计及运行风险的无功优化强化学习智能算法

为提高电力系统的运行安全性,本文将电力系统风险评估理论引入到传统无功优化中,建立了考虑运行风险的多目标无功优化数学模型,并为此提出了一种全新的迁移部落强化学习算法,该算法将人工智能算法的随机搜索机制和强化学习算法的迭代模式有机融合,利用知识矩阵储存部落寻优信息,通过知识迁移显著提高了在线学习阶段算法的速率。IEEE 118节点标准系统的仿真表明：迁移部落强化学习算法在保证较好的全局寻优性能的同时,速度可达传统人工智能算法的2-10倍,有效解决了考虑风险的多目标无功优化的动态快速求解。

Transfer tribe reinforcement learning algorithm for multi-objective reactive power optimization

In order to improve the operational security of power system, the risk assessment theory is introduced in classic reactive power optimization (RPO). So a new mathematical model of risk-based multi-objective reactive power optimization (RMRPO) is established. Besides, a novel transfer tribe reinforcement learning (TTRL) is proposed to solve the RMRPO problem, which effectively combines the tribal search collaboration and the trial-error process of reinforcement learning. The Q-matrix is adopted as the knowledge matrix to store the optimization information. Then the knowledge transfer can be executed to accelerate the rate of the proposed algorithm. The simulation on IEEE 118-bus system demonstrates that the rate of TTRL can be 2 to 10 times faster than other existing artificial intelligence algorithm. Meanwhile, the quality of the optimal solutions of TTRL can be guaranteed.