Abstract
The wind power plant is one of the fastest growing electrical power sources. The integration of wind turbine into the power grid originates various power quality problems. Low-voltage ride through (LVRT) capability improvement is impregnating exigency in recent power quality issue, which is acquainted with various renewable power generation resources like solar PV and wind power plant. Dynamic voltage restorer (DVR) is a custom power device (CPD), which is connected in series with the electrical system, and it is a contemporary and efficient CPD expended in the distribution system to curb the power quality problems. In this paper, LVRT capability of doubly fed induction generator (DFIG) wind turbine system connected to a power grid is enhanced by means of DVR. LVRT capability is improved by the introduction of neural network controller. This controller fulfils the various grid code requirements. Thus, the performance of the DVR becomes far superior by the robust control technique. The simulation results were compared with conventional PI controller and the proposed artificial neural network (ANN) controller. It is proved that the proposed system raises the reliability of grid-connected DFIG wind turbine system.
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Abbreviations
- ANN:
-
artificial neural network
- CPD:
-
custom power device
- DFIG:
-
doubly fed induction generator
- DVR:
-
dynamic voltage restorer
- EMF:
-
electromotive force
- FNN:
-
feed-forward neural network
- GSC:
-
grid side converter
- LVRT:
-
low-voltage ride through
- PQ:
-
power quality
- RSC:
-
rotor side converter
- STATCOM:
-
static synchronous compensator
- SVC:
-
static VAR compensator
- WECS:
-
wind energy conversion system
- WT:
-
wind turbine
- WTG:
-
wind turbine generator
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Puliyadi Kubendran, A.K., Ashok Kumar, L. (2020). LVRT Capability Improvement in a Grid-Connected DFIG Wind Turbine System Using Neural Network-Based Dynamic Voltage Restorer. In: Kumar, L., Jayashree, L., Manimegalai, R. (eds) Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications. AISGSC 2019 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-24051-6_2
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