Differential Evolution Based IDWNN Controller for Fault Ride‐Through of Grid‐Connected Doubly Fed Induction Wind Generators

Abstract

The key objective of wind turbine development is to ensure that output power is continuously increased. It is authenticated that wind turbines (WTs) supply the necessary reactive power to the grid at the time of fault and after fault to aid the flowing grid voltage. At this juncture, this paper introduces a novel heuristic based controller module employing differential evolution and neural network architecture to improve the low-voltage ride-through rate of grid-connected wind turbines, which are connected along with doubly fed induction generators (DFIGs). The traditional crowbar-based systems were basically applied to secure the rotor-side converter during the occurrence of grid faults. This traditional controller is found not to satisfy the desired requirement, since DFIG during the connection of crowbar acts like a squirrel cage module and absorbs the reactive power from the grid. This limitation is taken care of in this paper by introducing heuristic controllers that remove the usage of crowbar and ensure that wind turbines supply necessary reactive power to the grid during faults. The controller is designed in this paper to enhance the DFIG converter during the grid fault and this controller takes care of the ride-through fault without employing any other hardware modules. The paper introduces a double wavelet neural network controller which is appropriately tuned employing differential evolution. To validate the proposed controller module, a case study of wind farm with 1.5 MW wind turbines connected to a 25 kV distribution system exporting power to a 120 kV grid through a 30 km 25 kV feeder is carried out by simulation.

Fuzzy grey wolf optimization for controlled low-voltage ride-through conditions in grid-connected wind turbine with doubly fed induction generator

“…In fact, V dc must be low while rectifying the fault signal. The proposed GWFE-LVRT generates lower V dc when compared with the LVRT, GA-LVRT, 45 GWO-LVRT, 46 DE-LVRT, 47 PSO-LVRT, 48 and ABC-LVRT 49 models. A graphical representation of the gain acquired by the proposed and conventionally based systems is shown in Figure 8(c).…”

“…In fact, V dc must be low while rectifying the fault signal. The proposed GWFE-LVRT generates lower V dc when compared with the LVRT, GA-LVRT, 45 GWO-LVRT, 46 DE-LVRT, 47 PSO-LVRT, 48 and ABC-LVRT 49 models. A Figure 6.…”

A novel ROA optimized Bi-LSTM based MPPT controller for grid connected hybrid solar-wind system

“…The wind turbine system converts kinetic energy to mechanical energy, which is converted to electrical energy concerning the DFIG (Manonmani et al , 2015). The power obtained from DFIG wind turbine is given by: Here, ρ air denotes the density of air mass (kg/m 3 ), v denotes the wind speed, a r denotes the area and c p denotes the power coefficient.…”

“…The wind turbine system converts kinetic energy to mechanical energy, which is converted to electrical energy concerning the DFIG (Manonmani et al, 2015). The power obtained from DFIG wind turbine is given by:…”

Design of robust intelligent protection technique for large-scale grid-connected wind farm

“…The DFIG consists of wound rotor slip-ring induction generator, where its stator windings are connected to a constant frequency electric grid, and its rotor windings are connected to the grid via bidirectional converters. These converters consist of rotor side converter (RSC) and grid side converter (GSC) [15]. The control systems of wind turbine play an important role to control and obtain the maximum energy from the available wind speed.…”