Distribution customers and prosumers are extremely concerned about power quality in relation to the contemporary smart grid for the power industry (PQ). In fact, they choose to pay more money to ensure that they have access to a consistent and high-quality power supply. Even if the quality of the voltage and current offered to customers is a big concern, operators continue to place all of their attention on reliability. There are no established norms for tracking, penalising, or enforcing PQ-based tariff structures in LV distribution networks. To address the challenge of monitoring electrical grids in the face of unexpected occurrences resulting from the introduction of new energy sources, this paper presents a graphical cluster analysis-based approach that could be applied in Smart Power Quality Analysers (SPQAs), a proposed Instrument Class S in compliance with the standard UNE-EN 61000-4-30. Instruments can initiate the measuring operation based on a predetermined PQ threshold, producing the time-domain higher-order statistics (HOS) features necessary for classification in the event of an electrical outage. The results are good, with two separate classes of signals (sags and transients) and an accuracy of 80% over a battery of 160 signals. This paper also introduces the uncertainty inter-cluster area. Grid operators can improve database characterisation and introduce certain qualitative criteria for smart grid monitoring due to analysis of intensity cluster disruptions.