A Novel Controllable Crowbar Based on Fault Type Protection Technique for DFIG Wind Energy Conversion System Using Adaptive Neuro-Fuzzy Inference System

This paper proposes a novel controllable crowbar based on fault type (CBFT) protection technique for doubly fed induction generator (DFIG) wind energy conversion system connected to grid. The studied system consists of six DFIG wind turbines with a capacity of 1.5 MW for each of them. The operation mechanism of proposed technique is used to connect a set of crowbar resistors in different connection ways via activation of controllable circuit breakers (CBs) depending on the detected fault type. For each phase of DFIG, a crowbar resistor is connected in parallel with a controllable CB and all of them are connected in series to grid terminals. The adaptive neuro-fuzzy inference system (ANFIS) networks are designed to detect the fault occurrence, classify the fault type, activate the CBs for crowbar resistors associated with faulted phases during fault period, and deactivate them after fault clearance. The effectiveness of proposed CBFT protection technique is investigated for different fault types such as symmetrical and unsymmetrical faults taking into account the single-phase to ground fault is the most frequently fault type that occurs in power systems. Also, a comparison between the behaviours of studied system in cases of using traditional parallel rotor crowbar, classical outer crowbar, and proposed CBFT protection techniques is studied. The fluctuations of DC-link voltage, active power, and reactive power for studied system equipped with different protection techniques are investigated. Moreover, the impacts of different crowbar resistance values on the accuracy of proposed technique are studied. The simulation results show that, the proposed technique enhances the stability of studied wind turbine generators and contributes in protection of their components during faults.