基于神经网络和非线性原—对偶内点算法的电网无功优化补偿

REACTIVE POWER OPTIMIZATION COMPENSATION BASED ON NEURAL NETWORK AND NONLINEAR PRIME-DUALINTERIOR ALGORITHM

在简要分析了传统的电力系统无功优化的方法后 ,针对无功优化计算中离散变量和连续变量共存的问题 ,提出了用神经网络对补偿后电网的质量参数进行预测 ,并结合求解无功优化的非线性原 -对偶内点算法进行全局寻优 ,实现对电网无功优化补偿的控制方法 .结果表明 ,该控制系统提高了系统的功率因数 ,减少了系统的损耗 ,初步解决了电网参数复杂、补偿系统难以建模等问题 。

This paper briefly analyses the conventional reactive power optimization compensation. The new method proposes a new optimization reactive power compensation for electrical network that uses neural network to predict electric network's important parameters and nonlinear prime dual interior algorithm to optimize reactive power. This intelligent control system diminishs power losses, and settles the problems that the electric power has complicated parameters and it is hard to constitute the compensation system model. The result shows that the effect of this intelligent control system is good and this algorithm is valid.