Performance of a Direct Power Control System Using Coded Wireless ...

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Performance of a Direct Power Control System Using Coded Wireless OFDM Power Reference Transmissions for Switched Reluctance Aerogenerators in a Smart Grid Scenario Carlos Eduardo Capovilla, Member, IEEE, Ivan Roberto Santana Casella, Member, IEEE, Alfeu J. Sguarezi Filho, Member, IEEE, Tarcio Andre dos Santos Barros, Student Member, IEEE, and Ernesto Ruppert Filho, Member, IEEE Abstract—This paper presents a performance analysis of a wireless direct power control system for switched reluctance aerogenerators. Aiming at a smart grid scenario, the utilization of wireless technologies for transmitting control information requires a powerful modulation and errorcorrection coding schemes to avoid any serious problems to the energetic plant. These transmission errors can cause permanent damages in the components of the turbine and converters, and they can also compromise the quality of the energy delivered to the grid. The performance of the proposed system is investigated in a frequency-selective fading channel, thus enabling a deeper study of the impact of the use of wireless communications for reference signal transmissions. This research demonstrates the operational viability of wireless systems for this type of application, when an appropriate digital modulation and coding techniques are applied. Index Terms—Low-density parity check (LDPC), orthogonal frequency-division multiplexing (OFDM), power control, smart grid, switched reluctance generator (SRG), wind energy, wireless communications.

I. I NTRODUCTION

R

ECENTLY, the renewable power grids that carry electricity generated by wind, solar, and tidal sources have received new investments to turn out to be feasible and to optimize their use based on the concept of smart grids [1]. Among all sources of electric energy applied to this new one, wind generation has emerged as one of the most promising techniques and has been the focus of several recent scientific work [2]–[6].

Manuscript received September 1, 2013; revised January 13, 2014 and March 31, 2014; accepted April 24, 2014. Date of publication June 13, 2014; date of current version December 19, 2014. This work was supported in part by FAPESP. C. E. Capovilla, I. R. S. Casella, and A. J. Sguarezi Filho are with the Universidade Federal do ABC (UFABC), 09090-400 Santo André, Brazil (e-mail: [email protected]; [email protected]; [email protected]). T. A. dos Santos Barros and E. Ruppert Filho are with the Universidade Estadual de Campinas (UNICAMP), 13083-970 Campinas, Brazil (e-mail: [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIE.2014.2331017

In wind power generation, the induction and synchronous electric machines are the most widely used as electrical energy generators [7], [8]. These generators may operate with fixed or variable speed, depending on the use or not of power electronic converters. An alternative machine used in wind power generation for microgrids or isolated systems with relatively low loads is the switched reluctance machine (SRM) [9]–[12]. The switched reluctance generator (SRG) has the following main characteristics: mechanical robustness, high starting torque, high efficiency, and low cost [13]–[15]. The SRG is intrinsically a machine that produces unidirectional pulsed current and can perform this operation in the regime of fixed or variable speed. Therefore, its use can reduce the weight of the gearboxes employed in wind turbines. Since the SRG can operate at variable speeds, its operating range is wider than the induction and synchronous generators [13], [16], [17]. The behavior of the SRG in variable speed mode is presented in this paper [13], [18], [19]. For a successful smart grid implementation, it is necessary to develop a complete telecommunications framework, with a strong interaction, composed of communication networks, data management, and real-time monitoring applications. In particular, the application of a modern telecommunication system for controlling and monitoring requires a complex infrastructure for an efficient operation [20]–[22], and its development and operability present several nontrivial issues due to the convergence of different areas of knowledge and design aspects. In this way, wireless communications appear as an interesting solution for offering many benefits, such as low cost of development, expansion facilities, possibility of using the technologies currently applied in mobile telephone systems, flexibility of use, and distributed management. However, wireless transmissions are subject to distortions and errors caused by the propagation channel [23] that can cause serious problems to the controlled and monitored equipment and, thus, to the energy plant as a whole. This intrinsic problem of wireless communication systems can be circumvented through the use of forward error correction (FEC) [24]. This coding technique is used in all modern wireless digital systems and is essential to ensure the integrity of information, reducing significantly the bit error rate (BER),

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CAPOVILLA et al.: WIRELESS DIRECT POWER CONTROL SYSTEM FOR SWITCHED RELUCTANCE AEROGENERATORS

