dSPACE implementation for a proportional–integral-based root mean square voltage controller used in stand-alone wind energy conversion systems

Abstract

The induction machine and the synchronous machine are very promising in renewable energy production for uses in wind turbines to produce energy in remote areas, and we note that self-excited induction generator is more adapted due to difficult geographical conditions and best cost; on the other hand, permanent magnet synchronous generator works without any excitation system; all of these have a major problem in use which is the stability of voltage. To solve this problem, we propose in this article the examination of a control plot for keeping the produced root mean square voltage steady. This article presents an experimental comparative study of performances in terms of root mean square voltage on two possible small wind turbine systems. One of the systems is based on permanent magnet synchronous generator and the other is based on self-excited induction generator. Taking into account the parameters of load and wind speed, experimental test benches for both systems are implemented using the dSPACE card for controlling the pulse width modulation inverter to impose the V_rms value of the desired output voltage.

Keywords

Stand-alone generator wind energy self-excited induction generator permanent magnet synchronous generator dSPACE implementation

Introduction

The world’s energy crisis in recent decades, the massive consumption of fossil fuels, the issue of global warming, the impact on the government’s economic policy, climate change, and energy security have motivated the use and development of alternative sustainable and clean energy sources (Rapin and Noel, 2010) that would replace the current energy production. Hence, several sources of renewable energy are being exploited and researched for developing power extraction techniques aimed at reliability, lowering costs (manufacturing, usage, and recycling), and increased energy efficiency. Among these energies are those originated from wind and transformed into electricity through wind turbines, which has become competitive through the development of the wind turbine industry and the evolution of semiconductor technology.

The synchronous generator has traditionally been used for power generation in stand-alone operations. Particularly, the permanent magnet synchronous generator (PMSG) is a very interesting resolution in isolated and stand-alone wind turbine applications because of these benefits: high performance and great mechanical torque, and the dispensableness of the excitation system. These qualities are compensated for by a higher price than the asynchronous generators. The induction generator is an alternative and is increasingly being used because of its several advantages (Chakraborty et al., 1998). Some of these are a brush-less system, lower cost, robustness, less need of maintenance, and operational simplicity. In addition, the induction generator provides self-protection against short circuits and over currents and can generate power at different rotational speeds. The disadvantages, however, are reactive power consumption and poor voltage and frequency regulation (Seyoum et al., 2001). When connected to a grid, the grid controls the frequency and voltage, so the regulation is not an issue. In remote areas, where the induction generator is operated in isolated mode, there is no main grid to provide this regulation. Hence, the regulation of voltage and frequency is of great importance.

The appropriate combination of terminal capacitance, load, and speed is necessary to verify the condition of self-excitation of an induction machine (IM). These in turn cause certain limitations on the performance of the machine. The excitation requirements and output voltage stability of an induction generator have been dealt with extensively in the literature (Abbou et al., 2012; Barara et al., 2013). Sandhu (2003) presents the study of a variable speed prime mover for minimum capacitance required for self-excitation. He used a nodal admittance approach to find the value. The main limitation of the self-excited induction generator (SEIG) system is the poor voltage and frequency regulation when supplying variable loads connected to the stator terminals.

The fluctuating nature of wind causes frequent variation of the voltage and frequency at the output of the machine; consequently, we need to associate it with the load or the network by means of static converters that can enhance the nature of energy (voltage and frequency) utilizing sufficient control procedures (Chilipi et al., 2014; Errami et al., 2012).

The reference configuration is implemented using a three-phase pulse width modulation (PWM) rectifier. It is possible to perform dynamic and reliable speed or torque control of the generator, which makes it simple to move the working point over the full scope of the rotational speeds (Bharanikumar and Kumar, 2010). The major disadvantage of this structure is the complexity of the assembly which comprises three complete arms thus six switches (Adem, 2005).

Renewable technologies are able to generate electricity on site, thanks to the specific architectures of small wind turbines specially designed for isolated sites, such as the SEIG (with an excitation system) or the PMSG (no excitation system required). This context drives to simplify and develop as much as possible controlled wind system structures using electronic cutting techniques to broaden the exploitable range of wind speeds and provide energy according to the quality of voltage and frequency.

