Future distribution networks with increasing level of solar PV penetration will be managed using smart grid technologies capable of producing appropriate and timely response during normal and abnormal operational events. Distribution feeder loads vary throughout the day according to the trend of consumption of the customers. Solar PV outputs fluctuate in proportion to irradiance level of sun. Simultaneous occurrence of both of these variations would result in various operating conditions that may lead to unexpected events, and would require a large amount of network data to be processed and analyzed for decision making. It is envisaged that such data will be available in the future grids with the availability of smart technologies and advanced communication in residential dwellings, commercial buildings and industrial complexes. In this paper, an advanced intelligent computational tool is developed to characterize and analyze the large amount of data associated with wide variations in network behavior using SAX (Symbolic Aggregate Approximation) and pattern recognition. The proposed tool is capable of dealing with network asymmetry, load unbalance, single-phase solar PV integration and their impacts on upstream networks and will assist in making right and timely decision to mitigate adverse impacts of solar PV. The proposed tool has been tested with a practical three-phase distribution system in Australia and can provide an extensive assessment with less computational efforts and time.