and the latency of the system by adding redundancy to the transmitted information [25]. There are currently several different schemes of FEC used in commercial wireless communication systems, for instance, the Reed Solomon (RS) coding [26], convolutional coding (CC) [27], turbo coding (TC) [28]–[30], and low-density paritycheck (LDPC) coding [31]–[33]. Among them, LDPC is the one that presents the best performance, approaching significantly to the limits set by the seminal work of Shannon [34], and shows an excellent compromise between decoding complexity and performance [32]. Although the complexity of most of the LDPC encoders is higher than the equivalent CC, some LDPC encoder families present linear complexity by properly designing their parity-check matrix. In [35], a class of efficiently encodable irregular LDPC codes that admits low-complexity encoding and has lower error-rate floors than other irregular LDPC code-design approaches is proposed. It is also shown that this class of LDPC codes is equivalent to a class of systematic serial TC and an extension of irregular repeat accumulate (IRA) codes, reason to be commonly termed eIRA [36]–[38]. This technique can improve the systematic encoding process and generate good irregular LDPC codes for high-code-rate applications, and it is employed in DVB-S2 (long-length code), IEEE 802.3an, and IEEE 802.16e. In addition, in [39], a class of irregular structured LDPC codes with low error floor and low encoding complexity, by designing the parity-check matrix in a triangular plus dualdiagonal form, is proposed. The proposed irregular codes lower the error floor and dramatically increase the performance in the waterfall region of error-rate curves. Being characterized by linear encoding complexity, the encoder of the proposed code attains throughput over 10 Gb/s. The use of partially parallel decoder architecture leads to decoders having a reduced number of clock cycles per iteration, which can result in higher decoding throughput and make possible to implement LDPC codes even with relatively long lengths. LDPC coding can present high throughput, high errorcorrection capability, and low complexity; however, latency is still an issue. Long LDPC codes are undoubtedly the best choice nowadays for high-performance applications that are not sensitive to delays, but there is no consensus regarding the best FEC coding for the same latency constraints. For this case, some works [40], [41] have pointed out that block codes cannot outperform convolutional codes. However, they did not take into account all the discussed LDPC improvement techniques in their analysis. For a simplistic approximation, LDPC decoded latency can be obtained by the code block length. For an orthogonal frequency-division multiplexing (OFDM) system (e.g., IEEE802.16e) using 10-MHz bandwidth, code rate 1/2, eIRA LDPC code (64 800;32 400), quaternary phase-shift keying (QPSK) modulation, 1024 subchannels, cyclic prefix (CP) of 128, the end-to-end can be estimated as 8.28 ms. This value is considered suitable since, for smart grid applications, distributed power control delay can be on the order of 100 or 200 ms [42], [43]. If latency is an issue, the code length or code rate can be reduced to fulfill the specifications, without significantly sacrificing the error-correction capability.

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In addition, LDPC coding has recently been added to the IEEE 802.16e Standard, commonly known as Worldwide Interoperability for Microwave Access (WiMAX) for mobile applications [44], which will most likely be one of the main wireless standards used in smart grids [45]. WiMAX is one of the most promising wireless Internet Protocol-based technologies to be applied in smart grids. Its use can be very attractive when fiber optics is unavailable to all the system, when there is some implementation constraints regarding cost and/or location (e.g., offshore wind farms and distributed microgrids, among others), or when it is required as an efficient backup communication system. Its significant network capacity associated with low latency, high flexibility, and relatively low implementation cost is obtained mainly due to the employment of OFDM modulation [25]. OFDM is a very efficient modulation scheme to high data rate transmissions in frequency-selective fading channels that can mitigate the intersymbol interference (ISI) and, consequently, reduce the BER by a simple one-tap frequency-domain equalization (FDE) [29]. Together, OFDM and LDPC can make WiMAX very robust and reliable to transmit monitoring and control information in smart grids [46]. It is worth noting that there are researches in the scientific literature referencing the application of wireless technology in multilayered architecture for end-to-end connectivity at industrial systems [47], or for monitoring wind energy systems based on sensor networks [48]–[50]; however, there has not been any deep research presented about the use of wireless technology for control applications in these systems, making it difficult to estimate the real impact of its use or its advantages and difficulties. Additionally, implementation and studies of communication systems for wireless monitoring and control applications in wind plants referenced to a remote system are presented in the papers [51], [52], demonstrating its potential applicability in the close future. Although these works bring evidences and exemplify the actual advantages and features offered by the use of wireless communications, none of them proposes or examines techniques that can ensure the reliability and security for control and monitoring information on transmission error robustness, due to the degrading effects of wireless communication channel. Thus, this paper aims to fill a gap in the literature, to demonstrate the functional viability of the use of wireless systems for this type of application when an appropriate coding technique is applied. In this context, this paper presents a performance analysis of SRG direct power control for wind energy generation, using a coded wireless OFDM communication system to transmit the power references. Fig. 1 shows the simplified diagram of the proposed system. On the other hand, the proposed wireless communication system is an improvement of the system presented in [33]. In the new scheme, OFDM modulation is combined with a powerful LDPC coding scheme to enhance the robustness of the direct power control to the occurrence of transmission errors and to reduce the overall latency of the system in frequency-selective fading channels, usually found in microgrid applications.

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Fig. 3.

AHB converter.

Fig. 4.

SRG direct power control.

Fig. 1. Wind generator with wireless power control and its smart grid connection.

inductance. Thus, the same machine can be used as motor or generator by changing the firing angle of its drive keys. III. SRG P OWER C ONTROL S YSTEM A. AHB Converter

Fig. 2.

Inductance profile.

