- Original research
- Open Access
Design of robust intelligent protection technique for large-scale grid-connected wind farm
© The Author(s) 2018
- Received: 21 December 2017
- Accepted: 9 May 2018
- Published: 12 June 2018
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.
- Fault location
- Wind farm
- Active power
- Reactive power
It is well-recognized that one way of producing electricity from renewable energy sources is to use the wind turbines that convert the wind energy contained in the flowing air into the electrical energy. Moreover, the importance of this field is one of the best ways to protect the environment by reducing the carbon emissions [1–7]. Thus, the trends of building large-scale wind farm are essential to improve and increase the production efficiency of electricity. The wind farm has a lot of wind turbines which are connected to the electrical grid. The DFIG is one the most powerful generator in the market because it has various advantages such as variable speed and cost effective partially rated power converter [8–12]. As mentioned earlier, nowadays applicant is focused on a variable speed wind turbine.
The most prevalent generators in wind farm are the variable speed DFIG wind turbine . Moreover, the DFIG had been widely used in the large-scale 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) . The control systems of wind turbine play an important role to control and obtain the maximum energy from the available wind speed. The control systems of DFIG wind turbine are the RSC controller, the GSC controller and the pitch angle controller [16–21]. The disadvantage of DFIG is a sensitive to the faults occurrence in the grid [22, 23]. Furthermore, in case of a voltage dips nearing to the wind farm, over-current surges similar to short circuit currents will pass via stator windings, which will also flow via rotor windings due to a magnetic coupling between stator and rotor [24–26]. During this situation, need a special attention to protect the power electronic converters, DC-link capacitor and the DFIG windings from the dangerous effects by disconnecting the faulted zones. The high currents can destroy the wind turbine components therefore, the protection system is essential to protect and isolate the faulted zones inside large-scale wind farms.
In the literature, the protection technique for large-scale wind farm attracts the interest of researchers, where the most worrynty problem of wind farm generators is the cascading trip events caused by occurrence of different faults, which may propagate and cause isolation of all wind turbine generators over very large area [27–32].
This research focuses on avoiding trip events of all wind turbine generators during faults and proposes a robust intelligent protection technique based on artificial intelligence to trip only the faulted zone of wind farm. The applications of artificial intelligence in protection and control of power system are widely used [32–35]. The ANFIS architecture is an artificial intelligence approach that combines of a neural network system with a fuzzy logic system to achieve best performance and give suitable solutions for the studied problem [36–39].
The proposed protection technique for large-scale wind farm is performed and simulated using MATLAB/Simulink platform based on robust intelligent ANFIS technique. The proposed protection technique is designed to detect fault occurrence, classify fault type and determine fault location. Also, the proposed technique is utilized to isolate the wind turbine generators of faulted zone to protect their components and reduce undesirable fault effects. The variations of active and reactive power for faulted and un-faulted wind turbine generators with different fault types, fault locations, fault durations, cascaded faults, permanent fault and external grid fault are investigated. Furthermore, the behaviours of wind farm generators in cases of internal and external faults having different transition resistances are investigated. This rest of this paper can be organized as follows:
Section 2 provides the description of a mathematical modelling and control systems of DFIG wind turbine. In Section 3, analyzes the design of robust intelligent protection technique for large-scale wind farm using ANFIS. A discussion of the simulation results is reported in Section 4 to indicate the performance and efficiency of the proposed technique. Section 5 briefly presents the conclusion.
2.1 Mathematical modelling
The stator windings of DFIG are connected to the grid, while the rotor windings are fed via AC/DC/AC converters such as RSC and GSC. In addition to the converters, DC-link capacitor is connected between them to acts as energy storage and keeps the DC voltage ripple with some little variations. The RSC works at variable frequencies depending on available wind speed and the GSC works at grid frequency. The power flow direction via converters depends on the operation mode of the electrical generator. In the super-synchronous operation mode, the stator output power and the rotor slip power are delivered into electrical grid. In the sub-synchronous operation mode, the stator of DFIG delivers power to the grid and the slip power to the rotor via the slip rings and the converters.
