The invention discloses a 10kV
feeder line fault prediction method based on a CNN (
Convolutional Neural Network) and LightGBM (LightGBM), and the method mainly comprises the steps: 1), obtaining the
original data of a power distribution network, and carrying out the preprocessing of the
original data of the power distribution network; 2) extracting features from the
original data of the power distribution network, and constructing a
feature set f {f1, f2... F15, f16, L}, wherein L is a
label and represents whether the
feeder line has a fault or not, the element f1, the element f2, the elementf3, the element f4, the element f5 and the element f6 are inherent attribute characteristics, the element f7, the element f8, the element f9, the element f10, the element f11, the element f12 and theelement f13 are
statistical analysis characteristics, and the element f14, the element f15 and the element f16 are depth
time sequence features extracted by a
convolutional neural network CNN; 3) establishing a power distribution network fault prediction model; and 4) inputting the real-
time data of the 10kV
feeder line of the power distribution network into the power distribution network fault prediction model to obtain a feeder line fault prediction result. The 10kV feeder line fault prediction method has the advantages that the
rapidity and timeliness are better, and the prediction result can provide auxiliary decision support for the operation and
maintenance management personnel of the power distribution
network on the premise of meeting the accuracy requirement.