This paper is organized as follows: SRM principles are presented in Section II; SRG power control is shown in Section III; the coded wireless communication system is presented in Section IV; the main results are considered in Section V; and Section VI concludes the work. II. SRM The SRM has field coils in the slots and has no rotor coils or magnets. The rotor is composed of ferromagnetic material with salient electromagnetic poles. The lack of windings or permanent magnets in the rotor of SRM provides a number of advantages [14], such as low-cost manufacturing and materials, up to 60% of the cost of production of dc/ac machines (equivalent), and easy maintenance/repair due to concentrate on the stator windings, among others. The operating principle of the SRM is based on changes in rotor magnetic circuit reluctance. Fig. 2 shows the profile of the SRM winding inductance. If the magnetic saturation is neglected, then the inductance will vary linearly during the alignment between the rotor poles and the stator [18], [53]. The inductance is maximum when the rotor and stator are completely aligned, and minimum when the poles are fully aligned. The operation as a motor is obtained when the phase is excited during growth of the inductance, and for operation as a generator, the machine must be excited during the decay of the

There are several power converters to drive the SRG, but the configuration most widely used is the asymmetric half-bridge (AHB) converter, as shown in Fig. 3. This converter operates in two basic steps for SRG applications: excitation and generation. The excitation step occurs when the two switches (each phase) of the SRG open. In this case, the coil current of this phase increases due to the excitation voltage. In the generation, the two switches of phase are turned off, and the current starts to circulate through the diodes to the load. At each excitation period, the bus voltage Vdc transfers energy to the magnetic field of the corresponding phase. When the switches are opened (period of generation), this energy flows to the load or to the grid by using an inverter. B. Direct Power Control The proposed direct power control system consists in controlling the power generated by SRG directly, following the power reference received from the operator of the system. The maximum power reference value is generated at the point of maximum aerodynamic efficiency, in other words, at Popt = kopt wr3 , where Popt is the maximum reference of active power; kopt depends on the blade shape, on the gear box, and on the wind turbine parameters; and wr is the rotor speed of rotation (in radians per second) [54]. The diagram of the direct power control is shown in Fig. 4, and the control consists in keeping the activation angle of the AHB converter switches at a fixed value θon . The proportional–integral (PI) [55] controller processes the error (eP ) between Pref and the generated power P controlling the shutdown angle of the switches θoff , as shown in (1).

CAPOVILLA et al.: WIRELESS DIRECT POWER CONTROL SYSTEM FOR SWITCHED RELUCTANCE AEROGENERATORS

Fig. 5.

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VSC.

The expression for the angle θoff is given by  θoff = Kp eP + Ki eP dt

(1) Fig. 6.

Block diagram of the VSC control.

Fig. 7.

Coded wireless communication diagram.

where Kp is the proportional gain and ki is the integral gain of the PI controller. C. Power Grid Connection Converter The voltage source converter (VSC), as shown in Fig. 5, controls voltage Vdc , and it allows sending the generated power by SRG to the grid. The control strategy applied to the VSC consists of two control loops. There is an internal control loop that controls the current sent to the grid, and, externally, there is a bus voltage (Vdc ) control loop. The current control loop (isd , isq ) is responsible for controlling the power factor of the power sent to the grid [56]. The control voltage of the dc link is responsible for balancing the flow of power between the SRG and the grid [57]. The dc-link voltage (Vdc ) control of the VSC is realized in the synchronous coordinate system (dq) with employment grid voltage angle (θ = wt) used in the transformation abc dq, which is obtained using a phase-locked loop (PLL). The one guarantees the synchronization between the electrical grid and the inverter. With this finality, it calculates the grid angle through the closed control loop using the direct-axis component of the grid voltage and the calculated angle. The PLL is performed by a PI controller [58], which comes from the reference value i∗sd , while the value of i∗sq is derived from the power factor desired F P and Pref , as follows:  ∗ ∗ ∗ − Vdc ) dt (2) isd = Kpi (Vdc − Vdc ) + Kii (Vdc i∗sq

−3 ˆ Pref = 2

√

1 − FP2 . FP2

(3)

The reference values of current are compared with the values obtained from the electrical grid (isd and isq ), and they are processed by two PI controllers that generate the value of the grid space vector voltage vdq in the synchronous coordinate system, as follows:  (4) vds = Kps (i∗sd − isd ) + Kis (i∗sd − isd )dt   vqs = Kps i∗sq − isq + Kis



 i∗sq − isq dt.



(5)

This space vector is transformed for the coordinate system abc, providing the signal voltage v mod abc , which are, then, generated using the sinusoidal pulsewidth modulation. The control system for VSC is shown in Fig. 6. IV. C ODED W IRELESS C OMMUNICATION The proposed coded wireless communication, as shown in Fig. 7, uses LDPC coding [31], [32] and OFDM modulation [25] schemes to improve system performance and reliability in frequency-selective fading channels [29]. LDPC codes are (Nc , Nb ) binary linear block codes that have a sparse parity-check matrix H that can be described in terms of a Tanner graph [59], where each bit in the codeword corresponds to a variable node and each parity-check equation corresponds to a check node. A check node j is connected to a variable node k whenever the element hj,k in H is equal to 1 [27], [59]. The adopted coding process is based on eIRA codes [36]– [38]. The eIRA parity-check matrix can be represented by H = [ H1

H2 ]

(6)

where H1 is a sparse (Nm ) by (Nc ) matrix, which can be constructed irregularly by density evolution according to optimal weight distribution [37]; H2 is the (Nm ) by (Nm ) dualdiagonal square matrix; Nb is the number of control bits; Nc is the number of coded bits; and Nm is the number of parity bits.

