The invention discloses a user side reactive compensation optimization control method and
system based on
machine learning, and the method comprises the steps: obtaining the operation historical data of a power distribution network, carrying out the preprocessing, and constructing a
data set; training and verifying a
capacitor switching quantity prediction
machine learning model by using the
data set; constructing a multi-objective
loss function based on the capacitive reactive switching amount, the
power factor and the
voltage amplitude; constructing an objective function for a prediction result of the trained
capacitor switching amount prediction
machine learning model, and performing optimization to obtain an optimization result; adding a hard constraint condition to an optimization result, and forcing the output of the model to be within a set range; and the
capacitor switching amount prediction
machine learning model deployed behind the embedded device performs
online learning according to the real-
time data. According to the method, multiple targets of self-adaptive compensation
decision making, synchronous optimization of power factors,
voltage stability, equipment service life and the like in a dynamic scene can be realized, and the reactive
compensation effect is effectively improved.