Robust hybrid learning approach for adaptive neuro-fuzzy inference systems

Nik-Khorasani, Ali; Mehrizi, Ali; Sadoghi-Yazdi, Hadi

doi:10.1016/j.fss.2024.108890

Cited by 3 publications

References 32 publications

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RETRACTED: Enhancing the low-voltage ride-through capability of a wind turbine double-fed induction generator using hybrid model contains an adaptive neurofuzzy inference system controller and a crowbar circuit.

2024

Preprint

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The focal target of this paper is to increase the LVRT of the DFIG driven by WT. This task is realized, firstly, by inserting a crowbar circuit (CB) into rotor circuit (RC), in case of voltage dip (VD), to guard it from high current and high voltage that could result from the transient flux produced by the VD. In case of no CB circuit, the system may be vulnerable to instability. Secondly, a proposed ANFIS controller is proposed to control both the rotor side converter (RSC) and the grid side converter (GSC). While the RSC controller regulates the electromagnetic torque, the GSC controller control both the real power (RP) and reactive power (QP) supplied to the system during normal operation conditions beside with it keeps the value of the dc link voltage (DCLV) fixed. To enhance the LVRT, the GSC is controlled such that it injects the necessary reactive power required during severe voltage dips to improve the LVRT competency of the DFIG. From the results, it is obvious that the hybrid ANFIS and crowbar technique provides an improved LVRT response in challenging operating conditions.

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RETRACTED: Enhancing the low-voltage ride-through capability of a wind turbine double-fed induction generator using hybrid model contains an adaptive neurofuzzy inference system controller and a crowbar circuit.

2024

Preprint

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RETRACTED: Enhancing the low-voltage ride-through capability of a wind turbine double- fed induction generator using ANFIS controller and a crowbar circuit

Abdelaal

2024

Preprint

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The focal target of this paper is to increase the LVRT of the DFIG driven by WT. This task is realized, firstly, by inserting a crowbar circuit (CB) into rotor circuit (RC), in case of voltage dip (VD), to guard it from high current and high voltage that could result from the transient flux produced by the VD. In case of no CB circuit, the system may be vulnerable to instability. Secondly, a proposed ANFIS controller is proposed to control both the rotor side converter (RSC) and the grid side converter (GSC). While the RSC controller regulates the electromagnetic torque, the GSC controller control both the real power (RP) and reactive power (QP) supplied to the system during normal operation conditions beside with it keeps the value of the dc link voltage (DCLV) fixed. To enhance the LVRT, the GSC is controlled such that it injects the necessary reactive power required during severe voltage dips to improve the LVRT competency of the DFIG. From the results, it is obvious that the hybrid ANFIS and CB circuit provides an improved LVRT response in challenging operating conditions.

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Robust Harmonic Fuzzy Partition Local Information C-Means Clustering for Image Segmentation

Wu,

Zhou

2024

Symmetry

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Considering the shortcomings of Ruspini partition-based fuzzy clustering in revealing the intrinsic correlation between different classes, a series of harmonic fuzzy local information C-means clustering for noisy image segmentation are proposed. Firstly, aiming at the shortage of Zadeh’s fuzzy sets, a new concept of generalized harmonic fuzzy sets is originally introduced and the corresponding harmonic fuzzy partition is further defined. Then, based on the concept of symmetric harmonic partition, a new harmonic fuzzy local information C-means clustering (HLICM) is proposed and the local convergence of the algorithm is rigorously proved using Zangwill’s theorem. Finally, inspired by the improved fuzzy local information C-means clustering (IFLICM) and kernel-based weighted fuzzy local information C-means clustering (KWFLICM), two enhanced robust HLICM algorithms are constructed to further improve the ability of the algorithm to suppress noise. Compared with existing state-of-the-art robust fuzzy clustering-related algorithms, it has been confirmed that the two proposed algorithms have significant competitiveness and superiority.

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Robust hybrid learning approach for adaptive neuro-fuzzy inference systems

Cited by 3 publications

References 32 publications

RETRACTED: Enhancing the low-voltage ride-through capability of a wind turbine double-fed induction generator using hybrid model contains an adaptive neurofuzzy inference system controller and a crowbar circuit.

RETRACTED: Enhancing the low-voltage ride-through capability of a wind turbine double-fed induction generator using hybrid model contains an adaptive neurofuzzy inference system controller and a crowbar circuit.

RETRACTED: Enhancing the low-voltage ride-through capability of a wind turbine double- fed induction generator using ANFIS controller and a crowbar circuit

Robust Harmonic Fuzzy Partition Local Information C-Means Clustering for Image Segmentation

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