融合智能代理模型和改进微分进化算法的电力系统暂态稳定预防控制

Transient Stability Preventive Control of Power System Based on Intelligent Surrogate Model and Improved Differential Evolution Algorithm

摘要:

电力系统暂态稳定预防控制可看作含微分方程的非线性规划优化模型,开发一种寻优能力强、计算速度快的优化求解方法非常关键。考虑暂态稳定预防控制问题的特点,提出一种融合智能代理模型和改进微分进化算法优化求解算法。在微分进化算法的基础上,引入扩展变异操作及参数自适应调整策略,提高微分进化算法的寻优能力,并利用改进微分进化算法求解预防控制优化模型。同时,为提高求解速度,利用潮流特征和集成极限学习机建立暂态稳定裕度预测的智能代理模型,在迭代优化过程中快速估计稳定裕度水平,避免反复进行时域仿真计算。仿真结果表明,所提方法不仅增强了传统智能优化算法的寻优能力,并能大幅度减少预防控制策略的求解时间。

Abstract:

:Transient stability preventive control of power system can be expressed mathematically as a nonlinear dynamic programming problem with differential equation，and it is very important to develop an optimization algorithm with strong optimization ability and small computational cost． An optimization algorithm mixed with intelligent surrogate model and improved differential evolution algorithm is proposed to solve transient stability preventive control problem． Based on differential evolution algorithm，the extended mutation operation and the parameter adaptive adjustment strategy are introduced to improve the optimization ability of differential evolution algorithm． Meanwhile，in order to improve the speed of solving the optimal power flow， the power flow characteristic and ensemble extreme learning machine regression model are employed to predict the transient stability boundary in the iterative process． The simulation results show that the proposed method not only improves the optimization ability of the traditional intelligent optimization algorithm，but also greatly reduces the solving time complexity．