Gravitational-Search Algorithm for Optimal Controllers Design of Doubly-fed Induction Generator

Albatran, Saher; Alomoush, Muwaffaq I.; Koran, Ahmed

doi:10.11591/ijece.v8i2.pp780-792

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…Black hole algorithm (BHA) is inspired by this phenomenon and belongs to the swarm intelligence paradigm using adaptive strategies to search and optimize. Black hole algorithm can be defined as a sub-field of particle swarm optimization and they are inspired by physical laws, like gravitation search [64], intelligent water drop [65], or simulated annealing [66]. Other types of heuristic optimization algorithms are inspired by living bodies, like bacterial algorithm [67], bat algorithm [68], artificial bee colony algorithm [69], firefly algorithm [70], and ant colony algorithm [71].…”

Section: Black Hole Algorithm-based Optimizationmentioning

confidence: 99%

Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions

Bányai

2018

Energies

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Energy efficiency and environmental issues have been largely neglected in logistics. In a traditional supply chain, the objective of improving energy efficiency is targeted at the level of single parts of the value making chain. Industry 4.0 technologies make it possible to build hyperconnected logistic solutions, where the objective of decreasing energy consumption and economic footprint is targeted at the global level. The problems of energy efficiency are especially relevant in first mile and last mile delivery logistics, where deliveries are composed of individual orders and each order must be picked up and delivered at different locations. Within the frame of this paper, the author describes a real-time scheduling optimization model focusing on energy efficiency of the operation. After a systematic literature review, this paper introduces a mathematical model of last mile delivery problems including scheduling and assignment problems. The objective of the model is to determine the optimal assignment and scheduling for each order so as to minimize energy consumption, which allows to improve energy efficiency. Next, a black hole optimization-based heuristic is described, whose performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to increase energy efficiency in last mile logistics.

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Section: Black Hole Algorithm-based Optimizationmentioning

confidence: 99%

Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions

Bányai

2018

Energies

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“…TLBO outperformed many other rigorous optimization techniques due to the absence of any tunable gains and weighting factors. Recently, many different evolutionary algorithms have been used, which include the gravitational-search algorithm [18]− [21], grey wolf optimizer [22]− [24], and artificial bee colony algorithm [25]− [27]. This paper optimizes the HDV speed controller's proportional-integral (PI) gains (KP and KI) to achieve better accurate reference tracking performance under variable drive cycles.…”

Section: Introductionmentioning

confidence: 99%

Optimal proportional-integral speed control for closed-loop engine timing system

Albatran,

Harasis

2024

IJEECS

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In internal combustion engines, adjusting the air-fuel ratio is essential to control the speed and minimize the burnt fuel. The throttle opening is the actuator to control the air-fuel. A better design for the used/conventional controller can give a better response without additional cost. In this work, the proposed controller gains of the proportional-integral (PI) controller are tuned to enhance the speed in constant and variable drive cycle modes. The tuning process is conducted based on two of the most efficient performance indices used in this field. The performance indices are integral absolute error (IAE) and integral time absolute error (ITAE). The optimization problem is solved using three reliable stochastic optimization algorithms to ensure mature convergence of the solutions, to avoid local optima solutions, and to ensure effective shrinking of the search space. The optimization algorithms are teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), and genetic algorithm (GA). Different simulations are conducted to validate the results. The results are compared with conventional tuning methods regarding the system's time response.

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“…The results showed an enhancement to the overall system stability and performance. Based on gravitation laws, a new optimization method was used to tune the parameters of three PI controllers, which were used to design a more stable system, if subjected to large disturbances, in comparison with common genetic algorithm (GA) and PSO optimization methods [15].…”

Section: Introductionmentioning

confidence: 99%

Cuckoo search algorithm based for tunning both PI and FOPID controllers for the DFIG-Wind energy conversion system

Al‐Gabalawy

Hosny

Hussien

2020

IJECE

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Wind Energy has received great attention in this century. It influences the new power systems, adding new challenges to the power system expansion problem. Nowadays, double feed induction generator (DFIG) wind turbines are used majorly in wind farms, due to their advantages over other types. Therefore, the analysis of the system using this type has become very important. In this paper, a wind turbine modelling was introduced with suggested controllers, in order to enhance the system response, with respect to both pitch control and maximum output power. Cuckoo search algorithm (CSA), a meta-heuristic optimization technique, was implemented to determine the gains of a proportional-integral (PI) controller and fractional order proportional-integral-derivative (FOPID) controller to optimize the system, which considered three control loops: pitch, rotor-side converter, and grid-side converter control loop. Simulation results were determined using MATLAB/Simulink. The comparative analysis of the results showed that the PI Controller gave the simplest and the best response in case of the pitch and rotor-side control loops while the FOPID was the best when applied to the grid-side control loop. Based on the results and discussion, a suggestion of using a compination of each controller was introduced.

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Gravitational-Search Algorithm for Optimal Controllers Design of Doubly-fed Induction Generator

Cited by 6 publications

References 27 publications

Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions

Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions

Optimal proportional-integral speed control for closed-loop engine timing system

Cuckoo search algorithm based for tunning both PI and FOPID controllers for the DFIG-Wind energy conversion system

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