Owing to the problems of tedious setting process of key parameter and unreasonable setting results during the performing of traditional reactive power optimization

firstly

a curve division strategy based on slope segment merging is presented to divide the forecasting interval intelligently. Secondly

a hybrid strategy of standard Euclidean distance-dynamic time warping(ED-DTW)is used to calculate the similar distance of different data sets. Finally

an intelligent identifying framework of reactive power optimization parameter based on data mining association is proposed to mine the data in the database. The simulation is analyzed by adopting real grid data. The mining results show that the parameter identifying framework could automatically give parameter time division and setting results. The mining parameter used in real control shows that the calculation speed of this approach is fast and has better effect on reducing voltage deviation and improving qualified voltage rate.