In this context, the contribution envisaged with this work is to evaluate two new small isolated wind chain structures, where the PMSG and SEIG represent the essential part.

This article describes the experimental implementation of the PMSG- and SEIG-based small wind turbine systems, so we proposed insertion of the PWM inverter between the generator and the three-phase alternative current (AC) load, which acts on the system to control the V_rms value of the output voltage to the desired value even in the presence of variation of the load or wind velocity.

The design of the proposed control

We start this part by showing the stand-alone machines, which are proposed for sustaining the three-phase load. Figures 1 and 2 demonstrate the schematics of our framework (El Akhrif et al., 2016).

Figure 1.

A SEIG-based small wind turbine system.

Figure 2.

A PMSG-based small wind turbine system.

The following sections examine the scientific conditions that model the elements of the framework considered, in particular the SEIG, PMSG, voltage source inverter (VSI), and the wind turbine.

Equation model of the wind turbine

The wind power received by the blades was changed by the wind turbine into mechanical power. The contemplated model contains a wind model, an aerodynamic part, and a mechanical model (Elbeji et al., 2014).

Wind model

The variation $v (t)$ of the wind speed is written using the sum of the harmonics corresponding to each pulsation $ω_{i}$ with a phase $φ_{i}$ determined arbitrarily (Elbeji et al., 2014)

v (t) = \sum_{k = 1}^{N} A_{i} \cos (ω_{i} t + φ_{i})

(1)

with $N$ being the number of discrete values used for sampling.

Aerodynamic and mechanical model

The wind power developed by the turbine is given by the following equation (Hachicha and Krichen, 2012; Muljadi and Butterfield, 2001)

P_{v} = \frac{ρ S v^{3}}{2}

(2)

The turbine converts wind aerodynamic energy into mechanical energy. Its aerodynamic torque is given by

T_{a e r o} = \frac{1}{2 Ω_{t}} C_{p} (λ, β) ρ S v^{3}

(3)

The aerodynamic power at the turbine rotor can be written as follows

P_{a e r o} = C_{p} (λ, β) P_{v} = C_{p} (λ, β) \frac{ρ S v^{3}}{2}

(4)

where Ω_t is the angular velocity of the wind turbine, ρ is the air density (1.22 kg/m³), v is the wind speed (m/s), $S = π R^{2}$ is the area of the turbine blade in (m²), R is the radius of the turbine (m), and C_p is the power coefficient which is a function of the tip speed ratio λ and the blade pitch angle β (degrees).

The power coefficient C_p is expressed as a function of $λ_{i}$ and β (Muhando et al., 2009)

C_{p} (λ, β) = 0.5176 (\frac{116}{λ_{i}} - 0.4 β - 5) \exp (- \frac{21}{λ_{i}}) + 0.0068 λ

(5)

where λ_i is given by

\frac{1}{λ_{i}} = \frac{1}{λ + 0.08 β} - \frac{0.035}{β^{3} + 1}

(6)

The power extracted is optimal for $C_{p} = 0.48$ , $β = 0$ , and $λ = 8.1$ .

The fundamental principle of the dynamics is applied to know the evolution of mechanical speed

J \frac{d Ω_{m}}{d t} = T_{m} - T_{e m} - f Ω_{m}

(7)

where J and $f$ are the system moment of inertia and the friction coefficient, respectively.

Figure 2 shows the power coefficient variation C_p against the tip speed ratio λ and the pitch angle β, and Figure 3 shows the mathematical model of the mechanical part of the wind turbine with the maximum power point tracking (MPPT) algorithm.

Figure 3.

The power coefficient variation C_p against the tip speed ratio λ and the pitch angle β.

The MPPT operation mode aims to maximize power extraction for medium and low wind speeds by following the maximum power point curve $C_{p - m a x}$ as depicted in Figure 4. It is based on the recovery of the optimal rotor speed which allows the system to exploit the maximum power available for any instantaneous value of wind speed (Figure 5).

Figure 4.

Wind turbine model with the MPPT algorithm.

Figure 5.

The mechanical power curves at various wind speeds.

