Super‐Twisting Sliding Mode Control for Gearless PMSG‐Based Wind Turbine

In recent years, due to the several advantages of permanent magnet synchronous generator (PMSG), the number of wind farms utilizing this technology has been significantly grown. The determination of the failure mechanism in these devices is the major challenge which has been addressed in many studies. Particularly, response to grid code compliance by wind power in the voltage drop situation needs to be comprehensively analyzed. In this paper, a small signal model of a PMSG-based wind turbine for low voltage ride-through (LVRT) and suitable for stability and artificial intelligent studies is presented. Accordingly, the generator side converter controls the dc-link voltage, and the maximum power point tracking is performed by the grid side converter. Given the proposed model, the speed of the simulation for stability analysis studies can be significantly increased by intelligent methods. Furthermore, the simplified approach can be achieved for calculating the optimal coefficients of the proportionality-integral controller by intelligent methods in a short time. By simulating the proposed small-signal model and comparing it with the block-based simulation in MATLAB/SIMULINK software, the appropriate accuracy and efficiency of the proposed model are confirmed.

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“…Considering (13) and (16) and neglecting the stored power in L f , the following relation is obtained.…”

Section: Gsc Controllermentioning

confidence: 99%

“…For instance, in [3,[16][17][18][19], nonlinear control methods such as sliding mode control and back-stepping controls are presented as effective controls of the dc-link voltage. Utilizing an applicable and comprehensive model is essential to achieve accurate control studies and stability analyses.…”

Section: Introductionmentioning

confidence: 99%

Small-Signal Modeling of PMSG-Based Wind Turbine for Low Voltage Ride-Through and Artificial Intelligent Studies

et al. 2020

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“…In all of these methods, by improving the performance of the system under steady-state conditions, the performance of the system under dynamic conditions is reduced. To solve this problem, optimization tools, such as the spider monkey algorithm and the fuzzy logic control algorithm [24,25], as well as other methods, have been developed [26][27][28][29][30][31][32][33][34][35]. However, these methods complicate system computations and, as a result, reduce tracking speed.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Self-Predictive Incremental Conductance Algorithm with the Ability to Detect Dynamic Conditions

et al. 2021

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This paper presents a new version of the incremental conductance algorithm for more accurate tracking of the maximum power point (MPP). The modified algorithm is called self-predictive incremental conductance (SPInC), and it recognizes the operational region. It is capable of detecting dynamic conditions, and it detects sudden changes in power resulting from changes in the intensity of radiation or temperature. By selecting the appropriate step size, it obtains maximum power from the panel at any moment. The improved algorithm reduces output power ripple and increases the efficiency of the system by detecting the operating area and selecting the appropriate step size for each region. The SPInC algorithm divides the system’s work areas into three operating zones. It calculates the size of the appropriate step changes for each region after identifying the regions, which allows for more accurate tracking of the MPP and increases the system efficiency at a speed equal to the speed of the conventional method. These additional operations did not result in a system slowdown in the tracking maximum power. According to the MATLAB/Simulink simulation results, the SPInC algorithm is more efficient than conventional InC, and the ripple output power is reduced. SPInC is also compared to the improved perturb and observe (P&O) algorithm. In general, SPInC can compete with the popular algorithms that have been recently proposed for MPPT in the other researches.

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“…The PMSG‐based wind turbines connect to the grid via back‐to‐back (BTB) full power converter, which decreases the complexity of active power control in different situations. One of the most interesting issues related to this type of wind turbines is the FRT capability 7 …”

Section: Introductionmentioning

confidence: 99%

Application ofmulti‐stepbridge‐type fault current limiter for faultride‐throughcapability enhancement of permanent magnet synchronous generator‐based wind turbines

Firouzi

Nasiri

Benbouzid

et al. 2020

Int Trans Electr Energ Syst

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This study presents a new Multi‐Step Bridge‐type Fault Current Limiter (MSBFCL) for Fault Ride‐Through (FRT) capability enhancement of Permanent Magnet Synchronous Generator (PMSG)‐based wind turbine. In this structure, multi‐resistors in parallel with the Insulated‐Gate Bipolar Transistor (IGBT) switches are used to provide a controllable discrete step resistance. Considering the voltage sag depth at the Point of Common Coupling bus, the MSBFCL inserts the suitable limiting resistance in the faulted line path to limit short circuit current and boost the terminal voltage at determined value. Limiting the fault current based on multi‐step resistance prevents considerable overvoltage at the Grid Side Converter (GSC) of the PMSG and saturated operation mode of GSC control system in low voltage sag condition, which can lead to loss of controllability. The MSBFCL scheme is compared to the Superconducting Fault Current Limiter (SFCL) and Braking Chopper (BC) schemes. Simulations were carried out in Matlab/Simulink environment. Simulation results demonstrate that the MSBFCL provides a simple and effective solution to enhance the FRT capability of the PMSG in the whole range of voltage sag level and is advantageous over the SFCL and BC schemes.

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Super‐Twisting Sliding Mode Control for Gearless PMSG‐Based Wind Turbine

Cited by 72 publications

References 25 publications

Small-Signal Modeling of PMSG-Based Wind Turbine for Low Voltage Ride-Through and Artificial Intelligent Studies

Small-Signal Modeling of PMSG-Based Wind Turbine for Low Voltage Ride-Through and Artificial Intelligent Studies

Improvement of Self-Predictive Incremental Conductance Algorithm with the Ability to Detect Dynamic Conditions

Application ofmulti‐stepbridge‐type fault current limiter for faultride‐throughcapability enhancement of permanent magnet synchronous generator‐based wind turbines

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