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Given the constraint imposed on the H matrix, the generator matrix can be represented in the systematic form by the (Nb ) by (Nc ) matrix, as follows: G = [I

Ψ]

(7)

−T where I is the identity matrix, Ψ = HT1 × H−T is 2 , and H2 the upper triangular matrix. Otherwise, OFDM is a very efficient modulation technique for fading channels. The core of its structure is the inverse fast Fourier transform (IFFT) and fast Fourier transform (FFT) operations performed, respectively, in the transmission and reception processes. By combining IFFT/FFT and CP, OFDM can convert a frequency-selective fading channel into multiple orthogonal frequency flat-fading channels and can mitigate the ISI and, consequently, reduce the BER with a simple one-tap FDE [29]. Together, OFDM and eIRA are a very powerful choice to improve the performance of the wireless control system proposed in this paper. The encoding process can be accomplished by first multiplying the control information vector qb = [qb (0) · · · qb (Nb −1)]T by the sparse matrix HT1 and then by differentially encoding this partial result to obtain the parity bits. The systematic codeword vector qc = [qc (0) · · · qc (Nc −1)]T can be simply obtained by combining the control information and the parity bits. In the transmission process, for each transmitted frame, the codeword vector is interleaved and mapped to QPSK symbols by using Gray coding [25] to improve system throughput. The resulting vector, which is composed of Ns QPSK symbols, is then split in the V subvectors of Nf QPSK symbols qvs = [qsv (0) · · · qsv (Nf − 1)]T , where 0 ≤ v ≤ V − 1. Thereafter, each subvector is subjected to an operation of IFFT to result in the corresponding OFDM symbol vector qvf = [qfv (0) · · · qfv (Nf − 1)]T . CP symbols are added to each OFDM symbol vector to decouple the transmissions in the frequency domain at the receiver (converting a linear convolution in a circular convolution operation) [29], resulting in the transmit symbol vector sv = [sv (−Ncp ) · · · sv (Nf − 1)]T , where Ncp is the number of CP symbols. Finally, the frame composed of all the transmit symbol vectors is upconverted, filtered, and transmitted by the antenna. In the reception process, considering that the CP duration is longer than the delay spread of the fading channel, the complex low-pass equivalent discrete-time demodulated signal for each transmit vector, after the operations of CP removing and FFT, can be represented by [29]

qvr = Ξv · qvs + Wv

(8)

where qvr = [qrv (0) · · · qrv (Nf − 1)]T is the received vector; Ξv = [ξ v (0) · · · ξ v (Nf −1)]T is the frequency response vector related to the time-varying frequency-selective fading channel; and Wv = [W v (0) · · · W v (Nf − 1)]T is the frequencydomain additive white Gaussian noise vector. Note that vector multiplications are performed element by element. Once each transmitted QPSK symbol vector qvs is estimated, considering one-tap FDE based on least squares (LS) criteria

[29], the transmitted control bits can be recovered by performing symbol demapping, code deinterleaving, and bit decoding. Decoding can be accomplished by a message-passing algorithm [60]–[63] based on the maximum a posteriori criterion [27], which exchanges soft-information iteratively between the variable and check nodes. The exchanged messages can be represented by the following log-likelihood ratio (LLR):   p (qc (k) = 0|d) (9) Lck = log p (qc (k) = 1|d) where d is the vector of coded bits obtained by the processes of demodulation and deinterleaving. The LLR message from the jth check node to the kth variable node is given by  

Lqk ,j . (10) Lrj,k = 2 atanh Π tanh 2 k ∈Vj\k The set Vj contains the variable nodes connected to the jth check node, and the set Ck contains the check nodes connected to the kth variable node. Vj\k is the set Vj without the kth element, and Ck\j is the set Ck without the jth element. The LLR message from the kth variable node to the jth check node is obtained by  Lqk,j = Lck + Lrj  ,k (11) j  ∈ck\j

and the LLR for the kth code bit is given by  LQ k = L c k + Lrj,k .

(12)

j∈ck

At the end of each iteration, LQk provides an updated estimate of the a posteriori LLR of the transmitted coded bit qc (k). If LQk > 0, then qˆc (k) = 1, else qˆc (k) = 0. V. P ERFORMANCE A NALYSIS For the analysis presented here, the sampling time is 1μs, and the SRG parameters are presented in the Appendix. The simulation was performed on the MATLAB/SIMULINK package (SimPowerSystems and Communications System toolboxes). The power references have a step waveform with variations of the active power and the power factor, according to the following pattern: The active power and the power factor start at 2.5 kW and 1.0, respectively. In the time instant of 2.5 s, the active power and the power factor are changed to 5 kW and 0.85, respectively. Again, in the time instant of 3.5 s, the active power and the power factor are changed to 4 kW and −0.85, respectively. Finally, in the time instant of 4.0 s, the active power and the power factor return to their initial values. Additionally, the rotor speed has the following pattern: The speed starts at 1250 r/min. In the time instant of 2.5 s, it is changed to 1625 r/min. In the time instant of 3.5 s, it is changed to 1425 r/min. Finally, in the time instant of 4.0 s, it is changed back to 1250 r/min.

CAPOVILLA et al.: WIRELESS DIRECT POWER CONTROL SYSTEM FOR SWITCHED RELUCTANCE AEROGENERATORS

Fig. 8.

Power reference employing OFDM-CC.

Fig. 10.

DC control voltage employing OFDM-CC.

Fig. 9.

Power factor reference employing OFDM-CC.

Fig. 11.

Grid phase voltage and current employing OFDM-CC.

Fig. 12.

THD of current phase a employing OFDM-CC.