SEIG model

On the reference (d,q) system, the dynamic model of squirrel cage induction generator can be represented by two schemes (Figure 6). From the no-load test and the locked rotor test of the induction motor, we obtain the following parameters: L_sg = L_rg = 229 mH, L_mg = 217 mH, R_sg = 2.2 Ω, R_rg = 2.68 Ω. For motoring applications, these parameters can be used directly. The definitions of the different parameters are indicated at the end of the article. For the SEIG application, the variation of mutual inductance L_mg with voltage should be taken into consideration. The SEIG model can be represented by the following set of equations (Bašić et al., 2010)

{\begin{cases} s i_{s α} = \frac{1}{σ_{g} L_{s g} L_{r g}} (L_{m g}^{2} ω_{r} i_{s β} - L_{r g} R_{s g} i_{s α} + L_{m g} ω_{r} L_{r g} i_{r β} + L_{m g} R_{r g} i_{r α} - L_{r g} U_{s α} - L_{m g} K_{r α}) \\ s i_{s β} = \frac{1}{σ_{g} L_{s g} L_{r g}} (- {L_{r}}_{g} R_{s g} i_{s β} - {L^{2}}_{m g} i_{s α} + L_{m g} R_{r g} i_{r β} - L_{m g} ω_{r} L_{r g} i_{r α} - L_{r g} U_{s s β} - L_{m g} K_{r β}) \\ s i_{r β} = \frac{1}{σ_{g} L_{s g} L_{r g}} ({L_{m}}_{g} R_{s g} i_{s β} + L_{s g} ω_{r} L_{m g} i_{s α} - L_{s g} R_{r g} i_{r β} + L_{s g} ω_{r} L_{r g} i_{r α} + L_{m g} U_{s s β} - L_{s g} K_{r β}) \\ s i_{r α} = \frac{1}{σ_{g} L_{s g} L_{r g}} ({L_{m}}_{g} R_{s g} i_{s α} - L_{s g} ω_{r} L_{m g} i_{s β} - L_{s g} ω_{r} L_{r g} i_{r β} - L_{s g} R_{r g} i_{r α} + L_{m g} U_{s s α} - L_{s g} K_{r α}) \end{cases}

(8)

Figure 6.

The d–q model of SEIG at no load: (a) d-axis and (b) q-axis.

The magnetizing curve, also known as the excitation curve, shows the relation between the terminal voltage and the magnetizing current required by the machine. The terminal voltage is given as a function of the magnetizing current at no load at a given frequency. The curve is related to the machine properties and can vary from machine to machine, but the general shape is the same. The curve can be found from operating the IM as a motor at no load. It is important that this should be done at a constant frequency, as the characteristics of the curve change with frequency. Then we deduce the characteristic of the magnetizing inductance, L_mg, at a rated voltage.

In the SEIG, the variation of magnetizing inductance is the main factor in the dynamics of voltage buildup and stabilization. In this investigation, the magnetizing inductance is determined by driving the IM at a synchronous speed and taking measurements when the applied voltage was varied from 0% to 120% of the rated voltage with rated frequency.

It is necessary to use high-precision devices. The computed power will be erroneous if the accuracy of voltage and current measurements is poor. This is especially important because the magnetizing inductance for voltages and currents close to zero is used in the calculation for the initial self-excitation process.

The curve in Figure 7 shows the variation of the magnetizing inductance, measured at rated frequency, for the IM, and used in all the works developed in this article.

Figure 7.

Magnetizing inductance of the induction generator.

PMSG model

The PMSG model can be written, in the d–q synchronously rotating reference frame, by the following equation system (Chen et al., 2003; Yin et al., 2007)

\frac{d i_{d}}{d t} = - \frac{R_{d}}{L_{d}} i_{d} + \frac{L_{q}}{L_{d}} p ω i_{q} + \frac{1}{L_{d}} u_{d}

(9)

\frac{d i_{q}}{d t} = - \frac{R_{a}}{L_{q}} i_{q} - \frac{L_{d}}{L_{q}} p ω i_{d} - \frac{1}{L_{q}} p ω φ_{m} + \frac{1}{L_{q}} u_{q}

(10)

where d and q represent the synchronous rotating reference frame; $R_{a}$ is the armature resistance; $L_{d}$ and $L_{q}$ are the generator inductance values in the d–q axis; $i_{d}$ and $i_{q}$ are, respectively, the d- and q-axis components of current; $u_{d}$ and $u_{q}$ are the d- and q-axis voltage components, respectively; p is the pole pair number; and $φ_{m}$ is the permanent magnet flux.

