Operation and control of HVDC stations using continuous mixed<i>p</i>‐norm‐based adaptive fuzzy technique

Muyeen, S. M.; Hasanien, Hany M.

doi:10.1049/iet-gtd.2016.1690

Cited by 20 publications

(5 citation statements)

References 39 publications

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“…In [24], a new adaptive control based on set-membership engine algorithm is provided with improved performance than other techniques. The continuous p-norm mixed technique is presented as a new adaptive control of the VS, which rapidly updates the fuzzy inference system in [25]. In [26], the improved multiband-structured subband adaptive filter (IMSAF) algorithm-based self-tuned PI controller is employed to adjust the interface voltage source converters through a cascaded control structure.…”

Section: B Research Gap and Motivationmentioning

confidence: 99%

LMSRE-Based Adaptive PI Controller for Enhancing the Performance of an Autonomous Operation of Microgrids

et al. 2021

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Microgrids take a large part in power networks thanks to their operational and economic benefits. This research introduces a novel implementation of an adaptive proportional plus integral (PI) controller to boost the autonomous microgrid operation efficiency. The least mean and square roots of the exponential algorithm are utilized in the adaptive PI control strategy. The multi-objective function for both sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms is obtained by The Response Surface Methodology. The system is evaluated under different environments, which are stated as follows: 1) disconnect the system from the grid (islanding), 2) autonomous system exposure to load variability, and 3) autonomous system exposure to a symmetrical fault. The proposed practicality of the control plan is shown by the data of the simulation, which is extracted from PSCAD/EMTDC software. The strength of the suggested adaptive control is confirmed through matching its results with those obtained using the SFO and PSO based optimal PI controllers. INDEX TERMS microgrid; optimization; power systems; renewable energy. Ahmed Al-Durra (S'07-M'10-SM'14) received his PhD in ECE from Ohio State University in 2010.He is a Professor in the EECS Department at Khalifa University, UAE. His research interests are applications of control and estimation theory on power systems stability, micro and smart grids, renewable energy systems and integration, and process control. He has one US patent, one edited book, 12 book chapters, and over 210 scientific articles in top-tier journals and refereed international conference proceedings. He has supervised/co-supervised

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Section: B Research Gap and Motivationmentioning

confidence: 99%

LMSRE-Based Adaptive PI Controller for Enhancing the Performance of an Autonomous Operation of Microgrids

et al. 2021

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“…Then, (25)(26)(27)(28) are linearised in state-space system form, where the matrix A which corresponds to the poles of the closed-loop control can be obtained.…”

Section: Creation Of Objective Functionmentioning

confidence: 99%

“…Therefore, adaptive FLCs have been presented to adapt the input–output MFs. In [28], CMPN algorithm‐based adaptive FLCs are applied to enhance the performance of high‐voltage direct current system connected to the wind farm. The artificial neural network (ANN) controller has shown promise in dealing with the characteristics of system's non‐linearity.…”

Section: Introductionmentioning

confidence: 99%

Hybrid ANFIS‐GA‐based control scheme for performance enhancement of a grid‐connected wind generator

Soliman

Hasanien

Azazi

et al. 2018

IET Renewable Power Generation

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This study presents a novel application of a hybrid adaptive neuro-fuzzy inference system (ANFIS)-genetic algorithm (GA)-based control scheme to enhance the performance of a variable-speed wind energy conversion system. The variablespeed wind turbine drives a permanent-magnet synchronous generator, which is connected to the power grid through a frequency converter. A cascaded ANFIS-GA controller is introduced to control both of the generator-side converter and the gridside inverter. ANFIS is a non-linear, adaptive, and robustness controller, which integrates the merits of the artificial neural network and the FIS. A GA-based learning design procedure is proposed to identify the ANFIS parameters. Detailed modelling of the system under investigation and its control strategies are demonstrated. For achieving realistic responses, real wind speed data extracted from Zaafarana wind farm, Egypt, are considered in the analyses. The effectiveness of the ANFIS-GA controller is compared with that obtained using optimised proportional-integral controllers by the novel grey wolf optimiser algorithm taking into consideration severe grid disturbances. The validity of the ANFIS-GA control scheme is verified by the extensive simulation analyses, which are performed using MATLAB/Simulink environment. With the ANFIS-GA controller, the dynamic and transient stability of grid-connected wind generator systems can be further enhanced.

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“…Here, the alternating voltage generated by the PMSG was converted into the direct voltage by the generator-side converter and then connected to the DC bus. Apart from the high power transfer capability and cost-effectiveness in a longer transmission system, HVDC is also considered as an environmentally friendly technology owing to its low corona losses and ozone generation rates [22]. Thus, the power is delivered and converged in the cables through the mode of high voltage direct current (HVDC) in this paper.…”

Section: Configuration Of the Wind Farmmentioning

confidence: 99%

Output Power Smoothing Control for a Wind Farm Based on the Allocation of Wind Turbines

et al. 2018

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This paper presents a new output power smoothing control strategy for a wind farm based on the allocation of wind turbines. The wind turbines in the wind farm are divided into control wind turbines (CWT) and power wind turbines (PWT), separately. The PWTs are expected to output as much power as possible and a maximum power point tracking (MPPT) control strategy combining the rotor inertia based power smoothing method is adopted. The CWTs are in charge of the output power smoothing for the whole wind farm by giving the calculated appropriate power. The battery energy storage system (BESS) with small capacity is installed to be the support and its charge and discharge times are greatly reduced comparing with the traditional ESSs based power smoothing strategies. The simulation model of the permanent magnet synchronous generators (PMSG) based wind farm by considering the wake effect is built in Matlab/Simulink to test the proposed power smoothing method. Three different working modes of the wind farm are given in the simulation and the simulation results verify the effectiveness of the proposed power smoothing control strategy.

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Operation and control of HVDC stations using continuous mixedp‐norm‐based adaptive fuzzy technique

Cited by 20 publications

References 39 publications

LMSRE-Based Adaptive PI Controller for Enhancing the Performance of an Autonomous Operation of Microgrids

LMSRE-Based Adaptive PI Controller for Enhancing the Performance of an Autonomous Operation of Microgrids

Hybrid ANFIS‐GA‐based control scheme for performance enhancement of a grid‐connected wind generator

Output Power Smoothing Control for a Wind Farm Based on the Allocation of Wind Turbines

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