合蒙特卡洛法计算自锚式悬索桥在正常使用极限状态下的可靠度。

Fault Location in Transmission Lines Using BP Neural Network Trained with PSO Algorithm

In modem protection relays, the accurate and fast fault location is an essential task for transmission line protection from the point of service restoration and reliability. The applications of neural networks based fault location techniques to transmission line are available in many papers. However, almost all the studies have so far employed the FNN （feed-forward neural network） trained with back-propagation algorithm （BPNN） which has a better structure and been widely used. But there are still many drawbacks if we simply use feed-forward neural network, such as slow training rate, easy to trap into local minimum point, and bad ability on global search. In this paper, feed-forward neural network trained by PSO （particle swarm optimization） algorithm is proposed for fault location scheme in 500 kV transmission system with distributed parameters presentation, The purpose is to simulate distance protection relay. The algorithm acts as classifier which requires phasor measurements data from one end of the transmission line and DFT （discrete Fourier transform）. Extensive simulation studies carried out using MATLAB show that the proposed scheme has the ability to give a good estimation of fault location under various fault conditions.