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.