Xingquan Ji

TL;DR: A power system state forecasting model based on long-short term memory neural network is established, which can solve the problem of missing data combining power flow calculation and has high accuracy and robustness.

TL;DR: A novel real-time autonomous dynamic reconfiguration (ADR) method to reduce the cost of power loss and switch action of distribution network based on the deep learning (DL) algorithm that can be decision-making from the historical control dataset and the real- time system state.

TL;DR: A decentralized and parallel analytical target cascading (ATC) algorithm for interactive unit commitment (UC) implementation in regional power systems and the startup/shutdown variables of the thermal units and the variables in TG + ADN +-MG are integrated into the multi-level interactive UC model to optimize simultaneously, thus realizing the optimal goal of the whole network, resources complementary and optimal allocation of power system.

TL;DR: In this article, a dynamic three-phase symmetric distribution network reconfiguration (DNR) approach based on hierarchical clustering with timing constraints was developed, which can divide the time period according to the time-varying symmetric load demand and symmetric distributed generations (DGs) output condition for a given time interval.

TL;DR: An improved temporal constrained hierarchical clustering approach considering the temporal constraint is used to divide the operating status of the distribution network into multiple time intervals according to the similarity of loads and the output power of distributed generations.