H. Shayeghi, A. Younesi,
Volume 16, Issue 4 (12-2020)
Abstract
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.
Azzedine Khati,
Volume 20, Issue 3 (9-2024)
Abstract
In this research paper, a multivariable prediction control method based on direct vector control is applied to command the active power and reactive power of a doubly-fed induction generator used into a wind turbine system. To obtain high energy performance, the space vector modulation inverter based on fuzzy logic technique (fuzzy space vector modulation) is used to reduce stator currents harmonics and active power and reactive power ripples. Also the direct vector control model of the doubly-fed induction generator is required to ensure a decoupled control. Then its classic proportional integral regulators are replaced by the multivariable prediction controller in order to adjust the active and reactive power. So, in this work, we implement a new method of control for the doubly-fed induction generator energy. This method is carried out for the first time by combining the MPC strategy with artificial intelligence represented by Fuzzy SVM-based converter in order to overcome the drawbacks of other controllers used in renewable energies. The given simulation results using Matlab software show a good performance of the used strategy, particularly with regard to the quality of the energy supplied.
Nurul Husna Abd Wahab, Mohd Hafizuddin Mat, Norezmi Md Jamal, Nur Hidayah Ramli,
Volume 21, Issue 2 (6-2025)
Abstract
In islanded microgrids, circulating currents among parallel inverters pose significant challenges to system stability and efficient power distribution. Traditional droop control methods often struggle to manage these currents effectively, leading to inefficiencies and potential system damage. This study introduces an advanced fuzzy-robust droop control strategy that integrates fuzzy logic with robust droop control to address these challenges. By incorporating fuzzy logic, the proposed strategy enhances the adaptability of droop control to varying system conditions, improving the management of circulating currents and ensuring more accurate power sharing among inverters. Comprehensive mathematical modeling and extensive simulation analyses validate the performance of this control strategy. The results show that the fuzzy-robust droop control method significantly outperforms conventional approaches, achieving up to a 70% reduction in circulating currents. This improvement leads to a substantial reduction in power losses and enhances the dynamic response under varying load conditions. Additionally, the strategy improves voltage and frequency regulation, contributing to the overall stability and reliability of the microgrid. The findings provide a robust solution to the longstanding issue of circulating currents, optimizing microgrid operations, and paving the way for more efficient and resilient distributed energy systems. The advanced control strategy presented in this study not only addresses critical challenges but also demonstrates the potential for innovative methodologies to meet the growing demands of future energy infrastructures, where reliability and efficiency are essential.
Kumuthawathe Ananda-Rao, Steven Taniselass, Afifah Shuhada Rosmi, Aimi Salihah Abdul Nasir, Nor Hanisah Baharudin, Indra Nisja,
Volume 21, Issue 2 (6-2025)
Abstract
This study presents a Fuzzy Logic Controller (FLC)-based Maximum Power Point Tracking (MPPT) system for solar Photovoltaic (PV) setups, integrating PV panels, a boost converter, and battery storage. While FLC is known for its robustness in PV systems, challenges in battery charging and discharging efficiency can affect performance. The research addresses these challenges by optimizing battery charging, preventing overcharging, and enhancing overall system efficiency. The FLC MPPT system is designed to regulate the battery's State of Charge (SOC) while evaluating system performance under varying solar irradiance and temperature conditions. The system is modeled and simulated using MATLAB/Simulink, incorporating the PV system, MPPT algorithm, and models for the PV module and boost converter. System efficiency is assessed under different scenarios, with results showing 97.92% efficiency under Standard Test Conditions (STC) at 1000 W/m² and 25°C. Additionally, mean efficiencies of 97.13% and 96.13% are observed under varying irradiance and temperature, demonstrating the effectiveness of the FLC MPPT in regulating output. The system also extends battery life by optimizing power transfer between the PV module, boost converter, and battery, ensuring regulated SOC.
