Optimum active power tracking based control of brushless doubly-fed reluctance generator tied to renewable microgrid

ABSTRACT


INTRODUCTION
A brushless doubly-fed reluctance machine is an updated form of induction machine involved in power generation from wind.Although there is a similarity between the doubly-fed induction generator (DFIG) and brushless doubly-fed reluctance generator (BDFRG), in comparison to DFIG, BDFRG is gaining popularity because of its simple construction, robustness in control, and improved efficiency [1]- [3].The 423 secondary winding is connected to the grid through a bi-directional converter, which ensures power flow between the primary and secondary winding [4].It has a high saliency reluctance rotor and the numbers of salient poles are equal to the total number of pole pairs in the stator [5].Due to the absence of brushes and slip rings, the performance and efficiency are better and make this a good contender for wind power generation applications, which substitute the DFIG.There are various existing control techniques available for the active and reactive powers of the BDFRG.A flux-oriented vector control mechanism for BDFRG is presented in [6], [7].Different scalar control strategies are presented in [8], [9].Also, different vector control techniques are presented in [10]- [15].However, none of the authors have mentioned the optimum active power tracking of the controller for the minimization of copper losses both in the primary and control winding of the BDFRG.In this paper, an optimum active power tracking-based control mechanism has been presented for minimum copper losses of BDFRG.Initially, the reference active power is computed for minimum copper losses of the machine with the help of measured and known variables.Then the instantaneously computed active power is allowed to track its reference counterpart.As there is much similarity in the rotating principle between the DFIG and BDFRG, therefore in this paper, an MRAS-based rotor position and speed estimation technique is implemented for sensor less control as presented in [16]- [20].The presented MRAS technique used for rotor position and speed computation of BDFRG considers the active power of the machine as the adjustable variable and is not sensitive to the machine parameter variations unlike the mechanism presented in [21]- [24].The overall proposed scheme is shown in Figure 1.The highlights of the proposed control technique can be summarized as follows: i) Unlike the earlier BDFRG control techniques, the proposed control technique improves efficiency.
ii) The secondary winding flux position computation algorithm developed is non-sensitive to the machine parameter variations.Thus accurate.iii) The algorithm developed for the secondary winding flux position computation does not uses differentiators and integrators.Thus, putting less burden on the processor.iv) The algorithm developed for the secondary winding flux position computation exhibits the property of catch-on-fly.Thus, robust for wide wind speed variation applications.

PROPOSED CONTROL MECHANISM 2.1. Modeling of BDFRG
Unlike the DFIG, the brushless doubly-fed reluctance generator consists of two sinusoidal distributed winding in the stator.While the primary winding works at grid frequency, the control or secondary winding operates at a different frequency i.e. at the rotor frequency of the BDFRG.The d-q axis primary winding voltage expression is presented in (1) and that of secondary or winding in (2) in the synchronous reference frame.The vector positions of different variables are shown in Figure 2.

Modeling of the controller
The rating of the BDFRG is known for its active power generated at the primary winding.The stator's active power will remain always at 1p. u.During super synchronous operation, when the active power exceeds 1p.u, the excess active power of the machine will be supplied to the grid through the secondary winding.Similarly, during subsynchronous operation of the BDFRG, to maintain primary winding active power at 1p. u, the shortage of power will be recirculated through the secondary winding and the marginal active power loss due to power circulation is generally compensated by the microgrid.This power exchange process through the secondary winding will be accomplished through the secondary side converter (SSC) control mechanism with the help of the secondary grid side converter (GSC).
The primary active power of the BDFRG can be as (5).
) (      +       ) As the resistive drop in the primary winding is insignificant to the terminal voltage, at steady-state, (1) will be reduced to (6).
Substituting, ( 6) in ( 5), the primary winding active power can be expressed in terms of primary flux as (7).
The BDFRG copper loss considering both primary and secondary winding can be written as (9).

425
For minimum copper loss at optimum flux as (11).
Which is the condition for the minima.Substituting, (11) in (8), the optimum value of the primary winding active power can be computed for known values of primary frequency and primary winding self-inductance as (12).The proposed scheme is shown in Figure 3, where the SSC is controlled through two control loops.In loop-1, the instantaneous primary active power as in (5) tracks its optimum counterpart as computed in (12).Therefore, the total copper losses in the BDFRG minimize at all operating points through loop-1 control.Similarly, the primary winding terminal rated voltage level is ensured through the loop-2 control as shown in Figure 3.Where the instantaneous measured voltage is tracking its reference counterpart.In the proposed control scheme, the secondary flux position has been measured accurately through the proposed sensorless technique as presented in section 3. It can be observed from Figure 3 that   =   −   is a piece of vital information that majorly decides the accuracy of the control.Therefore,   must be computed accurately.The pulse width modulation (PWM) technique is used to control the bidirectional power exchange between the DClink and the secondary winding of the BDFRG as shown in Figure 3. Similarly, the GSC is controlled through two control loops.While loop-1 ensures the constant DC-link voltage, loop-2 ensures the power factor close to 1 of the generation system.

