Emotional deep learning programming controller for automatic voltage control of power systems - Yin, Linfei; Zhang, Chenwei; Wang, Yaoxiong; Gao, Fang; Yu, Jun; Cheng, Lefeng (IEEE access, 2021)
In recent years, the rapid development of artificial intelligence, especially deep learning technology, makes machine learning have application scenarios in the fields of power system stability analysis, coordination along with scheduling and load forecasting. This paper designs an emotional deep learning programming controller (EDLPC) for automatic voltage control of power systems. The designed EDLPC contains an emotional deep neural network (EDNN) structure and an artificial emotional Q-learning algorithm. Besides, a specially defined proportional-integral-derivative (PID) controller is added to the deep neural networks (DNNs) structure as the actuator of an EDNN to realize the automatic tuning of PID controller parameters. In terms of control, the controller combines the advantages of the EDNN and PID controller, meanwhile adopts a reinforcement learning algorithm to optimize the parameters. From the perspective of reinforcement learning, embedding prior knowledge into the output instructions of EDNN is helpful to weaken the fitting problem in the training process. Compared with the outputs of the DNN and Q-learning algorithm under the two cases, the EDLPC could gain the highest control performance with smaller voltage deviations. The simulation results verify the feasibility and effectiveness of the proposed method for automatic voltage control of power systems.