The references are transmitted to the SRG power controller through the wireless communication system described in Fig. 7, considering a carrier frequency of 2.45 GHz. The complete system is analyzed for a frequency-selective fading Rayleigh channel with two main uncorrelated multipath components and a Doppler spread of 180 Hz. The modeling of the channel includes the effect of multipath propagation and white Gaussian noise [29]. The wireless system employs a one-tap FDE-LS based on perfect channel estimation [29] and the (64 800; 32 400) eIRA coding scheme specified in [64]. An ordinary CC scheme with a (171, 133) generator polynomial with a constrain length of 7 is used as a reference of performance [27]. Both coding schemes have a code rate of (1/2) and employ a random interleaving of length 64 800. For simplicity, the number of iterations in the LDPC decoding is limited to 25. The bit duration is 8 · 10−4 s, and each transmitted frame is composed of 36 OFDM symbols (each one obtained by the IFT of a vector composed of 900 different coded QPSK symbols and 124 zero padding symbols) with the corresponding CP (each one obtained by the last 128 elements of the respective OFDM symbol vector), where Nf is 1024 and Ncp is 128. The results presented in Figs. 8–14 consider a signal-to-noise ratio (Eb /N0 ) of 10 dB, which reflects a typical condition of operation.

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In Figs. 8 and 9, the response of the wireless power controller employing OFDM modulation and the specified CC scheme

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Fig. 13.

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Fig. 14.

Power factor employing OFDM-LDPC.

Fig. 15.

DC control voltage employing OFDM-LDPC.

Fig. 16.

Grid phase voltage and current employing OFDM-LDPC.

Power reference employing OFDM-LDPC.

(OFDM-CC) to the reference signals for active power and power factor, respectively, is presented. The spikes shown in the response of the system are the result of errors in the wireless communication, due to the destructive effects of the channel in the transmitted signal, even with the use of a very efficient error-correction scheme. These spikes cause significant variations in the active power generated by the SRG (see Fig. 8) and in the power factor of the energy sent to the electrical grid (see Fig. 9). As a result, the dc control voltage Vdc has oscillations and abrupt variations, as shown in Fig. 10, that can sharply reduce the lifetime of the capacitor of this link. Allied to this, high values of (dv/dt) can cause undue actuation of the insulated gate bipolar transistor (IGBT) switches of the power converters, resulting in short circuits in the windings of the SRG and in permanent damages. The same errors cause, additionally, distortions on the current waveforms sent to the grid (see Fig. 11), which may damage the VSC converter and generate unwanted harmonic components to the mains, as shown in Fig. 12. The grid voltage has fixed values of frequency and amplitude in normal operation. The active and reactive power control are done by isd and isq currents, as presented in [56], due to the fact that the converter is directly connected to the electrical grid. In Fig. 12, it is also observed, by analyzing the FFT of phasea current signal, that there are significant harmonic components resulting in a total harmonic distortion (THD) of 1.68%. The THD analysis can increase the value in the worse period of 60 Hz. The analysis was made in a few periods by using a MATLAB Powergui toolbox. The ranks (harmonic components) are the result of errors in the wireless communication, due to the destructive effects of the channel in the transmitted signal and also due to the fact the controller does not have robustness against these errors. Thus, it is necessary to use a wireless control system capable of minimizing the occurrence of errors caused by the propagation channel. In this context, the proposal to use a more robust wireless control system based on OFDM and LDPC stands out. Figs. 13 and 14 show the response of the wireless direct power controller employing OFDM modulation and the proposed LDPC coding scheme (OFDM-LDPC) to the

same digital waveform used as reference and described at the beginning of this section. We can be observe the high performance of the controller by noting that the power signal and the power factor are followed properly by the controller. Additionally, we can verify the robustness of the wireless power control system based on LDPC coding, since there is no occurrence of errors in the recovered references at the SRG. The voltage Vdc has only abrupt variations in the time instants of the power reference changes, as shown in Fig. 15, avoiding the reduction in the lifetime of the capacitor and the undue actuation of the IGBTs by dv/dt. Fig. 16 illustrates the

CAPOVILLA et al.: WIRELESS DIRECT POWER CONTROL SYSTEM FOR SWITCHED RELUCTANCE AEROGENERATORS

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TABLE I P ERFORMANCE C OMPARISON (BER ×Eb /N0 )

robustness of the system to the errors and distortions of the transmitted signal caused by the propagation channel. For instance, a system operating in a time-variant frequencyselective fading channel, with a typical value of Eb /N0 of 10 dB, employing OFDM-CC will fail severely, while a system employing OFDM-LDPC coding will operate error free. To summarize, Table I shows the main results in Fig. 18. VI. C ONCLUSION

Fig. 17.

Fig. 18. channel.

THD of current phase a employing OFDM-LDPC.