2.2 Control systems
The control system strategy of DFIG wind turbine is designed based on controlling of RSC and GSC. Also, it is designed to regulate the blades pitch angle which regulates the speed of wind turbine and protects the wind turbine mechanical parts from damage. Usually, the control systems are designed using proportional integral (PI) controller due to simple structures, clear functionality, low cost and reliable performance [42–46]. Firstly, the RSC is utilized to control active power and reactive power (or voltage level) measured at generator terminals. The control of active power is used to adjust the angular speed of turbine rotor to follow power-speed characteristic of turbine for tracking the maximum power point. The reactive current which is flowing in a converter is used to control voltage level or reactive power at the DFIG terminals. Secondly, voltage level at the DC-link capacitor is controlled using the GSC controller. Also, it can be controlled to absorb or generate reactive power for supporting the grid voltage. Thirdly, wind turbine is provided with a pitch angle control system to limit the extracted power during the condition of high wind speed. The pitch angle control system is activated only when the rotor speed increasing than the rated value due to increasing of wind speed or fault occurring.
3.1 Studied large-scale wind farm
Parameters of large-scale wind farm based DFIG
Parameters of DFIG
Generator rated power (MW)
Generator rated voltage (V)
Resistance of the rotor (pu)
Leakage inductance of the rotor (pu)
Resistance of the stator (pu)
Leakage inductance of the stator (pu)
Mutual inductance (pu)
Lumped Inertia Constant (s)
The parameters of the power transmission line (25 kV)
Zero sequence resistance (Ω/km)
Positive sequence resistance (Ω /km)
Zero sequence inductance (H/km)
Positive sequence inductance (H/km)
Zero sequence capacitance (F/km)
Positive sequence capacitance (F/km)
Internal feeders (Z 1 , Z 2 , ……, Z 6 ) parameters
Zero sequence resistance (Ω /km)
Positive sequence resistance (Ω /km)
Zero sequence inductance (H/km)
Positive sequence inductance (H/km)
Wind turbine transformer parameters
Voltage ratio (kV)
PCC bus transformer parameters
Voltage ratio (kV)
3.2 The proposed protection technique based on ANFIS
ANFIS is the fuzzy system represented in a framework of the adaptive networks. Moreover, ANFIS combines the concepts of fuzzy inference systems (FIS) rule base and the learning benefit of artificial neural networks (ANN) to form hybrid adaptive systems with learning capabilities. ANFIS uses FIS rule base to describe the relationship between the input/output parameters and the ANN to train the data then find best parameters for the FIS membership function to get the suitable rules. The principles of ANFIS architecture consists of five layers, namely fuzzification layer, product rule layer, normalization layer, defuzzification layer and total summation layer. With input/output data for a given set of parameters, the membership function parameters of FIS model are adjusted using adaptive algorithm. The adaptive algorithm uses a back-propagation algorithm alone or a hybrid learning algorithm for training the parameters of MFs to emulate a given training data set where the hybrid algorithm applies a combination of the least-squares method and the back-propagation gradient descent method.
4.1 Impacts of fault types
In this subsection, the behaviours of wind farm generators during occurrence of different faults such as single-line to ground fault, double-line to ground fault, and three-line to ground fault are investigated in cases of isolating and un-isolating of the faulted wind turbine generators by controlling of CBs trip signals. The studied faults are occurred close to row 1 at fault location (F1) as shown in Fig. 3.
Based on the fluctuations of active power, reactive power and their percentage errors of the studied system for different fault types, it can be suggested that, the proposed protection technique isolates the faulted wind turbine generators in cases of double-line to ground fault and three-line to ground fault by tripping the faulted zone CBs.
