Design of a MPPT controller for permanent magnet synchronous generator driven wind turbine

Use your smartphone to scan this QR code and download this article ABSTRACT Wind and other renewable energies are more and more developed all over the world, especially in countries with high wind potential such as Vietnam, to replace fossil energy, which would be exhausted in the near future. One important characteristic of wind turbines is that at each different wind speed, there exists a working point, represented by the rotation speed and the mechanical power at the crankshaft of the wind turbine, at which themaximummechanical power is obtained, called maximum power point (MPP). Therefore, when the wind speed changes, this working point must be changed to be able to extract the maximum power from the wind to improve the total efficiency of thewind turbine system. This, in awind energy conversion system (WECS), is assigned to the maximum power point tracking (MPPT) controller. In this paper, a MPPT controller is proposed, based on an improved Perturb and Observe (P&O) algorithm, for wind turbines using permanent magnet synchronous generator (PMSG), to maximize energy without measuring the wind speed and power characteristics of the wind turbine. An experimental model is also designed and tested in laboratory conditions, in which two coefficients K1 and K2 are used in turn when the working point is far or close to themaximumpower point. The experimental results show that the proposed MPPT controller allows the extraction of maximum power from wind turbines under variable wind speed without determining the wind speed and characteristics of the wind turbine system.


INTRODUCTION
In recent years, the use of renewable energy resources is more and more increased due to the increasing need for energy and the shortage of traditional energy sources in the near future 1 .
The literature review shows that, renewable energy systems are not cost competitive against conventional fossil fuel power systems. However, the need for cleaner power and improvements in alternative energy technologies gives good potential for widespread use of such systems.
Community facilities such as rural hospitals, schools, telecommunication and water pumping stations can contribute significantly to the welfare of people and rural development. Renewable energy systems have demonstrated the potential to provide support in some of the basic infrastructure needs in remote and urban areas. One of the interesting utilizations of the renewable energy in community development is to electrify remote villages and rural areas located so far from power stations and distribution networks which are uneconomical to install. Recent research and development in renewable energy sources have shown excellent potential, as a form of supplementary contribution to conventional power generation systems. In order to meet sustained load demands during varying natural conditions, different energy sources and converters need to be integrated with each other for extended usage of alternative energy. Therefore, it is necessary to construct a system capable of generating maximum power under these constraints 2 . The wind energy conversion system (WECS) incorporated in the proposed scheme consists of a wind turbine coupled to a permanent magnet synchronous generator (PMSG). An AC/DC power electronic interface with diode bridge rectifier and a DC/DC boost converter are used for maximum power tracking. An improved P&O method is proposed to avoid the instable effect of original one 3 . There are two types of MPPT controller for wind turbine: indirect and direct. Indirect MPPT controllers are based on the knowledge of the generator's power characteristic, which is usually not available with a high degree of accuracy and also changes with rotor aging and require wind speed measurement 4 . On the other hand, direct MPPT controllers are independent from the knowledge of the generator curve, and the operating point is independent of climatic conditions 5 . In this paper, a direct MPPT controller for variable speed PMSG driven wind turbine is proposed and tested in laboratory condition to extract maximum power from the wind without any knowledge about generator's characteristic or load condition.

Wind aerodynamics
Mechanic power of a wind turbine can be expressed in terms of the air density ρ, the blade radius R blade , and is the wind speed v wind 6 : where C p is the power coefficient. This coefficient is also known as Betz limit. It can be expressed in terms of reduced velocity λ and blade angle β . If ω is the rotor speed, the reduced speed λ is defined: A generic equation is used to model the power coefficient C p = C p (λ , β ), based on the modeling turbine characteristics described in 7 : where: The characteristic function C p vs. λ , for various values of the pitch angle β , is illustrated in Figure 1. The maximum value of C p is achieved for β = 0 • . This particular value λ opt results in the point of optimal efficiency where the wind turbine captures the maximum power 8 . In this work, with laboratory condition, the tested wind turbine does not have the pitch controller system, so we considered that β = 0 • at all times. If the speed ratio is maintained at its optimal value λ opt , the power coefficient is at its maximum value C pM = C p (λ opt ), the maximum power extracted from the wind turbine will be: On the other hand, the speed ratio assumed to be maintained at the optimum value, we obtain the optimum speed rotor: Thus, for each wind speed v wind , there is an optimal rotor speed ω opt which made a maximum power recovered from the wind (Figure 2) 9 .

