Design of relief-cavity in closed-precision forging of gears

To reduce the difficulty of material filling into the top region of tooth in hot precision forging of gears using the alternative die designs, relief-cavity designs in different sizes were performed on the top of die tooth. The influences of the conventional process and relief-cavity designs on corner filling, workpiece stress, die stress, forming load and material utilization were examined. Finite element simulation for tooth forming, die stress and forming load using the four designs was performed. The material utilization was further considered, and the optimal design was determined. The tooth form and forming load in forging trials ensured the validity of FE simulation. Tooth accuracy was inspected by video measuring machine(VMM), which shows the hot forged accuracy achieves the level of rough machining of gear teeth. The effects of friction on mode of metal flow and strain distribution were also discussed.

A Data-driven Method for Fast AC Optimal Power Flow Solutions via Deep Reinforcement Learning

With the increasing penetration of renewable energy,power grid operators are observing both fast and large fluctuations in power and voltage profiles on a daily basis.Fast and accurate control actions derived in real time are vital to ensure system security and economics.To this end,solving alternating current(AC)optimal power flow(OPF)with operational constraints remains an important yet challenging optimization problem for secure and economic operation of the power grid.This paper adopts a novel method to derive fast OPF solutions using state-of-the-art deep reinforcement learning(DRL)algorithm,which can greatly assist power grid operators in making rapid and effective decisions.The presented method adopts imitation learning to generate initial weights for the neural network(NN),and a proximal policy optimization algorithm to train and test stable and robust artificial intelligence(AI)agents.Training and testing procedures are conducted on the IEEE 14-bus and the Illinois 200-bus systems.The results show the effectiveness of the method with significant potential for assisting power grid operators in real-time operations.

Deep Reinforcement Learning Based Real-time AC Optimal Power Flow Considering Uncertainties

Modern power systems are experiencing larger fluctuations and more uncertainties caused by increased penetration of renewable energy sources(RESs) and power electronics equipment. Therefore, fast and accurate corrective control actions in real time are needed to ensure the system security and economics. This paper presents a novel method to derive realtime alternating current(AC) optimal power flow(OPF) solutions considering the uncertainties including varying renewable energy and topology changes by using state-of-the-art deep reinforcement learning(DRL) algorithm, which can effectively assist grid operators in making rapid and effective real-time decisions. The presented DRL-based approach first adopts a supervised-learning method from deep learning to generate good initial weights for neural networks, and then the proximal policy optimization(PPO) algorithm is applied to train and test the artificial intelligence(AI) agents for stable and robust performance. An ancillary classifier is designed to identify the feasibility of the AC OPF problem. Case studies conducted on the Illinois 200-bus system with wind generation variation and N-1 topology changes validate the effectiveness of the proposed method and demonstrate its great potential in promoting sustainable energy integration into the power system.