4.2 Impacts of fault locations
As shown in Fig. 11a, when the fault occurs at F1, the active power is fluctuated between 49.05 MW and 119 MW and it stabilizes at a value of 90 MW before fault clearance. Also, it is fluctuated between 29.58 MW and 104.5 MW, then it stabilizes at the value of 71.6 MW during fault occurrence at F2 and F3, while it fluctuated between 12.65 MW and 85.8 MW, then it stabilizes at the value of 54.5 MW when the fault occurs at F4, F5 and F6. The reactive power variations are indicated in Fig. 11b. It is clear that, when the fault occurs at F1, the reactive power is decreased to − 122.9 MVAR during fault period and fluctuated between − 13.48 MVAR and 2.28 MVAR after fault clearance, then returns to steady state value. When the fault occurs at F2 and F3, the reactive power is decreased to − 128.6 MVAR and fluctuated between − 21.92 MVAR and 9.88 MVAR after fault clearance. Also, it is decreased to − 117.4 MVAR when the fault occurs at the locations of F4, F5, and F6, where it is fluctuated between − 30.72 MVAR and 17.59 MVAR after fault clearance.
4.3 Impacts of fault duration
4.4 Impacts of cascaded faults occurrence
4.5 Impacts of permanent fault occurrence
4.6 Impacts of external fault occurrence
4.7 Impacts of transition resistance
A comparison between the proposed protection technique with others protection techniques [30–32] is provided as follows: Chen et al.  have presented superconducting fault current limiter (SFCL) protection system to suppress the fault current of DFIG-Based wind farm. Wang et al.  have proposed a flexible fault ride through strategy to allow a few wind turbine generators to trip and maintains the connection to grid of most generators during fault. Noureldeen et al.  have proposed an efficient ANFIS crowbar protection for DFIG wind turbines to protect wind turbine generator components during grid fault. The objective of the proposed technique focuses on avoiding trip events of all wind farm during faults and proposes a robust intelligent protection technique for wind farm based on ANFIS approach to trip only the faulted wind turbine. The proposed technique has some advantages comparing with others protection techniques such as classification of fault types, determination of fault locations and isolation of faulted zones. Moreover, the proposed technique is investigated at different fault conditions such as fault duration, cascaded faults, permanent fault, and variations of transition resistance values in cases of internal and external faults.
Finally, the simulation results show that, the proposed protection technique has the ability to detect and classify the fault, hence isolate the faulted zones during fault occurrence, where that lead to support the un-faulted generators to stay in services.
This paper presents a design of proposed robust intelligent protection technique for large-scale wind farm connected to electrical grid using ANFIS approach. The studied wind farm has a total rating capacity of 120 MW, where it consists of 60 DFIG wind turbines, and each generator has a capacity of 2 MW. Moreover, the wind farm generators are located in 6 rows, where each row consists of 10 generators and each row is simulated by one DFIG wind turbine with a rating of 20 MW. The proposed protection technique is designed using ANFIS approach to detect, classify and determine the location of different faults which occur in the grid-connected wind farm to protect the wind turbine generator components from dangerous effects. The proposed technique is investigated at different fault conditions such as fault types, fault location, fault duration, cascaded faults, permanent fault and external grid fault. Also, the impacts of internal and external faults in case of different transition resistances are studied. The fluctuations of active and reactive power are monitored at the PCC bus of wind farm for different fault conditions. The results show that, the measured active power for faulted row generators is fallen to zero MW due to isolation of faulted zone during fault occurrence. After fault clearance and reconnect the isolated zone, the generated active power has some fluctuation, then returns to steady state value. Also, the active power for all un-faulted rows is affected during different fault periods. The reduction of active power at PCC bus depends on the number of faulted rows and fault duration. The results show that, the proposed technique can detect, classify and determine the fault location, hence it isolates the faulted zone during fault occurrence and reconnect it after fault clearance. Also, the wind turbine generators which are located in un-faulted zones can remain in delivering their generated active power to the electrical grid. Moreover, the wind turbine generators in faulted zone return to deliver their active power to the grid after fault clearance. Finally, the simulation results demonstrate the applicability and confirm the robustness of proposed technique.
ON carried out the MATLAB model for studied wind farm. ON and IH carried out the ANFIS protection technique algorithms. ON and IH participated in result analysis and discussion. Both authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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