Showing 3 results for Pid Control
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.
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.
Suhail Mahmoud Abdullah, Thamir Hassan Atyia,
Volume 21, Issue 4 (11-2025)
Abstract
Optimal control of DC motors remains a critical research area in modern control systems, given their wide industrial applications and the need for accurate performance under variable conditions. This paper explores the application of genetic algorithms (GAs) to optimize the control parameters of DC motors, particularly PID controllers, with the goal of improving the dynamic response and robustness of DC motor systems. Compared to traditional constraint-based tuning methods, GAs, inspired by natural selection and evolution, offer comprehensive search capabilities that significantly improve parameter optimization, providing better speed regulation, reduced overshoot, and minimal steady-state error. This review highlights the key challenges faced when using GAs. Comparative results from various studies demonstrate that GA-based controllers consistently outperform traditional tuning methods in terms of stability, efficiency, and adaptability. Key findings related to energy consumption and stability are highlighted. It is essential to analyze the system performance in terms of rise time (tr), settling time (ts), overshoot ratio (Mp%), and steady-state error (Ess). A proportional-integral-differential (PID) controller provides a stable response by tuning its parameters according to a specific methodology using a genetic algorithm. This paper concludes by emphasizing the potential of genetic generators as a powerful and flexible optimization tool for intelligent control of DC motors.
