基于重要抽样与极限学习机的大电网可靠性评估

Reliability evaluation of large power system based on combination of important sampling and extreme learning machine

由于不确定因素多、电网规模大,原始蒙特卡洛模拟(MCS)在复杂电力系统可靠性评估中无法满足实时高效的要求。提出一种基于交叉熵(CE)的重要抽样与极限学习机(ELM)相结合的可靠性评估算法,一方面通过在系统抽样环节引入CE构建元件的最优概率分布,减小方差变化,加快指标收敛速度;另一方面,采用ELM对重要抽样的状态样本进行有监督学习,以所构建的网络学习模型替代传统非线性规划方法进行状态评估,提高单次系统状态评估的效率,从而实现快速可靠性评估。对IEEE RTS-79系统进行可靠性评估,与原始MCS和CE重要抽样的对比结果表明,在一定的误差范围内所提算法合理、有效,其计算效率较原始MCS和CE显著提高。

Due to many uncertainty factors and large-scale power grid,the traditional MCS( Monte Carlo Simulation)cannot meet the requirements of real-time and high efficiency in the reliability evaluation of complex power system.A reliability evaluation algorithm with the combination of CE( Cross-Entropy) based important sampling and ELM( Extreme Learning Machine) is proposed. On one hand,CE is introduced in the system sampling step to construct the equipment optimal probability distribution,decrease the variance variation and speed up the index convergence.On the other hand,ELM is adopted for supervised learning of the state samples from important sampling,and the traditional nonlinear programming method is replaced by the constructed network learning model for state evaluation,so as to improve the efficiency of single evaluation and realize fast reliability evaluation. Reliability evaluation is carried out in IEEE RTS-79 system,and comparison with the results of traditional MCS and CE important sampling shows that the proposed algorithm is rational and effective in a certain error range,and its computational efficiency is significantly higher than that of the traditional MCS and CE.