In the d–q synchronously rotating reference frame, the electromagnetic torque is represented by (Rapin and Noel, 2010)

T_{e m} = \frac{3}{2} n ((L_{d} - L_{q}) i_{d} i_{q} + φ_{m} i_{q})

(11)

Control strategy: proportional–integral controller and sinusoidal pulse width modulation

Figure 4 shows the inverter circuit. This circuit consists of three half-bridges, which are mutually phase-shifted by an angle of 2π/3 to generate the three-phase voltage waves.

For a three-phase inverter, the controls of the switches of one arm are complementary. For each arm, there are two independent states:

$S_{1, 2, 3} = 1$ indicates that the switch on the upper half arm (1, 2, or 3) is close;

$\bar{S_{1, 2, 3}} = 0$ indicates that the switch on the lower half arm (1, 2, or 3) is open.

The diagram of a three-phase inverter feeding the IM is shown in Figure 6. The model of the three-phase inverter can be represented by the following equations

U_{a n} = \frac{U_{0}}{3} (2 S_{1} - S_{2} - S_{3})

(12)

U_{b n} = \frac{U_{0}}{3} (- S_{1} + 2 S_{2} - S_{3})

(13)

U_{b n} = \frac{U_{0}}{3} (- S_{1} - S_{2} + 2 S_{3})

(14)

The PWM technique is used to control the inverter. An isosceles triangle carrier wave of a high frequency f_p is compared with the sinusoidal modulating wave of low frequency, and the points of intersection determine the switching points of power devices. For example, for the half-bridge consisting of S₁ and S₂, the V_rms voltage adjusted to the terminal of the load is obtained by comparing a triangular wave (carrier) of high frequency with three sinusoidal modulating fixed low frequency and a variable amplitude. Figure 8 shows the control strategy.

Figure 8.

Three-phase DC/AC inverter.

The reference modulation voltages are given by

\begin{array}{l} V_{a r e f} = V_{m} \sin (2 π f t) \\ V_{a r e f} = V_{m} \sin (2 π f t - \frac{2 π}{3}) \\ V_{a r e f} = V_{m} \sin (2 π f t - \frac{4 π}{3}) \end{array}

(15)

The conventional proportional–integral (PI) controller is used to adjust the modulation depth as shown in Figure 9 with

r = \frac{V_{m}}{V_{p}}

(16)

where $V_{m}$ is the amplitude of the reference signal and $V_{p}$ is the amplitude of the control signal (carrier).

Figure 9.

Control strategy of PI-based RMS voltage controller.

Performance analysis of the experimental results

In order to test the proposed structure, a dSPACE board with a TMS320F240 digital signal processor (DSP) is utilized. The dSPACE works on the MATLAB/Simulink platform (El Akhrif et al. (2017a)).

To observe the signs, we utilize Control Disk programming related to dSPACE card. The convention that gives correspondence between the PC and the card is a model DS1104.

The MATLAB/Simulink platform was created and initiated by the graphical user interface (GUI) ControlDesk. This product enables us to see the distinctive factors of the framework to be controlled progressively.

Furthermore, the majority of the deliberate measures (current and voltage) were collected utilizing LEM sensors (LEM HX15P, LEM LV25P), and both are then changed to be a voltage running from 0 to ±10 V which will be the contribution of A/D individually.

SEIG test bench

A number of experimental tests are presented in this section using laboratory 3-kW, 220-V/380-V, 50-Hz, 1400-r/min, three-phase squirrel cage IM operating as an SEIG under a variety of loading conditions.

The prime mover in the test apparatus is a 3-kW direct current (DC) machine controlled by a mentor digital DC drive. In all tests, the DC machine is used to rotate the IM at 1500 r/min before the three-phase self-excitation capacitor bank is connected to the stator terminals of the machine. The load is then switched on after the self-excitation process has been completed. The results from these tests are logged and used to demonstrate the performance characteristics of the SEIG.

