title: Ð¡Ð¸Ð½ÑÐµÐ· Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÐµÐ½ÑÑÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ ÑÐµÐ³ÑÐ»ÑÑÐ¾ÑÐ° Ð²Ð¾Ð·Ð±ÑÐ¶Ð´ÐµÐ½Ð¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Synthesis and research of a centralized adaptive excitation regulator based on neural networks
Creators: name: ÐÐ¾Ð»ÑÑÐºÐ¸Ð½, ÐÐ°ÑÐ¸Ð»Ð¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: ÑÐµÑÑÑÐµÑ Ð¼Ð°ÑÐ¸Ð½Ð½Ð°Ñ ÑÐ½ÐµÑÐ³Ð¾ÑÐ¸ÑÑÐµÐ¼Ð° ÐºÑÐ½Ð´ÑÑÐ°
ÑÐ¸ÑÑÐµÐ¼Ð° Ð²Ð¾Ð·Ð±ÑÐ¶Ð´ÐµÐ½Ð¸Ñ
Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
four-machine kundur power system
excitation system
artificial intelligence
reinforcment learning
machine learning

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«Ð¡Ð¸Ð½ÑÐµÐ· Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÐµÐ½ÑÑÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ ÑÐµÐ³ÑÐ»ÑÑÐ¾ÑÐ° Ð²Ð¾Ð·Ð±ÑÐ¶Ð´ÐµÐ½Ð¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹Â». 
Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¸ÑÑÐ»ÐµÐ´ÑÐµÑÑÑ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÑ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð·Ð°Ð¼ÐµÐ½Ñ ÑÑÐ°Ð´Ð¸ÑÐ¸Ð¾Ð½Ð½ÑÑ ÑÐ¸ÑÑÐµÐ¼ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ. ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² Ð¿ÑÐ¾ÑÐµÑÑÐµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ:
1. ÐÐ½Ð°Ð»Ð¸Ð· ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð² Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ ÑÐ¸ÑÑÐµÐ¼ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ, Ð¾ÑÐ»Ð¸ÑÐ½ÑÑ Ð¾Ñ ÑÑÐ°Ð´Ð¸ÑÐ¸Ð¾Ð½Ð½ÑÑ, Ð² ÑÐ¾Ð¼ ÑÐ¸ÑÐ»Ðµ Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½ÑÑ Ð¸ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð², Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ ÑÐ¸ÑÑÐµÐ¼ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ ÐºÑÑÐ¿Ð½ÑÑ ÑÐ½ÐµÑÐ³Ð¾Ð¾Ð±ÑÐµÐ´Ð¸Ð½ÐµÐ½Ð¸Ð¹.
2. ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð½ÐµÐ¿Ð¾ÑÑÐµÐ´ÑÑÐ²ÐµÐ½Ð½Ð¾ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ ÑÐ¸ÑÑÐµÐ¼ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð² ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÐµÑÐ³ÐµÑÐ¸ÐºÐµ.
3. Ð¡ÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÑÑÐ°Ð´Ð¸ÑÐ¸Ð¾Ð½Ð½ÑÑ ÑÐ¸ÑÑÐµÐ¼ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð¸ Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½ÑÑ ÑÐ¸ÑÑÐµÐ¼ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ¹ Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼. ÐÑÐµÐ½Ð¸ÑÑ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÐ¼ÑÑ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð², ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÑÑ Ð¿ÑÐµÐ¸Ð¼ÑÑÐµÑÑÐ²Ð° Ð¸ Ð½ÐµÐ´Ð¾ÑÑÐ°ÑÐºÐ¸.
ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð»Ð¾ÑÑ Ð½Ð° Ð±Ð°Ð·Ðµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ ÐºÐ¾Ð¼Ð¿Ð»ÐµÐºÑÐ° Matlab, Ð²ÐºÐ»ÑÑÐ°Ñ ÑÐ°ÐºÐ¸Ðµ ÐµÐ³Ð¾ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½ÑÑ ÐºÐ°Ðº Simulink, ÑÑÐ»Ð±Ð¾ÐºÑÑ Control System, Neural Networks, Deep Learning, Reinforcement Learning Ð¸ Ñ.Ð´.
Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ Ð¼ÐµÑÐ¾Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐµÐ¼ÑÐµ Ð¿ÑÐ¸ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ð¸ Ð²Ð¾Ð·Ð±ÑÐ¶Ð´ÐµÐ½Ð¸ÐµÐ¼ Ð² ÑÐ½ÐµÑÐ³Ð¾ÑÐ¸ÑÑÐµÐ¼Ð°Ñ, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ Ðº Ð³Ð»Ð¾Ð±Ð°Ð»ÑÐ½Ð¾Ð¼Ñ ÑÐµÐ³ÑÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ. ÐÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ñ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¾ Ð²Ð°ÑÐ¸Ð°Ð½ÑÐ¾Ð² Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÑ ÑÐ¸ÑÑÐµÐ¼ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð° Ð¸Ñ ÑÐ°Ð±Ð¾ÑÐ°.
description: The subject of the graduate qualification work is "Synthesis and research of a centralized adaptive excitation regulator based on neural networks "
This paper explores the possibility of using neural network technologies as a replacement for traditional control systems. The research set the following goals:
1. Analysis of existing control systems, different from traditional, including adaptive and neural network methods, in the field of control systems of large power systems.
2. Application of algorithms directly based on artificial neural networks in the field of control systems in the electric power industry.
3. Comparison of traditional control systems and adaptive control systems based on neural networks and reinforcement learning. Assess the effectiveness of the proposed approaches, consider the advantages and disadvantages.
The study was conducted on the basis of the Matlab software package, including its components such as Simulink, Control System, Neural Networks, Deep Learning, Reinforcement Learning, etc.
The paper analyzes the methods of machine learning used in controlling the excitation in energy systems, as well as approaches to global regulation. Several options for neural network control systems were proposed and their work is investigated.