Design and real time implementation of adaptive neural-fuzzy inference system controller based unity single phase power factor converter

Benyamina, Abderrahmen; Moulahoum, Samir; Çolak, İlhami; Bayındır, Ramazan

doi:10.1016/j.epsr.2017.07.025

Cited by 29 publications

(12 citation statements)

References 30 publications

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“…The if–then rules of the Sugeno fuzzy system are given as follows 19 : where: ( e ) and ( de ) are the inputs to ANFIS; A i and B i are the antecedent membership functions; u i is the output; p i and q i represent the weighting factors for ith input and r i is the ith output bias. From Fig.…”

Section: Proposed Control Designmentioning

confidence: 99%

“…According to Fig. 4, the ANFIS controller is normalized by using scaling factors (K e , k de and k u ) to get an optimal performance as fallow: The if-then rules of the Sugeno fuzzy system are given as follows 19 :…”

Section: Anfis Based Dc-link Voltage Controllermentioning

confidence: 99%

“…Literature regarding active power factor correction has introduced many modern control theories focused on achieving high performance against various uncertainties and parameters variations. These control methods were employed to control the output voltage of the AC-DC rectifier, including sliding mode control (SMC) 6,14,15 , fuzzy logic 16,17 , fractional control 18 , and artificial intelligent control 19 . Sliding Mode Control, known for its simplicity and model-free implementation, requires high control efforts, potentially leading to excessive switching, increased losses, and component stress [20][21][22] .…”

mentioning

confidence: 99%

“…By fusing the human-like thinking ability of the FLC system and the learning capability of the artificial neural network, an adaptive neural-fuzzy inference system (ANFIS) is designed, which generates results more accurately regarding to other schemes that used only fuzzy system or neural network [28][29][30][31] . Moreover, the ANFIS offers more computing advantages by eliminating the construction of mathematical models, thereby decreasing the computational time needed and maintaining consistent performance in the presence of perturbations 19 . The ANFIS approach is implemented in this paper to enhance the performance of a single-phase PFC rectifier; hence, it ensures a controlled dc-link voltage regardless of the dynamic parameters' changes such as load and reference output voltage.…”

mentioning

confidence: 99%

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A dSPACE-based implementation of ANFIS and predictive current control for a single phase boost power factor corrector

Babes,

Latrèche,

Bouafassa

et al. 2024

Sci Rep

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This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating an adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The innovative control strategy addresses key challenges in power quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, the proposed system achieves precise input current shaping, resulting in a remarkably low total harmonic distortion (THD) of 3.5%, which is well below the IEEE-519 standard threshold of 5%. Moreover, the power factor reaches an outstanding 0.990, indicating highly efficient energy utilization and near-unity power factor operation. To validate the theoretical findings, a 500 W laboratory prototype was implemented using the dSPACE ds1104 digital controller. Steady-state analysis reveals sinusoidal input currents with minimal THD and a power factor approaching unity, thereby enhancing grid stability and energy efficiency. Transient response tests further demonstrate the system’s robustness against load and voltage fluctuations, maintaining output voltage stability within an 18 V overshoot and a 20 V undershoot during load changes, and achieving rapid response times as low as 0.2 s. Comparative evaluations against conventional methods underscore the superiority of the proposed control strategy in terms of both performance and implementation simplicity. By harnessing the strengths of ANFIS-based voltage regulation and predictive current control, this scheme offers a robust solution to power quality issues in AC/DC boost rectifiers, promising substantial energy savings and improved grid stability. The results affirm the potential of the proposed system to set new benchmarks in power factor correction technology.

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Section: Proposed Control Designmentioning

confidence: 99%

Section: Anfis Based Dc-link Voltage Controllermentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A dSPACE-based implementation of ANFIS and predictive current control for a single phase boost power factor corrector

Babes,

Latrèche,

Bouafassa

et al. 2024

Sci Rep

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show abstract

“…It is also worth mentioning the numerous methods combining fuzzy logic and ANNs in different ways. As an example, in [37], a combination of fuzzy and neural network techniques is used for improved DC voltage control by an APF, where a fuzzy logic controller extracts the training dataset for the neural network. In [38], an adaptive backstepping fuzzy neural network controller is designed to suppress the harmonics in a SAPF.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Loads Compensation Using a Shunt Active Power Filter Controlled by Feedforward Neural Networks

2021

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The shunt active power filter (SAPF) is a widely used tool for compensation of disturbances in three-phase electric power systems. A high number of control methods have been successfully developed, including strategies based on artificial neural networks. However, the typical feedforward neural network, the multilayer perceptron, which has provided effective solutions to many nonlinear problems, has not yet been employed with satisfactory performance in the implementation of the SAPF control for obtaining the reference currents. In order to prove the capabilities of this simple neural topology, this work describes a suitable strategy of use, based on the accurate estimation of the Fourier coefficients corresponding to the fundamental harmonic of any distorted voltage or current. An effective training method has been developed, consisting of the use of many distorted patterns. The new generation procedure uses random combinations of multiple harmonics, including the possible nominal frequency deviations occurring in real power systems. The design of the generation of reference signals through computations based on the Fourier coefficients is presented. The objectives were the harmonic mitigation and power factor correction. Practical cases were tested through simulation and also by using an experimental platform, showing the feasibility of the proposal.

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Jellyfish search algorithm-based optimum tuning of PI controller for a front-end converter in a DFIG-based wind energy conversion system

Rauth,

Kastha,

Bajpai

2024

Soft Comput

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Design and real time implementation of adaptive neural-fuzzy inference system controller based unity single phase power factor converter

Cited by 29 publications

References 30 publications

A dSPACE-based implementation of ANFIS and predictive current control for a single phase boost power factor corrector

A dSPACE-based implementation of ANFIS and predictive current control for a single phase boost power factor corrector

Nonlinear Loads Compensation Using a Shunt Active Power Filter Controlled by Feedforward Neural Networks

Jellyfish search algorithm-based optimum tuning of PI controller for a front-end converter in a DFIG-based wind energy conversion system

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