PMSG test bench

The PMSG utilized as a part of this trial examination is a three-phase, 1.5-kW, 2.1-A, 415-V, 50-Hz, and 1500-r/min IM. An induction motor is used to drive the PMSG, which is a three-phase, 3-kW, four-pole squirrel cage, 7.2-A/12.5-A, 220-V/380-V, 50-Hz, and 1400-r/min IM.

An Altivar 28HU72N4 variable frequency drive (VFD) is used to change the IM speed; it is a three-phase, 4-kW, 380-V/500-V, and 13.0-A/11.8-A IM. The experimental setups are presented in Figures 10 and 11.

Figure 10.

Photograph of the experimental setup (SEIG).

Figure 11.

Photograph of the experimental setup (PMSG).

Experimental results of the SEIG

Effect of three-phase AC load variation

The SEIG is driven by a DC motor. In order to observe the impact of load variation on the V_rms output voltage, the induction generator was rotated at 1300 r/min and then the load values of 1 and 2 kW were introduced, respectively, at 2.5 and 13 s. The value of the V_rms reference voltage is set at 120 V. We note that the proposed control strategy will maintain almost constant V_rms voltage despite the variation of the load (Figure 12).

Figure 12.

SEIG rotor speed, SEIG stator current variation, SEIG stator voltage, and V_rms voltage in load test.

Effect of SEIG rotor speed variation

In this case, the load was kept constant and the speed of rotation of the SEIG was varied. We keep V_rms reference voltage at 120 V. It is noted that even in the presence of the speed variation, the V_rms voltage is almost constant (Figure 13).

Figure 13.

SEIG rotor speed, SEIG stator current, SEIG stator voltage, and V_rms voltage in speed variation test.

V_rms output voltage regulation

To test the performance of the proposed control strategy, and under the same conditions as above we varied the reference V_rms voltage. We find that during these experiments the output V_rms voltage measured perfectly follows the V_rms reference voltage with good dynamic. Other features of the machine maintain appropriate values (Figure 14).

Figure 14.

V_rms voltage regulation, SEIG stator current, SEIG stator voltage, and SEIG rotor speed.

Experimental results of the PMSG

To test the accomplishment of the proposed examine, stand-alone generation system is experienced when the PMSG is directly connected to the load (Case I: S₁ ON, S₂ and S₃ OFF) and when the PMSG feeding an inverter which is connected with a RL Load (Case II: S₁ OFF, S₂ and S₃ ON).

Response of the system when the PMSG is used directly

Starting of the PMSG

To start excitation, we use a VFD connected to the IM; we change the IM speed from 0 to 1350 r/min, and Figure 15(a) to (f) shows the output variation of the voltage, current, and frequency.

Figure 15.

PMSG system performance under a fixed wind speed.Ua: generated volatge; Iabc: generated currents; Ω: rotor speed; f: frequency.

Consequence of the wind speed variation

We change the IM speed, respectively, as 1250, 1000, 1400, and 1000 r/min. Figure 16(a) to (d) shows the output variation of the voltage, current, and frequency. As we can see, the system performance is varying notably with change in the rotor speed of the PMSG.

Figure 16.

PMSG system performance under varying wind speed.

Consequence of the load change

At this time, we fix the wind speed at 1500 r/min and add a resistive load (Figure 17). The experimental results in Figure 8 show that the output voltage changes the profile when we connect more loads and the current values increase.

Figure 17.

PMSG system performance under load change.

These above results show that, in view of the use in an autonomous wind system, PMSG cannot be used directly with variation of wind speed and consequently cannot feed a variable load; these changes will affect the fluctuation of the voltage and frequency. Indeed, even for a fixed value of wind speed the voltage variation is about 12% (Figure 8) and the frequency variation of 4%.

Experimental results with the proposed PI regulation

Response of the PMSG under a fixed wind speed

The PMSG rotates at 1350 r/min, and then we start the regulation with the V_rms reference value of 250 V; Figure 18 shows the measured performance in the output of the VSI. So we can clearly see that the proposed PI controller maintains the output voltage of the stand-alone generator at 250 V_rms (El Akhrif et al., 2017).

Figure 18.

PMSG system performance with PI regulation under a constant wind speed.