Performance comparison in frequency-selective fading

high quality of the energy delivered to the grid, and Fig. 17 demonstrates the reduction of harmonic distortion through Fourier analysis. To complete the analysis, Fig. 18 presents a comparison of the BER performance for different values of Eb /N0 , for the proposed OFDM communication system employing three different schemes: No Coding, CC, and LDPC. As expected, the performance of OFDM-LDPC is significantly superior than OFDM-CC for frequency-selective fading channels. As pointed out in Fig. 18, for a BER of 10−5 , the performance improvement of OFDM-LDPC over OFDM-CC is more than 12 dB and more than 30 dB over no coding. It can be noted that, even for a relatively low BER of 10−5 , there are changes in the reference signals of the active and reactive power, which can cause serious problems for the generation system. However, the use of OFDM-LDPC coding significantly reduces the required Eb /N0 for a given BER and the necessity of retransmissions, considerably improving the

In this paper, the performance of a direct power control system using coded wireless OFDM for a variable reluctance aerogenerator in a smart grid scenario has been presented. An initial implementation using OFDM modulation with a conventional CC scheme with good error-correction capability showed that, even for a relatively high Eb /N0 , the references are changed, causing significant undue variations in the active power generated by the SRG and in the power factor of the energy sent to the grid. These variations can cause permanent damages in the components of the turbine and converters, and these can also compromise the quality of the energy delivered to the grid. In this way, a specific wireless power control system is necessary to enhance the robustness of the system to the degradation imposed by the wireless communication channel. The presented analysis showed that the proposed wireless power control system employing OFDM modulation (smart grid tendency) and a powerful LDPC coding scheme offers a significant improvement of performance, even under severe noise and fading conditions, drastically reducing or even eliminating the errors in the response of the system and also reducing its overall latency. With this analysis, the operational viability of OFDM-LDPC wireless communication for power control implementations has been presented, showing that the physical integrity of the aerogenerator and the power quality delivered to the power grid can be guaranteed. A PPENDIX Main parameters of the SRG: Pn = 7.5 kW, Vn = 280 V, wn = 1500 r/min, Ns /Nr = 8/6, Rs = 253 mΩ, Lmax = 145.9 mH, Lmin = 9.15 mH, and J = 0.08 Kg · m2 . Gain of controllers: Kp = 0.001, Ki = 0.1, Kpi = 0.01, Kii = 3, Kps = 0.05, and Kis = 2. R EFERENCES [1] J. Blau, “Europe plans a North Sea grid,” IEEE Spectr., vol. 47, no. 3, pp. 12–13, Mar. 2010.

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[2] C. Cecati, C. Citro, A. Piccolo, and P. Siano, “Smart operation of wind turbines and diesel generators according to economic criteria,” IEEE Trans. Ind. Electron., vol. 58, no. 10, pp. 4514–4525, Oct. 2011. [3] M. Glinkowski, J. Hou, and G. Rackliffe, “Advances in wind energy technologies in the context of smart grid,” Proc. IEEE, vol. 99, no. 6, pp. 1083–1097, Jun. 2011. [4] J. Wang, X. Du, and X. Zhang, “Comparison of wind power generation interconnection technology standards,” in Proc. Asia-Pac. Power Energy Eng. Conf., Mar. 2011, pp. 1–4. [5] W. Xiwen, Q. Xiaoyan, X. Jian, and L. Xingyuan, “Reactive power optimization in smart grid with wind power generator,” in Proc. Asia-Pac. Power Energy Eng. Conf., Mar. 2010, pp. 1–4. [6] X. She, A. Q. Huang, F. Wang, and R. Burgos, “Wind energy system with integrated functions of active power transfer, reactive power compensation voltage conversion,” IEEE Trans. Ind. Electron., vol. 60, no. 10, pp. 4512– 4524, Oct. 2013. [7] Y. He, J. Hu, and Z. Rend, “Modelling and control of wind-turbine used DFIG under network fault condition,” in Proc. Int. Conf. Elect. Mach. Syst., Sep. 2005, vol. 2, pp. 986–991. [8] S.-K. Kim and E. Kim, “PSCAD/EMTDC-based modeling and analysis of a gearless variable speed wind turbine,” IEEE Trans. Energy Convers., vol. 22, no. 2, pp. 421–430, Jun. 2007. [9] Y.-C. Chang and C.-M. Liaw, “Establisment of a switched reluctance generator-based common dc microgrid system,” IEEE Trans. Power Electron., vol. 26, no. 9, pp. 2512–2527, Sep. 2011. [10] D. A. Torrey, “Switched reluctance generators and their control,” IEEE Trans. Ind. Electron., vol. 49, no. 1, pp. 3–14, Feb. 2002. [11] Y. Hu, X. Song, W. Cao, and B. Ji, “New SR drive with integrated charging capacity for plug-in hybrid electric vehicles (PHEVs),” IEEE Trans. Ind. Electron., vol. 61, no. 10, pp. 5722–5731, Oct. 2014. [12] F. L. M. dos Santos et al., “Multiphysics NVH modeling: Simulation of a switched reluctance motor for an electric vehicle,” IEEE Trans. Ind. Electron., vol. 61, no. 1, pp. 469–476, Jan. 2014. [13] D. McSwiggan, L. Xu, and T. Littler, “Modelling and control of a variablespeed switched reluctance generator based wind turbine,” in Proc. Univ. Power Eng. Conf., Sep. 2007, pp. 459–463. [14] R. Krishnan, Switched Reluctance Motor Drives, Modeling, Simulation, Analysis, Design and Applications. Boca Raton, FL, USA: CRC Press, 2001. [15] S. Mendez, A. Martinez, W. Millan, C. E. Montano, and F. Perez-Cebolla, “Design, characterization, validation of a 1-kW AC self-excited switched reluctance generator,” IEEE Trans. Ind. Electron., vol. 61, no. 2, pp. 846– 855, Feb. 2014. [16] X. Zhang, G. Tan, S. Kuai, and Q. Wang, “Position sensorless control of switched reluctance generator for wind energy conversion,” in Proc. AsiaPac. Power Energy Eng. Conf., Mar. 2010, pp. 1–5. [17] E. Sunan, F. Kucuk, H. Goto, H. Guo, and O. Ichinokura, “Three-phase full-bridge converter controlled permanent magnet reluctance generator for small-scale wind energy conversion systems,” IEEE Trans. Energy Convers., vol. 29, no. 3, pp. 589–593, Sep. 2014. [18] K. Ogawa, N. Yamamura, and M. Ishda, “Study for small size wind power generating system using switched reluctance generator,” in Proc. IEEE Int. Conf. Ind. Technol., 2006, pp. 1510–1515. [19] S. F. Azongha, S. Balathandayuthapani, C. S. Edrington, and J. P. Leonard, “Grid integration studies of a switched reluctance generator for future hardware-in-the-loop experiments,” in Proc. IEEE Ind. Electron. Soc., Nov. 2010, pp. 3079–3084. [20] R. Strzelecki and G. Benysek, Power Electronics in Smart Electrical Energy Networks. London, U.K.: Springer-Verlag, 2008. [21] N. Liu, J. Chen, L. Zhu, J. Zhang, and Y. He, “A key management scheme for secure communications of advanced metering infrastructure in smart grid,” IEEE Trans. Ind. Electron., vol. 60, no. 10, pp. 4746–4756, Oct. 2013. [22] T. Sauter and M. Lobashov, “End-to-end communication architecture for smartgrids,” IEEE Trans. Ind. Electron., vol. 58, no. 4, pp. 1218–1228, Apr. 2011. [23] R. Smolenski, J. Bojarski, A. Kempski, and P. Lezynski, “Time-domainbased assessment of data transmission error probability in smart grids with electromagnetic interference,” IEEE Trans. Ind. Electron., vol. 61, no. 4, pp. 1882–1890, Apr. 2014. [24] T. J. Li, “Low complexity capacity approaching schemes: Design analysis and applications,” Ph.D. dissertation, Texas A&M Univ., College Station, TX, USA, 2002. [25] J. G. Proakis, Digital Communications. New York, NY, USA: McGraw-Hill, 2008. [26] J. Jiang and K. R. Narayanan, “Iterative soft decision decoding of Reed Solomon,” IEEE Commun. Lett., vol. 8, no. 4, pp. 244–246, Apr. 2004.

