Artificial Gorilla Troops Optimizer for Frequency Regulation of Wind Contributed Microgrid System

Ramesh, M.; Yadav, Anil Kumar; Pathak, Pawan Kumar

doi:10.1115/1.4056135

Cited by 17 publications

(10 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the genetic algorithm requires a large number of samples for solution, incurs a high time cost, and may fall into local optimal solutions. Pathak et al 18 , 19 used an artificial gorilla force optimizer (GTO) to obtain controller parameters. The results show that the method has the advantages of small frequency deviation, short stability time and small integration error.…”

Section: Introductionmentioning

confidence: 99%

Optimization of PID control parameters for marine dual-fuel engine using improved particle swarm algorithm

Hu,

Guo,

Zhou

et al. 2024

Sci Rep

View full text Add to dashboard Cite

This study presents a comprehensive investigation into the optimization of PID control parameters for marine dual-fuel engines using an improved particle swarm algorithm. Through the development of a Matlab/Simulink simulation model, the thermodynamic behavior of the engine and the functionality of its control system are analyzed. The PID control parameters for air–fuel ratio control and mode switching control systems are fine-tuned utilizing the improved particle swarm algorithm (PSO). Simulation results demonstrate that the proposed improved PID-PSO approach outperforms traditional PID and traditional PSO-PID control methods in terms of reduced overshoot, minimized steady-state error, faster response times, and improved stability across various operating conditions and response modes. In comparison to traditional PID and PSO-PID controllers, the improved PSO-PID controller reduces the response time by 0.47 s and 0.21 s, the maximum overshoot by 98.43% and 96.05%, and decreases the absolute errors by 87.42% and 90.55%, respectively, in air–fuel ratio control using the step response method. The study's findings offer valuable insights into enhancing the performance and efficiency of marine dual-fuel engines through advanced control strategies.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimization of PID control parameters for marine dual-fuel engine using improved particle swarm algorithm

Hu,

Guo,

Zhou

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The proposed system was applied on the IEEE 39-bus system, including solar PV, wind turbine, and diesel generator units. In [28], an autonomous diesel-wind microgrid was illustrated with a PID frequency controller and an integral-type sliding mode control (I-SMC) for effective load frequency control (LFC). Utilizing the artificial gorilla troops optimizer (GTO) for controller parameter tuning, the results showed minimal frequency deviations, faster settling times, and reduced integral errors compared to other methods.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Kepler Optimization Method for Nonlinear Multi-Dimensional Optimal Power Flow

Alqahtani,

Almutairi,

Shaheen

et al. 2024

Axioms

View full text Add to dashboard Cite

Multi-Dimensional Optimal Power Flow (MDOPF) is a fundamental task in power systems engineering aimed at optimizing the operation of electrical networks while considering various constraints such as power generation, transmission, and distribution. The mathematical model of MDOPF involves formulating it as a non-linear, non-convex optimization problem aimed at minimizing specific objective functions while adhering to equality and inequality constraints. The objective function typically includes terms representing the Fuel Cost (FC), Entire Network Losses (ENL), and Entire Emissions (EE), while the constraints encompass power balance equations, generator operating limits, and network constraints, such as line flow limits and voltage limits. This paper presents an innovative Improved Kepler Optimization Technique (IKOT) for solving MDOPF problems. The IKOT builds upon the traditional KOT and incorporates enhanced local escaping mechanisms to overcome local optima traps and improve convergence speed. The mathematical model of the IKOT algorithm involves defining a population of candidate solutions (individuals) represented as vectors in a high-dimensional search space. Each individual corresponds to a potential solution to the MDOPF problem, and the algorithm iteratively refines these solutions to converge towards the optimal solution. The key innovation of the IKOT lies in its enhanced local escaping mechanisms, which enable it to explore the search space more effectively and avoid premature convergence to suboptimal solutions. Experimental results on standard IEEE test systems demonstrate the effectiveness of the proposed IKOT in solving MDOPF problems. The proposed IKOT obtained the FC, EE, and ENL of USD 41,666.963/h, 1.039 Ton/h, and 9.087 MW, respectively, in comparison with the KOT, which achieved USD 41,677.349/h, 1.048 Ton/h, 11.277 MW, respectively. In comparison to the base scenario, the IKOT achieved a reduction percentage of 18.85%, 58.89%, and 64.13%, respectively, for the three scenarios. The IKOT consistently outperformed the original KOT and other state-of-the-art metaheuristic optimization algorithms in terms of solution quality, convergence speed, and robustness.

show abstract

“…Complex control algorithms are used by the system to establish each generator's optimal power output based on real-time changes in the system's creating capacity and load demand. [5][6][7] The complexity of MPS is rising as a result of the diversity of power demand. In order to minimize the discrepancy in traditional power plants, RESs are applied in MPS.…”

Section: Introductionmentioning

confidence: 99%

Enhancing frequency regulation in multi‐area interconnected MPS with virtual inertia using MPC + PIDN controller

Vidyarthi,

Kumar

2024

Optim Control Appl Methods

View full text Add to dashboard Cite

The challenge of controlling frequency deviation becomes more difficult as the complexity of a power network increases. The robustness of the controller has a major impact on the stability of a Modern Power system (MPS). Due to the hybridization of MPS basic AGC controllers (PID, FOPID, and TID) are insufficient to give optimal performance of a plant. This requires a robust controller. So, a novel MPC + PIDN controller has been proposed and evaluated by comparing it with several existing controllers, which gives optimal performance in terms of overshoot, undershoot, and settling time. A new modified Opposition‐based Sea‐horse Optimization (OSHO) method has been suggested to optimize the various controller settings. To demonstrate the OSHO's superiority, it is compared with a few popular, existing meta‐heuristic optimizations. The higher penetration levels of RESs reduced system inertia which further deteriorate frequency response in MPS. To overcome these challenges virtual inertia (VI) is implemented with MPC. VI is applied to improve the performance of the AGC of the interconnected MPS along with emphasizing the nature of intermittent renewable energy sources (RESs) of PV and wind energy. To determine the reliability and flexibility of the proposed controller, analysis has been done under a different situation, including step, random disturbances, and modified IEEE‐39 bus. Finally, the stability analysis is performed on a bode plot and the proposed results are compared with previously published literature. The extensive study demonstrates strong evidence that the suggested control approach is efficient and effective.

show abstract

Artificial Gorilla Troops Optimizer for Frequency Regulation of Wind Contributed Microgrid System

Cited by 17 publications

References 37 publications

Optimization of PID control parameters for marine dual-fuel engine using improved particle swarm algorithm

Optimization of PID control parameters for marine dual-fuel engine using improved particle swarm algorithm

Enhanced Kepler Optimization Method for Nonlinear Multi-Dimensional Optimal Power Flow

Enhancing frequency regulation in multi‐area interconnected MPS with virtual inertia using MPC + PIDN controller

Contact Info

Product

Resources

About