多智能体搜寻者优化算法在电力系统无功优化中的应用

针对无功优化这个典型的非线性问题,提出了一种基于多Agent系统的搜寻者优化算法MASOA (Multi-agent Seeker Optimization Algorithm)来求解.该算法针对SOA算法邻域划分随意性较大,融入智能体技术,在改进SOA算法邻域划分合理性的同时,提高粒子寻优的准确度;利用SOA算法的进化机制,引入自适应思想,使新算法具有良好的非线性搜索能力,更好地适应无功优化问题.以网损最小为目标函数,在IEEE 30节点系统上进行测试,并与四种智能算法进行比较,结果表明,MASOA在算法计算精度、收敛稳定性、寻优时间等方面都具有普遍优势,能有效地应用于电力系统无功优化中.

Reactive power optimization in power system based on multi-agent seeker optimization algorithm

A multi-agent seeker optimization algorithm (MASOA) is proposed for optimal reactive power dispatch and voltage control of power system that is a typical non-linear problem. According to the more arbitrary partition of neighborhood region for SOA, the algorithm has combined with agent technology and has increased the accuracy of particles optimization with improving the rationality of partition of neighborhood region for SOA. By adopting the evolutional mechanism of SOA to import adaptive method, it has been manifested that the novel algorithm has possessed good nonlinear search capability and has better effective for the optimization problem of reactive power. MASOA applied for optimal reactive power is evaluated on an IEEE 30-bus power system. The modeling of reactive power optimization is established taking the minimum network losses as the objective. It is shown that the proposed approach converges to better solutions much faster than the earlier four reported approaches and the algorithm can make effectively use in reactive power optimization.