[27] S. Lin and D. J. Costello, Error Control Coding. Englewood Cliffs, NJ, USA: Prentice-Hall, 2004. [28] C. Berrou, A. Glavieux, and P. Thitimajshima, “Near Shannon limit errorcorrecting coding and decoding: Turbo-codes,” in Proc. IEEE Int. Commun. Conf., 1993, pp. 1064–1070. [29] I. R. S. Casella, “Analysis of turbo coded OFDM systems employing space-frequency block code in double selective fading channels,” in Proc. IEEE Int. Microw. Optoelectron. Conf., Oct./Nov. 2007, pp. 516–520. [30] J. Chen and A. Abedi, “Distributed turbo coding and decoding for wireless sensor networks,” IEEE Commun. Lett., vol. 15, no. 2, pp. 166–168, Feb. 2011. [31] R. G. Gallager, Low-Density Parity-Check Codes. Cambridge, MA, USA: MIT Press, 1963. [32] Y. Zhang and W. E. Ryan, “Toward low LDPC-code floors: A case study,” IEEE Trans. Commun., vol. 57, no. 6, pp. 1566–1573, Jun. 2009. [33] I. R. S. Casella, A. J. S. Filho, C. E. Capovilla, and E. Ruppert, “A wireless deadbeat power control for wind power generation systems in smart grid applications,” in Proc. Braz. Power Electron. Conf., Sep. 2011, pp. 520–523. [34] E. C. Shannon, “A mathematical theory of communication,” Bell Syst. Tech. J., vol. 27, no. 3, pp. 379–423, Jul. 1948. [35] M. Yang, W. E. Ryan, and Y. Li, “Design of efficiently encodable moderate-length high-rate irregular LDPC codes,” IEEE Trans. Commun., vol. 52, no. 4, pp. 564–571, Apr. 2004. [36] H. Jin, A. Khandekar, and R. J. McEliece, “Irregular repeat-accumulate codes,” in Proc. Int. Symp. Turbo Codes Relat. Topics, Sep. 2000, pp. 1–8. [37] Y. Zhang, W. E. Ryan, and Y. Li, “Structured eIRA codes with low floors,” in Proc. Int. Symp. Inf. Theory, Sep. 2005, pp. 174–178. [38] J. Kim, A. Ramamoorthy, and S. Mclaughlin, “The design of efficientlyencodable rate-compatible LDPC codes,” IEEE Trans. Commun., vol. 57, no. 2, pp. 365–375, Feb. 2009. [39] Z. He, P. Fortier, and S. Roy, “Class of irregular LDPC Codes with low error floor and low encoding complexity,” IEEE Commun. Lett., vol. 10, no. 5, pp. 372–374, May 2006. [40] T. Hehn and J. B. Huber, “LDPC codes and convolutional codes with equal structural delay: A comparison,” IEEE Trans. Commun., vol. 57, no. 6, pp. 1683–1692, Jun. 2009. [41] M. Kaiser, W. Fong, and M. Sikora, “A comparison of decoding latency for block and convolutional codes,” in Proc. Int. Symp. Commun. Theory Appl., 2009, pp. 1–5. [42] B. Akyol, H. Kirkham, S. Clements, and M. Hadley, “A survey of wireless communications for the electric power system,” U.S. Dept. Energy, Washington, DC, USA, Jan. 2010. [43] Y. Jeon, “QoS requirements for the smart grid communications system,” Int. J. Comput. Sci. Netw. Security, vol. 11, no. 3, pp. 86–94, Mar. 2011. [44] Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed and Mobile Wireless Access Systems, IEEE Std. 802.16-2004, 2004. [45] M. Anderson, “WiMax for smart grids,” IEEE Spectr., vol. 47, no. 7, p. 14, Jul. 2010. [46] V. Sood, D. Fischer, J. Eklund, and T. Brown, “Developing a communication infrastructure for the smart grid,” in Proc. IEEE Elect. Power Energy Conf., Oct. 2009, pp. 1–7. [47] F. Salvadori et al., “Monitoring and diagnosis in industrial systems using wireless sensor networks,” in Proc. IEEE Int. Symp. Intell. Signal Process., Oct. 2007, pp. 1–6. [48] Z. H. Khan, J. M. Thiriet, and D. Genon-Catalot, “Wireless network architecture for diagnosis and monitoring applications,” in Proc. IEEE Consum. Commun. Netw. Conf., Jan. 2009, pp. 1–2. [49] M. Adamowicz, R. Strzelecki, Z. Krzeminski, J. Szewczyk, and L. Lademan, “Application of wireless communication to small WECS with induction generator,” in Proc. IEEE Mediterranean Electrotech. Conf., Apr. 2010, pp. 944–948. [50] M. Adamowicz, R. Strzelecki, J. Szewczyk, and L. Lademan, “Wireless short-range device for wind generators,” in Proc. Biennial Baltic Electron. Conf., Oct. 2010, pp. 313–316. [51] O. Anaya-Lara, N. Jenkins, and J. R. McDonald, “Communications requirements and technology for wind farm operation and maintenance,” in Proc. IEEE Int. Conf. Ind. Inf. Syst., Aug. 2006, pp. 173–178. [52] C. Wanzhi, T. Zhiyong, Z. Quangui, and C. Liang, “Research of wireless communication based on lonworks for wind turbine control system,” in Proc. IEEE Int. Conf. Energy Environ. Technol., Oct. 2009, pp. 787–789. [53] K. Kiyota, T. Kakishima, and A. Chiba, “Comparison of test result and design stage prediction of switched reluctance motor competitive with 60-kW rare-earth PM motor,” IEEE Trans. Ind. Electron., vol. 61, no. 10, pp. 5712–5721, Oct. 2014.

