This paper develops a machine learning aggregated integer linear programming approach for the full observability of the automated smart grids by positioning of micro-synchrophasor units, taking into account the reconfigurable structure of the distribution systems. The proposed stochastic approach presents a strategy occurring in several stages to micro-synchrophasor unit positioning based on the load level and demand in the system and based on the pre-determined sectionalizing and tie switches. Such a technique can also deploy the zero-injection limitations of the model and reduce the search space of the problem. Moreover, a novel method based on whale optimization method (WOM) is introduced to simultaneously enhance the reliability indices in order to specify the optimum topology for each phase and reduce the costs of power losses and customer interruptions. Although the problem of micro-synchrophasor placement is formulated in an integer linear programming framework, the restructuring technique is resolved on the basis of the WOM heuristic approach. Considering the uncertainty due to the metering devices or forecast errors, a stochastic framework based on point estimation is deployed to handle the uncertainty effects. The simulation and numerical results on a real system verify that the proposed method assures visibility of the distribution network pre and post reconfiguration in the time horizon of the planning. Furthermore, the results show that the system observability can be guaranteed at different load levels even though the system experiences different reconfiguration and topologies.

本文开发了一种机器学习聚合整数线性规划方法，通过微同步相量单元的定位实现自动化智能电网的完全可观测性，同时考虑到配电系统的可重构结构。所提出的随机方法提出了一种策略，该策略基于系统中的负载水平和需求，并基于预先确定的分段和联络开关，分几个阶段进行微同步相量单元定位。这种技术还可以部署模型的零注入限制并减少问题的搜索空间。而且，引入了一种基于鲸鱼优化方法 (WOM) 的新方法来同时增强可靠性指标，以便为每相指定最佳拓扑并降低功率损耗和客户中断的成本。虽然微同步相量放置问题是在整数线性规划框架中制定的，但重构技术是在 WOM 启发式方法的基础上解决的。考虑到计量装置或预测误差造成的不确定性，部署了基于点估计的随机框架来处理不确定性影响。真实系统上的模拟和数值结果验证了所提出的方法确保了在规划的时间范围内重新配置前后配电网络的可见性。此外，