Approximate Value Iteration in the Reinforcement Learning Context. Application to Electrical Power System Control
نویسندگان
چکیده
In this paper we explain how to design intelligent agents able to process the information acquired from interaction with a system to learn a good control policy and show how the methodology can be applied to control some devices aimed to damp electrical power oscillations. The control problem is formalized as a discrete-time optimal control problem and the information acquired from interaction with the system is a set of samples, where each sample is composed of four elements: a state, the action taken while being in this state, the instantaneous reward observed and the successor state of the system. To process this information we consider reinforcement learning algorithms that determine an approximation of the so-called Q-function by mimicking the behavior of the value iteration algorithm. Simulations are first carried on a benchmark power system modeled with two state variables. Then we present a more complex case study on a four-machine power system where the reinforcement learning algorithm controls a Thyristor Controlled Series Capacitor (TCSC) aimed to damp power system oscillations.
منابع مشابه
Low-Area/Low-Power CMOS Op-Amps Design Based on Total Optimality Index Using Reinforcement Learning Approach
This paper presents the application of reinforcement learning in automatic analog IC design. In this work, the Multi-Objective approach by Learning Automata is evaluated for accommodating required functionalities and performance specifications considering optimal minimizing of MOSFETs area and power consumption for two famous CMOS op-amps. The results show the ability of the proposed method to ...
متن کاملVoltage Coordination of FACTS Devices in Power Systems Using RL-Based Multi-Agent Systems
This paper describes how multi-agent system technology can be used as the underpinning platform for voltage control in power systems. In this study, some FACTS (flexible AC transmission systems) devices are properly designed to coordinate their decisions and actions in order to provide a coordinated secondary voltage control mechanism based on multi-agent theory. Each device here is modeled as ...
متن کاملMini/Micro-Grid Adaptive Voltage and Frequency Stability Enhancement Using Q-learning Mechanism
This paper develops an adaptive control method for controlling frequency and voltage of an islanded mini/micro grid (M/µG) using reinforcement learning method. Reinforcement learning (RL) is one of the branches of the machine learning, which is the main solution method of Markov decision process (MDPs). Among the several solution methods of RL, the Q-learning method is used for solving RL in th...
متن کاملAn Online Q-learning Based Multi-Agent LFC for a Multi-Area Multi-Source Power System Including Distributed Energy Resources
This paper presents an online two-stage Q-learning based multi-agent (MA) controller for load frequency control (LFC) in an interconnected multi-area multi-source power system integrated with distributed energy resources (DERs). The proposed control strategy consists of two stages. The first stage is employed a PID controller which its parameters are designed using sine cosine optimization (SCO...
متن کاملApplication of variational iteration method for solving singular two point boundary value problems
In this paper, He's highly prolic variational iteration method is applied ef-fectively for showing the existence, uniqueness and solving a class of singularsecond order two point boundary value problems. The process of nding solu-tion involves generation of a sequence of appropriate and approximate iterativesolution function equally likely to converge to the exact solution of the givenproblem w...
متن کامل