Adaptive Super-Twisting Sliding Mode Control of Active Power Filter Using Interval Type-2-Fuzzy Neural Networks

Wang, Jiacheng; Fang, Yunmei; Fei, Juntao

doi:10.3390/math11122785

Cited by 5 publications

(5 citation statements)

References 42 publications

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“…The authors in [4,5] explored the power electronic converters and supraharmonics and [17] confirmed the effect of higher harmonic frequencies as the reduction in lifespan of power system devices. The authors in [8] successfully modeled the emissions from supraharmonic devices from the parallel operation of two converters and confirmed that it can be predicted by linear models.…”

Section: Introductionmentioning

confidence: 67%

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Investigations into Higher-Frequency Hysteresis Current Controller for Supraharmonic Hybrid Active Filters

Koduah,

Svinkunas,

Bandza

et al. 2024

Applied Sciences

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Hysteresis current controllers (HCCs) are extensively used in low-frequency power inverters and active power filters. Very little is known about their strengths and weaknesses when applied at supraharmonic frequencies. The major concern regarding the use of HCCs is their variable and uncontrollable switching frequencies. This results in difficulties in filter design, high switching losses, and the possibility of resonance conditions with power system elements. In this article, investigations are conducted on the application of HCCs in a hybrid filter for a 6 kHz matrix converter (MC)-coupled supraharmonic load. The effects of the MC-coupled load and VSC switches on the HCC’s switching frequency are analyzed. A novel mathematical model for obtaining a fixed-frequency HCC is presented. The model is verified in open- and closed-loop HCC control configurations for a three-phase hybrid active power filter (HAPF). Fast Fourier transform (FFT) analysis of the supply current after the implementation of the novel HCC-HAPF showed 2.31% of the 50 Hz fundamental. The analysis is verified in a MATLAB/Simulink environment.

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Section: Introductionmentioning

confidence: 67%

“…Active power filters are preferred for harmonic compensation due to their compatibility and smaller weight and size [7][8][9][10]. Traditional passive filters may not be practical in applications where size and weight are of much concern.…”

Section: Introductionmentioning

confidence: 99%

Investigations into Higher-Frequency Hysteresis Current Controller for Supraharmonic Hybrid Active Filters

Koduah,

Svinkunas,

Bandza

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Therefore, designing controllers using nominal values is bound to be influenced by unknown uncertainties. In this article, unknown uncertainty is summarized as a term in system dynamics [35,36].…”

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

“…(4) Layer 4-hidden layer: The mapping of input signals in this layer is the key to the performance of the RBFNN [35]. The output of this layer can be expressed as: 14)…”

Section: Output Layermentioning

confidence: 99%

Self-Evolving Chebyshev Radial Basis Function Neural Complementary Sliding Mode Control

Zhang

Fei

2023

Mathematics

Self Cite

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A novel intelligent complementary sliding mode control (ICSMC) method is proposed for nonlinear systems with unknown uncertainties in this paper. A self-evolving Chebyshev radial basis function neural network (RBFNN) (SECRBFNN) with self-learning parameters and structure is proposed and combined with complementary sliding mode control (CSMC). CSMC not only has the advantages of the strong robustness of traditional SMC but also has certain advantages in reducing chattering and control accuracy. The SECRBFNN, which combines the advantages of the Chebyshev network (CN) and an RBFNN, is used to estimate unknown uncertainties in nonlinear systems. Meanwhile, a node self-evolution mechanism is proposed to avoid redundancy in the number of neurons. Eventually, the detailed simulation results demonstrate the feasibility and superiority of the proposed method.

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“…While the theoretical validation of the method has been conducted in previous studies, the feasibility and superiority of the algorithm in simulations are primary concerns. Hence, the simulation environment is an ideal experimental setting, allowing for the selection of high sampling frequencies to achieve rapid data acquisition [28]. However, in practical applications, constraints on the computational capabilities of control devices often prevent the realization of the ideal sampling frequencies observed in simulations.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Interval Type-2 Fuzzy Neural Super-Twisting Control Applied to Single-Phase Active Power Filters

Wang,

Li,

Fei

2024

Applied Sciences

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This research introduces an improved control strategy for an active power filter (APF) system. It utilizes an adaptive super-twisting sliding mode control (STSMC) scheme. The proposed approach integrates an interval type-2 fuzzy neural network with a self-feedback recursive structure (IT2FNN-SFR) to enhance the overall performance of the APF system. The IT2FNN with STSMC proposed here consists of two components, with one being IT2FNN-SFR, which demonstrates robustness for uncertain systems and the ability to utilize historical information. The IT2FNN-SFR estimator is used to approximate the unknown nonlinear function within the APF. Simultaneously, the STSMC component is integrated to reduce system chattering, improving control precision and overall system performance. STSMC combines the robustness and simplicity of traditional sliding mode control, effectively addressing the chattering problem. To mitigate inaccuracies and complexities associated with manual parameter setting, an adaptive law of sliding mode gain is formulated to achieve optimal gain solutions. This adaptive law is designed within the STSMC framework, facilitating parameter optimization. Experimental validation is conducted to verify the harmonic suppression capability of the control strategy. The THD corresponding to the designed control algorithm is 4.16%, which is improved by 1.24% and 0.55% compared to ASMC and STSMC, respectively, which is below the international standard requirement of 5%. Similarly, the designed controller also demonstrates advantages in dynamic performance: when the load decreases, it is 4.72%, outperforming ASMC and STSMC by 1.15% and 0.38%, respectively; when the load increases, it is 3.87%, surpassing ASMC and STSMC by 1.07% and 0.36%, respectively.

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Adaptive Super-Twisting Sliding Mode Control of Active Power Filter Using Interval Type-2-Fuzzy Neural Networks

Cited by 5 publications

References 42 publications

Investigations into Higher-Frequency Hysteresis Current Controller for Supraharmonic Hybrid Active Filters

Investigations into Higher-Frequency Hysteresis Current Controller for Supraharmonic Hybrid Active Filters

Self-Evolving Chebyshev Radial Basis Function Neural Complementary Sliding Mode Control

Evaluation of Interval Type-2 Fuzzy Neural Super-Twisting Control Applied to Single-Phase Active Power Filters

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