Optimal tuning of PI controller using PSO optimization for indirect power control for DFIG based wind turbine with MPPT

Bekakra, Youcef; Attous, Djilani Ben

doi:10.1007/s13198-013-0150-0

Cited by 57 publications

(34 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, the idea of formulating the K p and K i gains' selection as an optimization problem is a promising solution. Such a control problem can be nonlinear, non-smooth or even non-convex and can be effectively solved thanks to advanced metaheuristics [10,11]. In this work, three PI controllers for the outer-loops at both RSC and GSC components are considered for the optimization process.…”

Section: Pi Controllers Tuning Problem Formulationmentioning

confidence: 99%

“…In recent years, an enormous variety of metaheuristics optimization algorithms has been applied to solve complex and hard problems in science and engineering fields. Bekakra and Attous [10] presented the Particle Swarm Optimization (PSO) algorithm to obtain the optimal gains of PI controllers for the indirect control of the active and reactive powers' loops of a DFIG. Integral Absolute Error (IAE), Integral Time-weighted Absolute Error (ITAE) and Integral Square Error (ISE) performance criteria were selected as objective functions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Direct Power Control Optimization for Doubly Fed Induction Generator Based Wind Turbine Systems

Alhato

2019

MCA

View full text Add to dashboard Cite

This study presents an intelligent metaheuristics-based design procedure for the Proportional-Integral (PI) controllers tuning in the direct power control scheme for 1.5 MW Doubly Fed Induction Generator (DFIG) based Wind Turbine (WT) systems. The PI controllers’ gains tuning is formulated as a constrained optimization problem under nonlinear and non-smooth operational constraints. Such a formulated tuning problem is efficiently solved by means of the proposed Thermal Exchange Optimization (TEO) algorithm. To evaluate the effectiveness of the introduced TEO metaheuristic, an empirical comparison study with the homologous particle swarm optimization, genetic algorithm, harmony search algorithm, water cycle algorithm, and grasshopper optimization algorithm is achieved. The proposed TEO algorithm is ensured to perform several desired operational characteristics of DFIG for the active/reactive power and DC-link voltage simultaneously. This is performed by solving a multi‐objective function optimization problem through a weighted‐sum approach. The proposed control strategy is investigated in MATLAB/environment and the results proved the capabilities of the proposed control system in tracking and control under different scenarios. Moreover, a statistical analysis using non-parametric Friedman and Bonferroni–Dunn’s tests demonstrates that the TEO algorithm gives very competitive results in solving global optimization problems in comparison to the other reported metaheuristic algorithms.

show abstract

Section: Pi Controllers Tuning Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Direct Power Control Optimization for Doubly Fed Induction Generator Based Wind Turbine Systems

Alhato

2019

MCA

View full text Add to dashboard Cite

show abstract

“…We use traditional tuning method and our proposal method to specify K P and K I of PI controller for the model and we obtain results are described in table 1. To compare performance of tradition and our proposal method we use step change of wind speed which is initially set to 5m/s, suddenly changes to 6m/s at 3s lasts until 6s and change to 7 m/s extends to 9s, corresponding to rotor current changes from -5.65A to -9.47A at 3s and to -15.30A at 6s [9]. Figure 5 and 6 show step response of system with PI controller tuned by traditional and CRO methods.…”

Section: Simulationmentioning

confidence: 99%

Tuning Pi Controller Bases on Chemical Reaction Optimization Algorithm

Cong¹

2019

AJECE

View full text Add to dashboard Cite

This paper we present Chemical Reaction Optimization (CRO) algorithm for determining optimal parameters of PI controller. The model of doubly fed induction generator (DFIG) is used as a plant in this paper. Tuning PI controller using traditional method such as Ziegler-Nichols (ZN) method usually produces large overshoot and Integral time absolute error, integral absolute error and integral square error performance indices. Therefore, recently researchers have applied random search approach such as genetic algorithm (GA) and particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO) to find optimal parameters for PI controller. Among modern heuristics algorithm, CRO was introduced in 2010, it combines features of both GA and Simulated Annealing (SA) to find global minimum in search space. CRO has been applied to solve successfully many optimization problems such as: Minimum transportation cost, resource-constrained project scheduling problem, channel assignment problem in wireless mesh networks, standard continuous benchmark functions, and so on. In this paper we present to apply CRO algorithm to search optimal parameters for PI controller. The comparison between tuning PI controller by CRO and traditional Ziegler-Nichols method is presented. The simulation results show the advantages of PI tuning using CRO compared to traditional method in terms of performance index and setting time.

show abstract

“…The generator reference speed using different types of dither signals. around in a multidimensional search space then during its flight each particle adjusts its position according to its own experience (P-best), and according to the experience of a neighboring particle (G-best), made the best position encountered by itself and its neighbor [21].…”

Section: Optimized Pi Control Using Particle Swarm Optimizationmentioning

confidence: 99%

Power Maximization and Control of Variable-Speed Wind Turbine System Using Extremum Seeking

Rafaat¹,

Hussein²

2018

JPEE

View full text Add to dashboard Cite

Maximizing the power capture is an important issue to the turbines that are installed in low wind speed area. In this paper, we focused on the modeling and control of variable speed wind turbine that is composed of two-mass drive train, a Squirrel Cage Induction Generator (SCIG), and voltage source converter control by Space Vector Pulse Width Modulation (SPVWM). To achieve Maximum Power Point Tracking (MPPT), the reference speed to the generator is searched via Extremum Seeking Control (ESC). ESC was designed for wind turbine region II operation based on dither-modulation scheme. ESC is a model-free method that has the ability to increase the captured power in real time under turbulent wind without any requirement for wind measurements. The controller is designed in two loops. In the outer loop, ESC is used to set a desired reference speed to PI controller to regulate the speed of the generator and extract the maximum electrical power. The inner control loop is based on Indirect Field Orientation Control (IFOC) to decouple the currents. Finally, Particle Swarm Optimization (PSO) is used to obtain the optimal PI parameters. Simulation and control of the system have been accomplished using MATLAB/Simulink 2014.

show abstract

Optimal tuning of PI controller using PSO optimization for indirect power control for DFIG based wind turbine with MPPT

Cited by 57 publications

References 12 publications

Direct Power Control Optimization for Doubly Fed Induction Generator Based Wind Turbine Systems

Direct Power Control Optimization for Doubly Fed Induction Generator Based Wind Turbine Systems

Tuning Pi Controller Bases on Chemical Reaction Optimization Algorithm

Power Maximization and Control of Variable-Speed Wind Turbine System Using Extremum Seeking

Contact Info

Product

Resources

About