点多面广、分散无序的分布式光伏电站规模化接入电网是我国新型电力系统向低碳演进的重要路径之一.低成本、高效率的分布式光伏电站数据获取是光伏电站开展精细化管理、精益化运维的重要基础.为此,该文提出一种基于改进多目标鲸鱼优化算法(improved multi-objective whale optimization algorithm,IMOWOA)与轻量梯度提升机(light gradient boosting machine,LightGBM)的分布式光伏数据虚拟采集方案.针对虚拟采集区域划分难题,该方案首先在网格化区域划分的基础上提出一种自编码器相似性分析方法,获取满足相似性需求的光伏电站集;为解决参考电站集选择难题,提出一种改进的多目标鲸鱼优化算法,提高算法的全局搜索能力,基于区域内光伏电站的历史功率数据,同时对参考电站子集与LightGBM超参数进行优化,从而实现仅选取部分分布式光伏电站安装完备的数据采集装置,完成区域范围内所有电站功率数据的高精度虚拟采集.最后,以我国江苏省某区域范围内的 29 个分布式光伏电站为算例进行分析,验证提出的方法的可行性和有效性.
The large-scale access to the grid of distributed PV power stations with many points and wide,scattered,and disorderly is one of the crucial paths for the evolution of China's new power system towards low carbon.Therefore,low-cost and high-efficiency distributed PV power station operation data collection is the primary condition for its exemplary management and lean operation and maintenance.To this end,this paper proposes a distributed PV virtual collection scheme based on the improved multi-objective whale optimization algorithm(IMOWOA)and light gradient boosting machine(LightGBM).For the virtual collection region division problem,the scheme first proposes an autoencoder similarity analysis method under the premise of grided region division to obtain the PV power station set satisfying the similarity requirement.In order to solve the problem of reference power station set selection,an improved multi-objective whale optimization algorithm is proposed to improve the global search capability of the algorithm.The subset of reference power plants and LightGBM hyperparameters is optimized simultaneously based on the region's historical power data of photovoltaic power stations,thus achieving selection of only distributed photovoltaic power stations for installation of complete data collection devices to complete high-precision virtual collection for power data of all stations within the region.Finally,the feasibility and effectiveness of the proposed virtual collection method are verified by analyzing 29 distributed PV stations in a regional area of Jiangsu Province,China.