Overall fuzzy logic control strategy of direct driven PMSG wind turbine connected to grid

Fannakh, Mhamed; Elhafyani, Mohamed Larbi; Zouggar, Smail; Zahboune, Hassan

doi:10.11591/ijece.v11i6.pp5515-5529

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…Both the signals are given input to the 'Fuzzy Logic Controller with Ruleviewer' block which generates the output signal 'o'. A 'saturation' block is used to limit the output value in specific range for stability of the system [20]. The rule viewer with respect to the input signal values is shown in figure 6.…”

Section: Flc Modelingmentioning

confidence: 99%

A fuzzy integrated non-linear backstepping control of a grid connected PMSG wind farm

Banothu.Balasubramanyam

2024

jes

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In this paper a grid connected PMSG wind farm is controlled by non-linear backstepping control. The PMSG power is delivered to grid through two back-to-back connected IGBT based VSCs. The VSCs are controlled by individual Sin PWM techniques for which the reference signals are generated non-linear backstepping controller. A capacitor is connected at the common DC link of the back-to-back VSCs for voltage stability. The backstepping controller designed for the control of PMSG has the capability to extract maximum power from the wind farm. The machine side connected VSC operates in synchronization to the rotor angle and the grid side connected VSC operates in synchronization with the grid voltages. The DC voltage regulator of the grid side converter control should be optimum for better stability of the system. Therefore, the conventional PI controller of the voltage regulator is replaced by a 49-rule base FLC which reduces the ripple and settling time of the DC link voltage. A 2MG PMSG wind farm is considered for performance analysis with PI and FLC in the non-linear backstepping control structure. The comparative analysis is carried out in MATLAB software with Simulink block sets utilized to design the wind farm connected to grid. The graphs are compared to determine the performance of the controller with different operating conditions.

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Section: Flc Modelingmentioning

confidence: 99%

A fuzzy integrated non-linear backstepping control of a grid connected PMSG wind farm

Banothu.Balasubramanyam

2024

jes

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“…In order to complete the DQ transformation, it is necessary to subtract θm from θS in order to get the angle θr. The torque expression can be made simpler by employing the stator flux orientation frame in the fashion shown in (25):…”

Section: Ac Voltage (mentioning

confidence: 99%

Limits of reactive power compensation of a doubly fed induction generator based wind turbine system

2023

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The doubly fed induction generator (DFIG) systems feature a significant amount of free power capacity that may be used for reactive power adjustment when they are put into practical use. This change, which is occasionally overlooked, is a significant one. Using DFIG systems for wind turbines (WT), this paper explored strategies for reducing and using reactive power. In order to investigate the power characteristic and how it is regulated in DFIG systems, a mathematical model for the steady-state performance of DFIG WT has been developed and presented. Here is a detailed derivation of the limiting range of DFIG's reactive power capacity as well as the physical constraints on reactive power output. The distribution of the DFIG WT at a distribution network's end is demonstrated by a simulation example. Within this simulation, reactive power management strategy, load fluctuation, and the change in wind speed are all taken into consideration. Due to the possibility of a rise in the voltage at the access point, can concluded that both acceptable and efficient to use DFIG WT's reactive power capabilities as an additional continuous reactive power source for effectiveness.

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“…Therefore, these are called interval-valued fuzzy sets or type-2 fuzzy sets. These fuzzy sets can be expressed as (20):…”

Section: Dynamic Fuzzification Process For the Fuzzy Antecedent And C...mentioning

confidence: 99%

Real-Time Metaheuristic Algorithm for Dynamic Fuzzification, De-Fuzzification and Fuzzy Reasoning Processes

2022

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This paper presents a systematic approach to designing a dynamic metaheuristic fuzzy logic controller (FLC) to control a piece of non-linear plant. The developed controller is a multiple-input–multiple-output (MIMO) system. However, with the proposed control mechanism is possible to adapt it to single-input–single-output (SISO) systems as well. During real-time operation, the dynamic behavior of the proposed fuzzy controller is influenced by a metaheuristic particle swarm optimization (PSO) mechanism. Nevertheless, to analyze the performance of the developed dynamic metaheuristic FLC as a piece of non-linear plant, a 1 kW four-wheel independent-drive electric rover is controlled under different road constraints. The test results show that the proposed dynamic metaheuristic FLC maintains the wheel slip ratio of all four wheels to less than 0.35 and a top recorded translational speed of 90 km/h is maintained for a fixed orientation.

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Overall fuzzy logic control strategy of direct driven PMSG wind turbine connected to grid

Cited by 6 publications

References 26 publications

A fuzzy integrated non-linear backstepping control of a grid connected PMSG wind farm

A fuzzy integrated non-linear backstepping control of a grid connected PMSG wind farm

Limits of reactive power compensation of a doubly fed induction generator based wind turbine system

Real-Time Metaheuristic Algorithm for Dynamic Fuzzification, De-Fuzzification and Fuzzy Reasoning Processes

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