A novel identification and location method for transient power quality disturbance sources

The application of power electronics and high penetration of new energy generation have brought great economic and social benefits. Meanwhile, new power quality phenomena and new issues have come into existence, such as three-phase unbalance, harmonic and low-frequency resonance, etc. In order to solve power quality problems fundamentally. The paper proposes a novel identifying and locating method utilizing transient power quality data. Based on the operational big data of power system, spatio-temporal data model of power quality is established. Then, based on big data, by adopting random forest algorithm, the data is analyzed to identify and locate transient power quality problem disturbance sources. This paper simulates in IEEE 30 bus system, and the results indicate the accuracy in identifying and locating short circuit disturbance sources.