基于混沌粒子群算法的光伏电站站内无功优化

REACTIVE POWER OPTIMIZATION OF PV POWER PLANT STATION BASED ON CHAOS-PARTICLE SWARM OPTIMIZATION

光照强度的变化不仅会造成光伏电站并网点电压波动,而且可能会导致光伏发电单元出口电压越限,呈现较大的时空分散特性。并且随着光伏有功出力增加,光伏电站的稳定性会下降。针对上述问题,在建立大型光伏电站的模型上,采用改进的混沌粒子群算法对光伏电站进行多目标无功优化,以均衡光伏电站站内节点电压,提高其稳定裕度,并降低有功网损为目标,仿真验证所提方法的正确性。通过求解向量组的极大线性无关组判断粒子的进化方向,有利于粒子保持多样性。

The large photovoltaic power station is composed of a plurality of groups of photovoltaic generating units in parallel. Illumination of light intensity will not only cause the PCC voltage fluctuation,but also may cause the voltage of the photovoltaic power generation unit exit the limit,showing a large space and time dispersion characteristics. And with the increase of active power output,and the stability of photovoltaic power plants will decline. Aiming at balancing the node voltage of photovoltaic power station,improving the stability margin and reducing the active power loss,multi objective reactive power optimization is applied by improved chaos particle swarm optimization algorithm in photovoltaic power plant,basing on the model of large scale photovoltaic power station. Finally,the correctness of the proposed method is verified by simulation. In this paper,the evolution direction of the particle is judged by the maximal linear independent group of the vector group,which is beneficial to the diversity of the particle.