  This paper proposes a new deep learning (DL) based model-free robust method
for bulk system on-line load restoration with high penetration of wind power.
Inspired by the iterative calculation of the two-stage robust load restoration
model, the deep neural network (DNN) and deep convolutional neural network
(CNN) are respectively designed to find the worst-case system condition of a
load pickup decision and evaluate the corresponding security. In order to find
the optimal result within a limited number of checks, a load pickup checklist
generation (LPCG) algorithm is developed to ensure the optimality. Then, the
fast robust load restoration strategy acquisition is achieved based on the
designed one-line strategy generation (OSG) algorithm. The proposed method
finds the optimal result in a model-free way, holds the robustness to handle
uncertainties, and provides real-time computation. It can completely replace
conventional robust optimization and supports on-line robust load restoration
which better satisfies the changeable restoration process. The effectiveness of
the proposed method is validated using the IEEE 30-bus system and the IEEE
118-bus system, showing high computational efficiency and considerable
accuracy.
