Study and Order of a Medium Power Wind Turbine Conversion Chain Connected to Medium Voltage Grid

The growing concern about the energy crisis and environmental protection has caused a growing interest in wind power generation systems. Researchers and engineers urgently need to create new multiphase induction machines for the production of wind energy, since they are essential parts of wind turbines. This study offers control and stability analysis of a multiphase induction machine based on the entropy stability requirements for its linearized model. The generated model was used to assess the on-load properties of the multiphase induction machine and calculate its steady-state parameters under each operating circumstance. According to the analysis, the eigenvalues depend on the machine parameters, with the excitation capacitance and speed variation being the most important. Stabilization of the multiphase induction machine is the main focus of the singular values, which vary according to its variables. The simulated results include an examination of a multiphase induction machine steady state for voltage build-up at various types of load.

Section: Introductionmentioning

confidence: 99%

Persistent Voltage Control of a Wind Turbine-Driven Isolated Multiphase Induction Machine

Ben Sliemene,

Khlifi

2023

“…Wind power generation implementation was introduced on the basis of DFIG, a fuzzy PI gain scheduling developed for DFIG vector control units used in variable speed wind turbines [2]. Upon theoretical analysis of the wind turbine and DFIG processing, due to mathematical models of the system, a DFIG separation control was developed based on a fuzzy-PI controller in [3]. Wind power generation by DFIG can be connected directly to the grid via the stator, which is driven by a direct AC/DC inverter.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Comparison of Fuzzy-PI and Genetic Control Algorithms for Active and Reactive Power Flow between the Stator (DFIG) and the Grid

Guediri¹,

Touil²

2022

This paper performs a comparison between Fuzzy-PI regulators and Genetic Algorithm (GA) for controlling an active and reactive Doubly-Fed Induction Generator (DFIG) for providing power to the electrical grid. Theoretical analysis, modeling, and simulation studies are provided. Control strategies were developed for both active and reactive forces in order to optimize energy production. The performance of the two control strategies was examined and compared using benchmarks for durability and reference traceability. This paper studied a system consisting of a wind turbine operating at variable wind speed and a two-feed asynchronous machine (DFIG) connected to the grid by the stator and fed by a transducer at the side of the rotor. The conductors were separately controlled for active and reactive power flow between the stator (DFIG) and the grid, which was achieved in this article using conventional PI and fuzzy logic controllers. The considered controllers generated reference voltages for the rotor to ensure that the active and reactive power reached the required reference values. This was done in order to ensure effective tracking of the optimum operating point and the maximum output of electrical power. System modeling and simulation were examined in Matlab/Simulink. Dynamic analysis of the system was performed under variable wind speed.

The Effect of Stator Inter-Turn Short-Circuit Fault on DFIG Performance Using FEM

Mellah

Arslan²,

Sahraoui³

et al. 2022

Doubly-Fed Induction Generators (DFIG) are operated for wind energy production, and as their capacity is increasing, their safety and reliability become more important. Several faults affect the performance of DFIG. The stator winding Inter-Turn Short-Circuit Fault (ITSCF) is one of the most prevalent electric machine failures. This study examined the stator ITSCF effects on DFIG performance for different cases. The DFIG was designed and engineered using the Finite Element Method (FEM) and the Maxwell software, and was examined in healthy operation and four defective cases with various Numbers of Inter-Turn Short Circuit Faults (NITSCF): 4, 9, 19, and 29. These models allowed the examination of the effects and the comparison of each case separately from the healthy state. The comparison was plotted in Matlab to show the effects of the faults. The novelty of this study was that it investigated the effects of different NITSCF on the performance of DFIG and not only on their effect on the stator current and distribution of magnetic flux density. A better understanding of the short circuit effects on the performance of the DFIG can be exploited for subsequent implementations of early fault detection systems.