Recurrent wavelet neural network controller with improved particle swarm optimisation for induction generator system

Teng, Li-Tao; Lin, Faa-Jeng; Chiang, Hsuan-Ching; Lin, Jyun-Ru

doi:10.1049/iet-epa:20080038

Cited by 13 publications

(11 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By substituting u* into (7) to replace u, (7) can be written as ω ω m m k e = * + 1 (9) where the tracking error of rotor speed e m m = * − ω ω and k1 is a positive constant if k1 can be set such that the characteristic polynomial in (9) is strictly Hurwitz, meaning that all roots of the polynomial lie strictly in the open left-half complex plane. In other words, the speed error will converge to zero as time approaches infinity.…”

Section: Supervisory Controller Designmentioning

confidence: 99%

“…Therefore, model-free-based methods, such as those based on fuzzy control [4,5], fuzzy neural networks (NNs) [6], cerebellar model articulation controllers (CMAC) [7,8], and wavelet NNs [9], have been developed. Although PI control is the most widely utilized solution in industry, its parameters must be tuned to perform satisfactorily when a modification is made or a change occurs in the controlled system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of a Novel Self‐Tuned Adaptive Supervisory Cerebellar Model Articulation Controller for Induction Motor Drive

Wang¹,

Tseng²,

Tseng³

2012

Asian Journal of Control

View full text Add to dashboard Cite

This work presents a novel speed control scheme for an induction motor (IM) using an adaptive supervisory differential cerebellar model articulation controller (ASDCMAC). The ASDCMAC has a supervisory controller and an adaptive differential cerebellar model articulation controller (ADCMAC), and the ASDCMAC is utilized as the speed controller. The supervisory controller monitors the control process to keep speed tracking error within a predefined range, and the ADCMAC learns and approximates system dynamics. The connective weights of ADCMAC are adjusted online, according to adaptive rules derived in Lyapunov stability theory, to ensure system stability. The robustness of the proposed ASDCMAC against parameter variations and external load torque disturbances is verified via simulations and experiments, respectively. Three control schemes, the ASDCMAC, fuzzy control, and PI control, are investigated experimentally, and a performance index, root mean square error (RMSE), is utilized for each scheme. The experimental results demonstrate that the ASDCMAC outperforms the two other control schemes with external load torque variations.

show abstract

Section: Supervisory Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a Novel Self‐Tuned Adaptive Supervisory Cerebellar Model Articulation Controller for Induction Motor Drive

Wang¹,

Tseng²,

Tseng³

2012

Asian Journal of Control

View full text Add to dashboard Cite

show abstract

“…In (Saleh & Rahman, 2009) wavelets allow a new switching strategy to be developed in order to reduce the harmonic content of the output voltage in a ac/dc converter maintaining unity power factor. A three-phase induction generator (IG) system for stand-alone power systems is controlled by means of one ac/dc plus one dc/ac converter and applying a recurrent wavelet neural network (RWNN) controller with improved particle swarm optimization (IPSO) (Teng et al, 2009). The controllers in dc/ac converters can be optimized by applying wavelets, this is the case of (Mercorelli et al, 2004), where it is employed for optimization of the applied model predictive controller.…”

Section: Wavelet Analysis In Controllers For Power Convertersmentioning

confidence: 99%

Discrete Wavelet Transforms for Synchronization of Power Converters Connected to Electrical Grids

Pigazo¹,

Moreno²

2011

Discrete Wavelet Transforms - Theory and Applications

View full text Add to dashboard Cite

“…It was developed through simulation of a simplified social system and has been found to be robust in solving continuous nonlinear optimization problem in [25][26][27][28][29]. The PSO technique can generate a high quality solution within shorter calculation time and stable convergence characteristics than other stochastic methods in [30][31][32][33][34]. Much research is still in progress for proving the potential of the PSO in solving complex dynamical systems.…”

Section: Introductionmentioning

confidence: 99%

Wavelet–Neural–Network Control for Maximization of Energy Capture in Grid Connected Variable Speed Wind Driven Self- Excited Induction Generator System

El-Sousy¹,

Al-Asmari²

2013

Discrete Wavelet Transforms - A Compendium of New Approaches and Recent Applications

View full text Add to dashboard Cite

Discrete Wavelet Transforms -A Compendium of New Approaches and Recent Applications 164 necessary in [36][37][38]. In addition, for the research of wind energy conversion systems, the developments of wind turbine emulators are also necessary in [43,44]. However, the fuzzy logic controller, the sliding-mode controller, and the PI controller adopted in [40][41][42][43][44][45][46][47][48] may not guarantee the robustness when parameter variations or external disturbance occurred in the control system in practical applications due to the lack of online learning ability.This Chapter is organized as follows. Section 2 presents the variable speed wind generation system description. In this section the analysis of the wind turbine is carried out and the maximum power point tracking analysis is also introduced. In Section 3, the dynamic model of the self-excited induction generator is introduced to analyze all its characteristics. Section 4 provides the indirect field-orientation control (IFOC) dynamics for the IG (torque, slip angular frequency and voltage commands) which are derived from the dynamic model of SEIG. The d-q axes current control according to the IG rotor speed gives the maximum mechanical power from the wind turbine and the losses of the IG are minimized. In Section 5, the dynamic equations of the CRPWM converter in the synchronous reference frame are carried out based on the IFOC dynamics of the IG. The dynamic equations of the grid-side CRPWM voltage source inverter connected to the grid are given in Section 6. By using vector control technique, the currents of the CRPWM inverter are controlled with very high bandwidth. The vector control approach is used, with a reference frame oriented along the grid voltage vector position. This allows independent control of the active and reactive power. Section 7 considers the design procedures for the PID voltage controller of the IG-side CRPWM voltage source converter, the PID active power and reactive power controllers for the grid-side CRPWM inverter. In Section 8, an intelligent maximization hybrid control system based on the WNN with IPSO is proposed in order to control the DC-link voltage of the IG-side CRPWM voltage source converter, active power and reactive power of the grid-side CRPWM voltage source inverter effectively based on the MPPT from the wind driven SEIG system. Finally, to testify the design of the proposed hybrid control system and MPPT control scheme, the variable speed wind generation system is simulated in Section 9. The dynamic performance of the system has been studied under different wind velocities. The simulation results are provided to demonstrate the effectiveness of the proposed hybrid control for variable speed wind generation system. Variable Speed Wind Generation System DescriptionThe proposed wind generation system is shown in Figure 1. The wind turbine is coupled to the shaft of a SEIG. The output of the SEIG is connected to a double-sided CRPWM voltage source converter connected to a utility grid. Double-Sided Converter System...

show abstract

Recurrent wavelet neural network controller with improved particle swarm optimisation for induction generator system

Cited by 13 publications

References 23 publications

Development of a Novel Self‐Tuned Adaptive Supervisory Cerebellar Model Articulation Controller for Induction Motor Drive

Development of a Novel Self‐Tuned Adaptive Supervisory Cerebellar Model Articulation Controller for Induction Motor Drive

Discrete Wavelet Transforms for Synchronization of Power Converters Connected to Electrical Grids

Wavelet–Neural–Network Control for Maximization of Energy Capture in Grid Connected Variable Speed Wind Driven Self- Excited Induction Generator System

Contact Info

Product

Resources

About