Increasing efficiency of the switched reluctance generator using parametric regression and optimization methods

Reis, Marcio R. C.; Calixto, Wesley P.; Araujo, Wanderson R. H.; Matias, Calebe A.

doi:10.1109/epe.2017.7967297

Cited by 6 publications

(8 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An alternative for engineering problems with difficult analytical formulation is the use of metaheuristics, such as the genetic algorithm (GA) and the particle swarm optimization (PSO) [53], or learning methods, such as artificial neural networks (ANN). As a result, several paper have made use of intelligent algorithms for the firing angle optimization of SRGs [40], [54]- [65]. In [55], the PSO algorithm is used to optimize the firing angles of an SRG in order to maximize power output and efficiency.…”

Section: A Optimizationmentioning

confidence: 99%

“…Other optimization algorithms such as differential evolution [64] and parametric regression [65] have also been reported in SRG-related publications. It should be noted that although intelligent algorithms present improved performance and reduced computational effort when compared to exhaustive search approaches, adequate algorithm parameter selection must be ensured in order to guarantee the convergence to global minima.…”

Section: A Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Switched Reluctance Generators in Wind Power Applications: Fundamentals, Control and Future Trends

et al. 2022

View full text Add to dashboard Cite

With the ever growing environmental concerns, renewable energy sources emerge as a promise of clean and abundant energy, enabling long-term sustainable development. In this context, wind power gained significant interest due to its relative low cost and availability. Switched reluctance generators (SRGs) are suitable candidates for wind energy conversion systems, as they present a simple structure, robustness, a wide range of speed and are capable of operating in harsh environments. The machine, however, poses challenges such as high torque ripple, acoustic noise production and highly nonlinear behavior. Nonetheless, with the use of adequate control strategies, high dynamic performance SRG-based wind energy conversion systems can be achieved. As a result, this article presents a state of the art review of SRGs in wind power applications. First, the fundamentals of the SRG are presented. Next, two categories of firing angle control are reviewed: optimization and closed-loop control. Then, voltage and power control strategies are discussed, being divided in model-independent and model-based approaches. After that, a review on grid-tied SRGbased wind energy conversion systems is carried out. The most common filter topologies as well as the employed control strategies are detailed. Lastly, an outline of the discussed topics is presented and future trends as well as suggestions for future investigation are listed.

show abstract

Section: A Optimizationmentioning

confidence: 99%

Section: A Optimizationmentioning

confidence: 99%

A Review on Switched Reluctance Generators in Wind Power Applications: Fundamentals, Control and Future Trends

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The asymmetric half-bridge converter (AHB) for a three-phase SRG shown in Figure 1 (b), mainly because it enables the machine to be driven both as a generator and as a motor. The windings of the stator are of a concentrated type and simple shape, the rotor has no winding, no magnets and low inertia [3,13]. The characteristics of the SRG depend on numerous features, mainly: machine structure (number of phases, number of stator and rotor poles, stator and rotor arcs), magnetization characteristic of the laminations, configuration of the converter and methodology of controller [13,14].…”

Section: Operation Of Three-phase Switched Reluctance Generatormentioning

confidence: 99%

“…The windings of the stator are of a concentrated type and simple shape, the rotor has no winding, no magnets and low inertia [3,13]. The characteristics of the SRG depend on numerous features, mainly: machine structure (number of phases, number of stator and rotor poles, stator and rotor arcs), magnetization characteristic of the laminations, configuration of the converter and methodology of controller [13,14]. Concentrated stator windings are divided into four diametrically symmetrical pairs linked in series to form…”

Section: Operation Of Three-phase Switched Reluctance Generatormentioning

confidence: 99%

“…As the following equation, the operating algorithm is based on the best local and global solution [18,19]. (13) where, at k iteration is the velocity of particle i, is the particle i updated velocity, is the weight and different inertia of particle i, C1 and C2 are constants of positive acceleration, at iteration k is current position of particle i, rand is between 0 and 1 random number, is the i th particle's best previous position, and is the best particle in the population among all particles. The new position can, therefore, be modified with the present position and updated velocity as [19,20] The constants positive acceleration C1 and C2 are set to 1.6.…”

Section: Closed Loop Operation Of Srgmentioning

confidence: 99%

See 1 more Smart Citation

Voltage control of switched reluctance generator using grasshopper optimization algorithm

Bahy

Nada

Elbanna

et al. 2020

IJPEDS

View full text Add to dashboard Cite

This paper presents a terminal voltage control approach of a Switched Reluctance Generator (SRG) based wind turbine generating systems. The control process is employed using a closed loop stimulated by the error between the reference voltage and the generator output voltage due to load and wind speed variation. This error feeds the tuned Proportional Integral controller (PI).Tuning of PI controller by conventional analysis methods is difficult by the existence of a significant non-linearity. A novel strategy method is presented here to determine optimum PI controller parameters of voltage control of SRG using Grasshopper Optimization Algorithm (GOA). This proposed approach is a simple and effective algorithm that is able to solve many optimization problems. The simplicity of algorithm provides high quality tuning of optimal PI controller parameters. The integral of time weighted squared error (ITSE) is used as the performance of the proposed GOA-PI controller. The effectiveness of the proposed strategy is tested with the three-phase 12/8 structure SRG. Outcomes indicate the supremacy of GOA over Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) in terms of control performance measures.

show abstract

Nonlinear simulation methodology for switched reluctance machine using induction profile found by parametric regression

Arajo

Reis

Calixto

et al. 2017

2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

View full text Add to dashboard Cite

Increasing efficiency of the switched reluctance generator using parametric regression and optimization methods

Cited by 6 publications

References 6 publications

A Review on Switched Reluctance Generators in Wind Power Applications: Fundamentals, Control and Future Trends

A Review on Switched Reluctance Generators in Wind Power Applications: Fundamentals, Control and Future Trends

Voltage control of switched reluctance generator using grasshopper optimization algorithm

Nonlinear simulation methodology for switched reluctance machine using induction profile found by parametric regression

Contact Info

Product

Resources

About