Double Fed Induction Generator Control Design Based on a Fuzzy Logic Controller for an Oscillating Water Column System

Napole, Cristian; Barambones, Óscar; Derbeli, Mohamed; Cortajarena, José Antonio; Calvo, Isidro; Alkorta, Patxi; Fernández-Bustamante, Pablo

doi:10.3390/en14123499

Cited by 9 publications

(8 citation statements)

References 41 publications

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“…The final stage is stage five, where the signals are processed. 40 The control signals generate the switching pulses, which can control pulse width modulation (PWM). The block diagram of STATCOM with fuzzy logic controllers is shown in Figure 3C.…”

Section: Fuzzy Logic Controlmentioning

confidence: 99%

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Voltage stability improvement of an Egyptian power grid‐based wind energy system using STATCOM

et al. 2022

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The increase in electricity demand places its focus on renewable energies as sustainable energy resources. Wind energy is one of the most important green energy sources. The doubly fed induction generator (DFIG)-based wind farm has now gained prominence due to its many advantages, such as variable speed operation and autonomous control of active and reactive power. When the DFIG stator windings are directly connected to the power grid, when a grid fault occurs, some unwanted high current may be produced in the rotor windings, and the protection system will prevent the rotor side converter (RSC) from operating. Therefore, voltage stability is a significant factor in maintaining the DFIG-based wind farm in operation during grid faults and disturbances. This paper applies a static synchronous compensator (STATCOM) to restore the voltage levels of the Egyptian power grid connected to Al Zafarana-5th stage wind farm, which is made of 100 Gamesa G52/850 kW DFIG machines. In this paper, the STATCOM is controlled by a proportional integral (PI) and is compared with a STATCOM controlled by fuzzy logic control (FLC). For simulation, the MATLAB/SIMULINK environment is used. Moreover, the simulation results show that STATCOM devices with fuzzy logic controllers improve the effects of grid faults and disturbances such as a single line to ground fault, a line to line fault, voltage sag, and voltage swell as compared with STATCOM with PI controllers. Also, STATCOM devices based on FLC improve the stability and power quality of the system and the power system restoration procedures for the existing and futureplanned wind farms.

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Section: Fuzzy Logic Controlmentioning

confidence: 99%

“…Different defuzzification methods like the centroid method, the bisector of area, mean of maximum, smallest of maximum, and largest of maximum. The final stage is stage five, where the signals are processed 40 . The control signals generate the switching pulses, which can control pulse width modulation (PWM).…”

Section: Fuzzy Logic Controlmentioning

confidence: 99%

Voltage stability improvement of an Egyptian power grid‐based wind energy system using STATCOM

et al. 2022

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“…The change in the air flow speed forces the turbine blades to rotate. The turbine is linked to a DFIG whose stator is directly connected to the grid network, and its rotor is connected to the grid through a rotor-side converter and gridside converter [3]. Oscillating water column turbines are ideal for a variety of generators, including permanent magnet synchronous generators (PMSGs) and double-fed induction generators (DFIGs).…”

Section: Introductionmentioning

confidence: 99%

“…Among the many benefits associate with PID controllers are their broad stability range, robustness, straightforward design, and the ease of choosing of gains. The sensitivity of the PID controllers to the nonlinearity and variable changes in the system causes the system to become unstable [3].…”

Section: Introductionmentioning

confidence: 99%

The Validation and Implementation of the Second-Order Adaptive Fuzzy Logic Controller of a Double-Fed Induction Generator in an Oscillating Water Column

Elnaghi,

Abelwhab,

Mohammed

et al. 2024

Electronics

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This article presents a second-order adaptive fuzzy logic controller (SO-AFLC) to improve the performance of a grid-connected double-fed induction generator (DFIG) in an oscillating water column power plant (OWCPP). The proposed SO-AFLC was used to improve the maximum power point tracking (MPPT), DC link voltage stability, and reactive power tracking for the DFIG oscillating water column power plant. The SO-AFLC reduces oscillations, overshooting, and mean square error. The SO-AFLC improved the mean square error by 40.4% in comparison to the adaptive fuzzy logic controller (AFLC) and by 84.9% in comparison to the proportional–integral differential controllers (PIDs). To validate the simulation results, an experimental investigation was performed on the Dspace DS 1104 control board. The SO-AFLC shows a faster response time, reduced undershooting, lower peak overshooting, and very low steady-state error in terms of DC link voltage, rotor speed, and maximum power point tracking. Moreover, the integral absolute error (IAE) index of the oscillating water column turbine was calculated. This index is meant to evaluate the SO-AFLC’s feasibility against the PID and AFLC under the same wave conditions.

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“…Napole et al design a fuzzy logic controller and airflow reference generator for oscillating water column systems aiming for the turbine speed to be regulated in a more effective way and efficiency of the system at whole to be improved [69]. The simulation results prove the enhanced system performance and stalling avoidance.…”

mentioning

confidence: 99%

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

2021

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The paper presents an analysis and summary of the current research state concerning the application of machine learning and fuzzy logic for solving problems in electronics. The investigated domain is conceptualized with aim the achievements, trending topics and future research directions to be outlined. The applied research methodology includes a bibliographic approach in combination with a detailed examination of 66 selected papers. The findings reveal the gradually increasing interest over the last 10 years in the machine learning and fuzzy logic techniques for modeling, implementing and improving different hardware-based intelligent systems.

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Double Fed Induction Generator Control Design Based on a Fuzzy Logic Controller for an Oscillating Water Column System

Cited by 9 publications

References 41 publications

Voltage stability improvement of an Egyptian power grid‐based wind energy system using STATCOM

Voltage stability improvement of an Egyptian power grid‐based wind energy system using STATCOM

The Validation and Implementation of the Second-Order Adaptive Fuzzy Logic Controller of a Double-Fed Induction Generator in an Oscillating Water Column

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

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