چکیده مقاله
The main purpose of this study is evaluating and classifying the design control methods for the microgrids systems to maintain stability and load variations by adjusting the controllerparameters especially stand alone operation In normal operation, distributed generation units provide power quality control In different distributed generation, there is a big challenge in controlling the system In facing with the challenge, two methods PID and MPC have been introduced and compared with each other The PID controller consists of three filter, proportional, integrate, differential Using the powerful efficiency of Deep learning methods, the introduced and compared with each other The PID controller consists of three filter, proportional, integrate, differential Using the powerful efficiency of Deep learning methods, the map between PID controller input and output can be easily learned via supervising learning In this paper, two Neural Networks and training of each of them have been studied which are the two networks, Convolutional Neural Network and Auto Encoder In this paper, Training of CNN and AE networks based on deep learning approach are presented
کلیدواژهها
نویسندگان
Mohammad Mohseni Ahad
Department of Automation, Petroleum University, Ahwaz, Iran,
شیوه ارجاع
Mohseni Ahad, Mohammad,1397,Training Convolutional and Auto-Encoder Neural-Networks Based on Deep Learning Approach to Designing PID-Controller for Micro Grid Systems,International Conference on Interdisciplinary Studies in Electrical, Computer, Mechanical and Mechatronics Engineering in Iran and the Islamic World,Karaj
ارائهشده در
مجموعه مقالات کنفرانس بین المللی تحقیقات بین رشته ای در مهندسی برق، کامپیوتر، مکانیک و مکاترونیک در ایران و جهان اسلام31 شهریور 1397 · کرج