COMPUTATION OF STATIONARY FLUX POSITION
It is already said, the accuracy of all the existing controllers including the proposed one is highly dependent on the secondary flux position information of the BDFRG.In this paper, a model reference adaptive system (MRAS) based secondary flux position computation scheme has been proposed and the same has been implemented using the proposed controller.In this MRAS model, primary winding active power is considered to be the adjustable variable.Considering (5), and substituting (3) in (5) for    = 0,    ≈ 0.
* = ( ≈ 0,  called the primary winding leakage factor.For the proposed MRAS, the reference primary adjustable active power  ̂ is compared with its reference counterpart   * .The error signal generated from the comparison is processed through a PI controller for the computation of secondary flux rotating speed   .The same has been integrated to compute the position of the secondary flux position   as shown in Figure 4.The proposed MRAS model has been tested using the MATLAB-Simulink software platform and the performance is shown in Figure 5. Regarding parameter sensitivity, the factor which can influence the adjustable variable in ( 13) is the factor (1 +   ) = .Assuming, variation in   by   which changes the influencing factor  to  + .Therefore,  +  = 1 + Therefore, the proposed MRAS model remains almost unbiased from BDFRG magnetizing inductance variations.To validate the above statement, the m L value has been increased by 50% and the performance of the scheme is tested.It has been observed from Figure 5(a) that the proposed MRAS model computes the position of the secondary winding flux with almost zero computational error.Similarly, from Figure 5(b), it can be observed that the system exhibits excellent performance for a reduced magnetizing inductance by 50%.

THE TEST RESULTS
Two 500 KVA transformers in parallel feeding power to the power pool along with an 800 KVA diesel generator with variable domestic and institutional loading is considered to be the microgrid for this experiment.A prototype experimental setup consists of a 2.5 kW BDFRG, a 5 kW DC separately excited motor used as the wind turbine emulator, a 1 kW back-to-back SSC and GSC connected between the secondary and primary winding of the BDFRG with necessary control arrangement, a dSPACE (CP1104) module along with an appropriate PC interface, 3 numbers DSOs (214 C, 1000 MHz), a power analyzer (Fluke 435) and a 2 kW local R-L load augmented with BDFRG terminals as shown in Figure 6.Also, the proposed scheme is simulated using MATLAB/Simulink.

427
Out of the many simulation results, some of the relevant results are presented here to validate the effectiveness of the proposed controller.The output of the proposed MRAS scheme is shown in Figure 7 where the computed speed of the secondary flux is shown in Figure 7(a) during the speed transition.To test the catchon-fly behavior of the proposed MRAS scheme, the rotor speed is varied from sub synchronous speed to super synchronous speed through synchronous From Figure 7(b), it can be observed that the proposed model precisely computes the secondary flux speed with insignificant estimation error as shown in Figure 7(c).The corresponding computed secondary flux position can be observed from Figure 7(d) near synchronous speed with almost zero computational error as shown in Figure 7(e).Out of the total feasible results captured to validate the performance of the proposed controller, four relevant results are presented as shown in Figure 8.As it is already said, irrespective of the rotor speed within the operating range, the primary active power remains at 1 p.u.The same can be observed in Figure 8(a).Where the active power remains constant at 2.2 kW even though the turbine speed varies randomly within the operating range.As the turbine output power varies, the quantity and direction of active power at the secondary winding of the BDFRG through the DC-link will also vary proportionately.Therefore, the net power supposed to be fed to the microgrid will vary.The same can be observed in Figure 8(b) and Figure 8(c).Since the proposed scheme has been designed to operate at unit power factor (UPF), it has been observed that the reactive power delivery for the proposed scheme is almost negligible as shown in Figure 8(d).From the input and output power information, with known values of the constant losses, the copper loss for the BDFRG has been computed with and without using the proposed controller.The same has been provided in Table 1.It has been observed that the proposed controller effectively improves the efficiency of the machine as the average copper loss is reduced almost by 0.68%.For wind energy-dominated microgrids, the energy saved through improved efficiency not only improve the economic sustainability of the generation companies (GENCOs) but also considerably reduces the maintenance cost of the system.Moreover, for the BDFRGs of higher ratings, the improved efficiency operation will increase the life expectancy of the generator.

CONCLUSION
The optimum active power tracking-based BDFRG control algorithm has been developed and the same has been successfully implemented.The mathematically developed SSC controller has been simulated and the results observed indicate the effectiveness of the scheme.The tested scheme has been implemented with the mentioned hardware with a virtual microgrid system.The proposed scheme exhibits significant improvement in the efficiency of the BDFRG over the entire operating range.It has been observed that the proposed scheme reduces average copper loss almost by 0.68%.Though the scheme has been implemented with a prototype system, it can be extended to higher-scale systems for better performance.Even though it is beyond the scope of this paper, for the efficiency improvement of BDFRG, only copper loss has been considered as the objective function, however, total loss of the BDFRG can be considered as the objective function to improve the efficiency further.Therefore, the proposed scheme will increase the active power generation capability, reduce maintenance costs, and increase the generator's life expectancy.

Figure 1 .
Figure 1.Overall proposed wind energy-based generation scheme

Figure 2 .
Figure 2. Primary and secondary winding variables phasor

Figure 3 .
Figure 3. Details of the proposed control mechanism

Figure 8 .
Figure 8. Performance of the controller; (a) primary active power in response to the variation of BDFRG rotor speed, (b) response of net active power generated from the BDFRG as the turbine output varies randomly, (c) secondary winding active power variation of BDFRG Primary winding as the turbine output varies randomly, and (d) power measured at the primary winding terminals of BDFRG