Electrical system modeling
The WECS incorporated in our scheme consists of a wind turbine coupled to a PMSG. Since the PMSG produces variable amplitude -variable frequency voltage, additional power electronic devices are required to meet power quality demand. A three-phase diode bridge rectifier is used for the AC/DC conversion. A boost converter (DC/DC) is used to vary the rotor speed by adjusting the converter's duty cycle (Figure 3). The dynamic model of PMSG can be represented in the Park's system using these equations 10 : The expression of electromagnetic torque in the rotor is given by: where p is the number of pole pair, λ m is the magnetic flux, L d is the direct axis inductance, L q is the inductance in quadrature, R s is the stator resistance and ω is the electrical angular frequency.
If the rotor is cylindrical, L d ≈ L q ≈ L s so: Relationship between mechanical torque and electromagnetic torque in a wind turbine: where J is the inertia of the wind turbine.
In PMSG wind generation systems, the output current and voltage are proportional to the electromagnetic torque and rotor speed, respectively 9 : Where I a is the stator current. On the other hand: V W T is the generator phase voltage and L s is the inductance of the generator.
From (14), (15) we have: Thus, in a variable speed PMSG driven wind turbine, we can vary the output voltage V W T of the generator (by adjusting the duty cycle of the boots converter) to change the rotor speed ω. If we can control our system to work at the optimal rotor speed ω opt , maximum power will be extracted from the wind turbine.

Conventional P&O MPPT controller
In the field of MPPT for wind energy, the P&O algorithm is a very popular method because of its simplicity and easy implementation. The main idea is the perturbation of rotor speed and takes reaction to reach the MPP. If the variation of the rotor speed increases the power extracted, the next variation will be kept in that direction. Conversely, if the variation of the rotor speed decreases the power extracted, the next variation will be reversed 10 . However, this method has its own negative points. The response to wind speed change is extremely slow, especially for large inertia wind turbines 11,12 . Rapidly fluctuating character of wind supply makes the situation even worse. Oscillation around the MPP is also another inevitable disadvantage. All these drawbacks can significantly lower MPPT efficiency and may cause oscillation in the system 13,14 .

Proposed MPPT controller
The proposed algorithm is based on the basic principles of the P&O algorithm, by adapting the value of perturbation step to achieve the faster convergence rate. Variation of the next step is proportional with the derivative of power and voltage (∆D=k*∆P/∆V). Around the maximum point, when ∆P varies near zero, we will stop the perturbation. In addition, when the wind speed varies, based on the rise in current or voltage, we can predict the new position of the MPP. That means, when wind speed increase, the new MPP will move to the left of the power curve, and when wind speed decrease, the new MPP will move to the right. The flow chart of the proposed algorithm is given in Figure 4. This algorithm needs to determine the two experimental constant k 1 and k 2 . When the operating point is far away from MPP, we should use a big variation to reach the MPP more quickly. When our system is working in the MPP and wind speed change, we will use smaller variation to keep the system stable. Thus k 1 > k 2 and their value are proportional to the turbine capacity. The k 1 and k 2 are determined experimentally on physical model shown in Figure 5.
The experimental parameters of our proposed controller are given in Table 1 .

EXPERIMENTAL, RESULTS AND DISCUSSION
Tested system The wind turbine emulator includes a tunnel and a three-phase motor controlled by an inverter, a wind speed gauge, a 6-bladed turbine system and a PMSG. The wind speed is varied by changing the three-phase   motor speed through the inverter. The output voltage is rectified by a three-phase diode bridge. These devices were produced by the DE LORENZO group (Italy) in Figures 5, 6 and 7.
To determine the maximum power point for each wind speed of a wind turbine system, an experiment is arranged as shown in Figure 8a and the results are shown in Figure 8b.

Results and discussion
At 12.5 m/s wind speed, we tested our system in 2 conditions: without MPPT controller (Figures 9 and 11) and with MPPT controller (Figures 10 and 12). We also use a battery and then a 60W-220V lamp as load, we obtained the following result:  With the battery load, when the MPPT is not activated, the charging power is 11.4W, and when the MPPT controller is activated, the charging capacity reaches 15.6W. With the 60W-220V lamp loading without MPPT system, the measured power is 3.9W, after activating the MPPT controller the measured power is now 15.6 W. Results obtained at different wind speeds with 60W-220V are given in Table 2.   Table 2 shows the maximum power supplied by the MPPT unit for lamps similar to the resistor survey results in Figure 8. We can see that with our proposed MPPT controller, when wind speed and load change, the power extracted from our WECS is always maximized. Table 3 presents the comparison of the time to reach the MPP when experimenting for wind speed from 0m/s to 10m/s, then down to 8m/s and then up to 10m/s. The P&O algorithm of Badreddine et al. 14 takes more than 20 seconds to achieve MPP while the proposed algorithm only needs 12 seconds in the inception phase. When the wind speed increases from 8m/s to 10m/s, the time to get the MPP of the proposed algorithm is also faster than that in Badreddine et al. 14 .

CONCLUSION
In this paper, an adaptive perturbation MPPT controller for WECS is designed and tested in laboratory condition. Without information of wind speed, generator's power characteristic or load condition, control signals were generated to extract maximum power from the wind. Experimental results show that our proposed controller works well to achieve the MPP of the wind turbine system at various wind speeds and load conditions.