Effect of wind speed

In order to verify the effect of wind speed, we apply the rotor speed as illustrated in Figure 10(a). Figures 19(a) and (b) and 20(a) and (b) demonstrate the attained results. It can be seen that the proposed PI controller keeps up the V_rms at 220 V_rms and the voltage and current waveforms change as indicated by the speed profile.

Figure 19.

PMSG system performance with PI regulation under a variable wind speed.

Figure 20.

PMSG system performance with PI regulation-b.

Step change in the set

The induction generator rotates at 1350 r/min and then we change the reference V_rms value as indicated by the accompanying esteems: 150 and 250 V at 0 and 4 s.

Figure 21(b) demonstrates the deliberate V_rms in the output of the VSI and from Figure 21(a) and (c) we can observe the produced voltage and current. So we affirm that the proposed PI controller demonstrates its quality to control the magnitude of the voltage of independent generator to keep the voltage at the desired value with good precion.

Figure 21.

PMSG system performance with PI regulation under set change.

Results under the change of the load

To check the impact on the proposed control, we applied no working load and after that a resistive load was applied (500 W at 8 s and 800 W at 12.5 s). As can be seen, the yield voltage diminishes (Figures 22 and 23) and the yield current of the load when we introduce the of load increments, while the V_rms still constant at 150 V anyway the variation of source and use of loads.

Figure 22.

PMSG system performance with PI regulation under load change.

Figure 23.

PMSG system response with PI regulation under load change.

To better appreciate the differences between the direct connection of PMSG with the load and the proposed control system in the configuration of the wind system, the first series of tests is carried out with the variation of wind speed imposed on the machine. The duration over which the measurements are made (18 s) and the disturbances induced by the load make it possible to distinguish the behavior between the proposed regulator and the direct system, and it can be seen that the system is perfectly following the instruction. The second series of tests concerned the variation of the load while maintaining a fixed wind speed; this test aims to confirm the robustness of the system under different load configurations. It can be said that the proposed system can function under different constraints encountered during the direct operation.

Conclusion

This article has presented a comparison study of stand-alone small PMSG- and SEIG-based wind energy conversion systems. A complete implementation of the systems is described and the required control strategy is defined for each system. The proposed control framework depends on a classical PI controller. In this issue, we assessed the performance of the voltage control under a variety of conditions, so we examine the impact of variation of wind speed symbolized here by variation of rotor speed and furthermore the impact of the change of the AC load. The dSPACE experimental results were shown to affirm the viability of the proposed structure. In the future examination, we have to contrast our regulator implementation. So we examine a fuzzy logic controller in the same exploratory settings.

The problem addressed in this work allowed us to study two modes of operation of PMSG used in wind energy systems and the second setup concerns the SEIG in autonomous operation for an isolated load. It is found that both of the systems are able to sustain V_rms at the desired value, thus ensuring the variable speed operation and with different loads. It is found that experimentally an SEIG-based system could provide higher efficiency than a PMSG-based system, so SEIG can be considered to be a better option for small wind energy conversion systems.

Given these observations and the results obtained, interesting prospects that can contribute to improving the operation of the SEIG–PMSG converter device are conceivable: generator reactions to load imbalances and interaction in the same conversion system (SEIG associated with PMSG).

Footnotes

Appendix 1 Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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dSPACE implementation for a proportional–integral-based root mean square voltage controller used in stand-alone wind energy conversion systems

Abstract

Keywords

Introduction

The design of the proposed control

Equation model of the wind turbine

Wind model

Aerodynamic and mechanical model

SEIG model

PMSG model

Control strategy: proportional–integral controller and sinusoidal pulse width modulation

Performance analysis of the experimental results

SEIG test bench

PMSG test bench

Experimental results of the SEIG

Effect of three-phase AC load variation

Effect of SEIG rotor speed variation

Vrms output voltage regulation

Experimental results of the PMSG

Response of the system when the PMSG is used directly

Starting of the PMSG

Consequence of the wind speed variation

Consequence of the load change

Experimental results with the proposed PI regulation

Response of the PMSG under a fixed wind speed

Effect of wind speed

Step change in the set

Results under the change of the load

Conclusion

Footnotes

Appendix 1

Declaration of conflicting interests

Funding

References

V_rms output voltage regulation