Search published articles

Showing 5 results for Younesi

H. Shayeghi, A. Younesi,
Volume 13, Issue 4 (December 2017)

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) algorithm and are fixed. The second one is a reinforcement learning (RL) based supplementary controller that has a flexible structure and improves the output of the first stage adaptively based on the system dynamical behavior. Due to the use of RL paradigm integrated with PID controller in this strategy, it is called RL-PID controller. The primary motivation for the integration of RL technique with PID controller is to make the existing local controllers in the industry compatible to reduce the control efforts and system costs. This novel control strategy combines the advantages of the PID controller with adaptive behavior of MA to achieve the desired level of robust performance under different kind of uncertainties caused by stochastically power generation of DERs, plant operational condition changes, and physical nonlinearities of the system. The suggested decentralized controller is composed of the autonomous intelligent agents, who learn the optimal control policy from interaction with the system. These agents update their knowledge about the system dynamics continuously to achieve a good frequency oscillation damping under various severe disturbances without any knowledge of them. It leads to an adaptive control structure to solve LFC problem in the multi-source power system with stochastic DERs. The results of RL-PID controller in comparison to the traditional PID and fuzzy-PID controllers is verified in a multi-area power system integrated with DERs through some performance indices.

R. Pour Ebrahim, S. Tohidi, A. Younesi,
Volume 14, Issue 1 (March 2018)

In this paper, a new sensorless model reference adaptive method is used for direct control of active and reactive power of the doubly fed induction generator (DFIG). In order to estimate the rotor speed, a high frequency signal injection scheme is implemented. In this study, to improve the accuracy of speed estimation, two methods are suggested. First, the coefficients of proportional-integral (PI) blocks are optimized by using Krill Herd algorithm. In the second method, the fuzzy logic control method is applied in the estimator structure instead of PI controllers. The simulation results for the proposed methods illustrate that the estimated speed perfectly matches the actual speed of the DFIG. In addition, the desired slip value is achieved due to the accurate response. On the other hand, the active and reactive power responses have fast dynamics and relatively low oscillations. Moreover, the fuzzy controller shows more robustness against the variations of machine parameters.

A. Younesi, S. Tohidi, M. R. Feyzi,
Volume 14, Issue 3 (September 2018)

Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be implemented in real time. In order to solve this problem, this paper presents an improved strategy in the field of nonlinear MPC (NMPC) of the permanent magnet synchronous motor (PMSM). The proposed method applies a sequence of reduction weighting coefficients in the cost function, over the prediction horizon. By using the proposed strategy, NMPC give a more accurate response with less number of prediction horizon. This means the computational time is reduced. It also suggests using an incremental algorithm to reduce the computational time. Performance of the proposed Nonlinear MPC (NMPC) scheme is compared with the previous NMPC methods via simulations performed by MATLAB/Simulink software, in permanent magnet synchronous motor drive system. The results show that the use of proposed structure not only lowers prediction horizon and hence computational time, but also it improves speed tracking performance and reduces electromagnetic torque ripple. In addition, using the incremental algorithm also reduces the computational time which makes it suitable for real-time applications.

A. Younesi, H. Shayeghi,
Volume 15, Issue 1 (March 2019)

The purpose of this paper is to design a supplementary controller for traditional PID controller in order to damp the frequency oscillations in a micro-grid. Q-learning, which is used for supervise a classical PID controller in this paper, is a model free and a simple solution method of reinforcement learning (RL). RL is one of the branches of the machine learning, which is the main solution method of Markov decision process (MDPs). The proposed control mechanism is consisting of two main parts. The first part is a classical PID controller which is fixed tuned using Salp swarm algorithm. The second part is a Q‑learning based control strategy which is consistent and updates its characteristics according to the changes in the system continuously. Eventually, a hybrid micro-grid is considered to evaluate the performance of the suggested control method compared to classical PID and fractional order fuzzy PID (FOFPID) controllers. The considered hybrid system is consisting of renewable energy resources such as solar-thermal power station (STPS) and wind turbine generation (WTG), along with several energy storage devices such as batteries, flywheel and ultra-capacitor with physical constraints and time delays. Simulations are carried out in various realistic scenarios considering system parameter variations along with changing in operating conditions. Results indicate that the proposed control strategy has an excellent dynamic response compared to the traditional PID and FOFPID controllers for damping the frequency oscillations in different operating conditions.

H. Shayeghi, A. Younesi,
Volume 16, Issue 4 (December 2020)

The main objective of this paper is to model and optimize the parallel and relatively complex FuzzyP+FuzzyI+FuzzyD (FP+FI+FD) controller for simultaneous control of the voltage and frequency of a micro-grid in the islanded mode. The FP+FI+FD controller has three parallel branches, each of which has a specific task. Finally, as its name suggests, the final output of the controller is derived from the algebraic summation of the outputs of these three branches. Combining the basic features of a simple PID controller with fuzzy logic that leads to an adaptive control mechanism, is an inherent characteristic of the FP+FI+FD controller. This paper attempts to determine the optimal control gains and Fuzzy membership functions of the FP+FI+FD controller using an improved Salp swarm algorithm (ISSA) to achieve its optimal dynamic response. The time-domain simulations are carried out in order to prove the superb dynamic response of the proposed FP+FI+FD controller compared to the PID control methods. In addition, a multi-input-multi-output (MIMO) stability analysis is performed to ensure the robust control characteristic of the proposed parallel fuzzy controller.

Page 1 from 1     

Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.