A Model-Free Distributed Cooperative Frequency Control Strategy for MT-HVDC Systems Using Reinforcement Learning Method

2021 ◽  
Author(s):  
Zhong-Jie Hu ◽  
Zhi-Wei Liu ◽  
Chaojie Li ◽  
Tingwen Huang ◽  
Xiong Hu
Keyword(s):  
Reinforcement Learning ◽  
Control Strategy ◽  
Frequency Control ◽  
Learning Method ◽  
Model Free

scholarly journals Acceleration control strategy for aero-engines based on model-free deep reinforcement learning method

2021 ◽  
pp. 107248
Author(s):  
Wenbo Gao ◽  
Muxuan Pan ◽  
Wenxiang Zhou ◽  
Feng Lu ◽  
Jinquan Huang
Keyword(s):  
Reinforcement Learning ◽  
Control Strategy ◽  
Learning Method ◽  
Model Free ◽  
Aero Engines

scholarly journals Assessing the Use of Reinforcement Learning for Integrated Voltage/Frequency Control in AC Microgrids

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1250
Author(s):  
Abdollah Younesi ◽  
Hossein Shayeghi ◽  
Pierluigi Siano
Keyword(s):  
Reinforcement Learning ◽  
Power Systems ◽  
Control Strategy ◽  
Distribution System ◽  
Frequency Control ◽  
Dynamic Performance ◽  
Iterative Solution ◽  
Pid Controllers ◽  
Model Free ◽  
Markov Decision

The main purpose of this paper is to present a novel algorithmic reinforcement learning (RL) method for damping the voltage and frequency oscillations in a micro-grid (MG) with penetration of wind turbine generators (WTG). First, the continuous-time environment of the system is discretized to a definite number of states to form the Markov decision process (MDP). To solve the modeled discrete RL-based problem, Q-learning method, which is a model-free and simple iterative solution mechanism is used. Therefore, the presented control strategy is adaptive and it is suitable for the realistic power systems with high nonlinearities. The proposed adaptive RL controller has a supervisory nature that can improve the performance of any kind of controllers by adding an offset signal to the output control signal of them. Here, a part of Denmark distribution system is considered and the dynamic performance of the suggested control mechanism is evaluated and compared with fuzzy-proportional integral derivative (PID) and classical PID controllers. Simulations are carried out in two realistic and challenging scenarios considering system parameters changing. Results indicate that the proposed control strategy has an excellent dynamic response compared to fuzzy-PID and traditional PID controllers for damping the voltage and frequency oscillations.

scholarly journals Solving Channel Allocation by Reinforcement Learning in Cognitive Enabled Vehicular Ad Hoc Networks

2021 ◽  
Author(s):  
Yunfan Su
Keyword(s):  
Dynamic Programming ◽  
Reinforcement Learning ◽  
Optimal Policy ◽  
Ad Hoc ◽  
Transition Probabilities ◽  
Channel Allocation ◽  
Dynamic Programming Method ◽  
Learning Method ◽  
Time Intervals ◽  
Model Free

Vehicular ad hoc network (VANET) is a promising technique that improves traffic safety and transportation efficiency and provides a comfortable driving experience. However, due to the rapid growth of applications that demand channel resources, efficient channel allocation schemes are required to utilize the performance of the vehicular networks. In this thesis, two Reinforcement learning (RL)-based channel allocation methods are proposed for a cognitive enabled VANET environment to maximize a long-term average system reward. First, we present a model-based dynamic programming method, which requires the calculations of the transition probabilities and time intervals between decision epochs. After obtaining the transition probabilities and time intervals, a relative value iteration (RVI) algorithm is used to find the asymptotically optimal policy. Then, we propose a model-free reinforcement learning method, in which we employ an agent to interact with the environment iteratively and learn from the feedback to approximate the optimal policy. Simulation results show that our reinforcement learning method can acquire a similar performance to that of the dynamic programming while both outperform the greedy method.

scholarly journals Solving Channel Allocation by Reinforcement Learning in Cognitive Enabled Vehicular Ad Hoc Networks

2021 ◽  
Author(s):  
Yunfan Su
Keyword(s):  
Dynamic Programming ◽  
Reinforcement Learning ◽  
Optimal Policy ◽  
Ad Hoc ◽  
Transition Probabilities ◽  
Channel Allocation ◽  
Dynamic Programming Method ◽  
Learning Method ◽  
Time Intervals ◽  
Model Free

Vehicular ad hoc network (VANET) is a promising technique that improves traffic safety and transportation efficiency and provides a comfortable driving experience. However, due to the rapid growth of applications that demand channel resources, efficient channel allocation schemes are required to utilize the performance of the vehicular networks. In this thesis, two Reinforcement learning (RL)-based channel allocation methods are proposed for a cognitive enabled VANET environment to maximize a long-term average system reward. First, we present a model-based dynamic programming method, which requires the calculations of the transition probabilities and time intervals between decision epochs. After obtaining the transition probabilities and time intervals, a relative value iteration (RVI) algorithm is used to find the asymptotically optimal policy. Then, we propose a model-free reinforcement learning method, in which we employ an agent to interact with the environment iteratively and learn from the feedback to approximate the optimal policy. Simulation results show that our reinforcement learning method can acquire a similar performance to that of the dynamic programming while both outperform the greedy method.

Energy Optimization of Solar Micro-Grid Using Multi Agent Reinforcement Learning

2015 ◽  
Vol 787 ◽  
pp. 843-847
Author(s):  
Leo Raju ◽  
R.S. Milton ◽  
S. Sakthiyanandan
Keyword(s):  
Reinforcement Learning ◽  
Energy Savings ◽  
Learning Method ◽  
Solar Pv ◽  
Q Learning ◽  
Pv Systems ◽  
Model Free ◽  
Individual Unit ◽  
Multi Agent ◽  
Micro Grid

In this paper, two solar Photovoltaic (PV) systems are considered; one in the department with capacity of 100 kW and the other in the hostel with capacity of 200 kW. Each one has battery and load. The capital cost and energy savings by conventional methods are compared and it is proved that the energy dependency from grid is reduced in solar micro-grid element, operating in distributed environment. In the smart grid frame work, the grid energy consumption is further reduced by optimal scheduling of the battery, using Reinforcement Learning. Individual unit optimization is done by a model free reinforcement learning method, called Q-Learning and it is compared with distributed operations of solar micro-grid using a Multi Agent Reinforcement Learning method, called Joint Q-Learning. The energy planning is designed according to the prediction of solar PV energy production and observed load pattern of department and the hostel. A simulation model was developed using Python programming.

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