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

This paper presents a design of robust intelligent protection technique using adaptive neuro-fuzzy inference system(ANFIS)approach to detect and classify the fault types during various faults occurrence in large-scale grid-connected wind farm.Also,it is designed to determine the fault location and isolate the wind turbine generators located in the faulted zone during fault occurrence and reconnect them after fault clearance.The studied wind farm has a total rating capacity of 120 MW,where it consists of 60 doubly fed induction generator(DFIG)wind turbines each has a capacity of 2 MW.Moreover,the wind farm generators are positioned in 6 rows,where each row consists of 10 generators.The impacts of fault type,fault location,fault duration,cascaded faults,permanent fault and external grid fault on the behaviours of the generated active and reactive power are investigated.Also,the impacts of internal and external faults in cases of different transition resistances are investigated.The simulation results indicate that,the proposed ANFIS protection technique has the ability to detect,classify and determine the fault location,then isolate the faulted zones during fault occurrence and reconnect them after fault clearance.Furthermore,the wind turbines generators which are located in un-faulted zones can stay to deliver their generated active power to the grid during fault period.

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.