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Showing 5 results for Pid
Improvement of Frequency Fluctuations in Microgrids Using an Optimized Fuzzy P-PID Controller by Modified Multi Objective Gravitational Search Algorithm
H. Shayeghi, A. Ghasemi,
Volume 12, Issue 4 (12-2016)
Abstract

Microgrids is an new opportunity to reduce the total costs of power generation and supply the energy demands through small-scale power plants such as wind sources, photo voltaic panels, battery banks, fuel cells, etc. Like any power system in micro grid (MG), an unexpected faults or load shifting leads to frequency oscillations. Hence, this paper employs an adaptive fuzzy P-PID controller for frequency control of microgrid and a modified multi objective Chaotic Gravitational Search Algorithm (CGSA) in order to find out the optimal setting parameters of the proposed controller. To provide a robust controller design, two non-commensurable objective functions are formulated based on eigenvalues-domain and time-domain and multi objective CGSA algorithm is used to solve them. Moreover, a fuzzy decision method is applied to extract the best and optimal Pareto fronts. The proposed controller is carried out on a MG system under different loading conditions with wind turbine generators, photovoltaic system, flywheel energy, battery storages, diesel generator and electrolyzer. The simulation results revealed that the proposed controller is more stable in comparison with the classical and other types of fuzzy controller.


Intelligent Control for the Variable-Speed Variable-Pitch Wind Energy System
M. Heidari,
Volume 13, Issue 3 (9-2017)
Abstract
In this paper, a new type of multi-variable compensation control method for the wind energy conversion systems (WECS) is presented. Based on wind energy conversion systems, combining artificial neural network (ANN) control and PID, a new type of PID NN intelligent controller for steady state torque of the wind generator is designed, by which the steady state torque output is regulated to track the optimal curve of wind power factor and the blade pitch angle is regulated to keep the stable power output. Also, the LPV model of the WECS, LPV compensator for the wind generator is designed to effectively compensate output of the wind generator torque and the blade pitch angle. Finally, simulation models of the control system based on a realistic model of a 8kw wind turbines are built up based on the Dspace platform. The results show that the proposed method can reduce interferences caused by disturbed parameters of the WECS, mechanical shocks of the wind generator speed are reduced while capturing the largest wind energyfluctuation range of wind generator power output is reduced, and the working efficiency of the variable pitch servo system is improved.

An Online Q-learning Based Multi-Agent LFC for a Multi-Area Multi-Source Power System Including Distributed Energy Resources
H. Shayeghi, A. Younesi,
Volume 13, Issue 4 (12-2017)
Abstract

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.


Optimal Temperature Control Scheme for Milk Pasteurization Process Using Different Tuning Techniques for a Proportional Integral Derivative Controller
A. O. Amole, O. E. Olabode, D. O. Akinyele, S. G. Akinjobi,
Volume 18, Issue 3 (9-2022)
Abstract
Milk is one of the important dairy foods, which forms an essential building block in the feed formulation for infant and growing children, and adults alike. However, the quality of the final product largely depends on the temperature of the pasteurization process. It is, therefore, a necessity to ensure that optimum temperature is maintained during pasteurization process, as over-temperature kills all the essential nutrients contained in the final product and similarly, low temperature is not desirable as the final product will not yield the desired nutritional value. As a result, the application of optimal temperature control scheme is a critical requirement for milk pasteurization. It is, on this background, that this paper presents the use of a Proportional (P), Integral (I), Derivative (D) abbreviated as PID controller for optimal control of temperature in the milk pasteurization process. The milk pasteurization temperature was modeled based on the first law of thermodynamics, while three different tuning techniques namely; Zigler-Nichols (ZN), Chien-Hrones-Reswick (CHR) and Cohen-Coon (CC) were employed to tune the PID controller for optimal control of the milk pasteurization temperature. The control schemes were simulated in MATLAB/Simulink, and the performance of each tuning technique was evaluated using the rise time, settling time, peak amplitude, and overshoot. Results showed that ZN tuned PID controller gave the lowest rise time, settling time, and peak amplitude of 0.177s, 0.34s, and 0.993, respectively, while the lowest overshoot of 0% was attained by both ZN and CHR. Based on these results, CC tuned PID controller exhibited moderate rise time of 1.02s, settling time of 6.49s, and overshoot of 5.67%, indicating that its performance is comparatively preferred with respect to other tuning techniques investigated. The results of this research find application in diary industries as it provides insight into the appropriate tuning technique for the PID controller to ensure optimum temperature control during milk pasteurization.

Design of the PD-FOPID Controller Based on Rain Optimization Algorithm to Improve Virtual Inertia Control Performance in Islanded Microgrids
Farhad Amiri, Mohammad H. Moradi,
Volume 21, Issue 1 (3-2025)
Abstract
Low inertia is one of the most important challenges for frequency maintenance in islanded microgrids. To address this issue, the innovative concept of Virtual Inertia Control (VIC) has emerged as a promising solution for enhancing frequency stability in such systems. This paper presents an advanced controller, the PD-FOPID, as a highly effective technique for improving the efficiency of VIC in islanded microgrids. By leveraging the Rain Optimization Algorithm (ROA), this approach enables precise fine-tuning of the controller's parameters. A key advantage of the proposed method is its inherent resilience to disruptions and uncertainties caused by parameter fluctuations in islanded microgrids. To evaluate its performance and compare it with alternative control methods, extensive assessments were conducted across various scenarios. The comparison includes VIC based on an H-infinity controller (Controller 1), VIC based on an MPC controller (Controller 2), Adaptive VIC (Controller 3), VIC based on an optimized PI controller (Controller 4), conventional VIC (Controller 5), and systems without VIC (Controller 6). The results demonstrate that the proposed methodology significantly outperforms existing approaches in the field of VIC. The simulations were conducted using MATLAB software.
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© 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.