Abstract:In order to mitigate voltage sag related problems based on power quality monitoring data, it is meaningful to improve the efficiency of power quality data analysis. Due to depending on accurate models of voltage sag, the traditional methods are inadequate for complex problems with multiple uncertainty factors. Therefore, the spatial-temporal multi-granular attribute analysis of voltage sag data and a related knowledge discovery method were proposed in this paper. Inspired by the cognitive hierarchy of complex problems, a framework consisted of “data-characteristic-index-information-knowledge” was proposed as a general technical route for voltage sag related problems. Based on the framework, to solve the problem of information loss caused by single granular, sag information in different granular was extended by voltage sag spatial-temporal multi-granular analysis. The relationship between power system structure attribute and voltage sag was discovered by granular reduction. Then, knowledge about voltage sag severity and propagation was derived. The synthetic and measured data were used to validate the effectiveness of the proposed method. Results showed that the proposed method can describe and resolve complex problems with many uncertainty factors.