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[54] E. Koutroulis and K. Kalaitzakis, “Design of a maximum power tracking system for wind-energy-conversion applications,” IEEE Trans. Ind. Electron., vol. 53, no. 2, pp. 486–494, Apr. 2006. [55] A. Tapia, G. Tapia, J. X. Ostolaza, and J. R. Sáenz, “Modeling and control of a wind turbine driven doubly fed induction generator,” IEEE Trans. Energy Convers., vol. 18, no. 2, pp. 194–204, Jun. 2003. [56] M. P. Kazmierkowski and L. Malesani, “Current control techniques for three-phase voltage-source PWM converters: A survey,” IEEE Trans. Ind. Electron., vol. 45, no. 5, pp. 691–703, Oct. 1998. [57] F. Blaabjerg, R. Teodorescu, M. Liserre, and A. V. Timbus, “Overview of control and grid synchronization for distributed power generation systems,” IEEE Trans. Ind. Electron., vol. 53, no. 5, pp. 1398–1409, Oct. 2006. [58] G. Abad, J. Lopez, M. A. Rodriguez, L. Marroyo, and G. Iwanski, Doubly Fed Induction Machine. Hoboken, NJ, USA: Wiley, 2011. [59] R. M. Tanner, “A recursive approach to low complexity codes,” IEEE Trans. Inf. Theory, vol. IT-27, no. 5, pp. 533–547, Sep. 1981. [60] T. Richardson, A. Shokrollahi, and R. Urbanke, “Design of capacityapproaching low-density parity-check codes,” IEEE Trans. Inf. Theory, vol. 47, no. 2, pp. 619–637, Feb. 2001. [61] T. Richardson and R. Urbanke, “The capacity of low-density parity check codes under message-passing decoding,” IEEE Trans. Inf. Theory, vol. 47, no. 2, pp. 599–618, Feb. 2001. [62] L. Dinoi, F. Sottile, and S. Benedetto, “Design of versatile eIRA codes for parallel decoders,” IEEE Trans. Commun., vol. 56, no. 12, pp. 2060–2070, Dec. 2008. [63] B. Shuval and I. Sason, “On the universality of LDPC code ensembles under belief propagation and ML decoding,” in Proc. IEEE Conv. Elect. Electron. Eng., 2010, pp. 355–359. [64] ETSI DVB-S.2, Std. ETSI 302–307, Mar. 2005.

Alfeu J. Sguarezi Filho (S’03–M’11) received the B.S. degree in electrical engineering from the Faculdade Area 1, Salvador, Brazil, in 2005 and the M.S. and Ph.D. degrees from the University of Campinas, Campinas, Brazil, in 2007 and 2010, respectively. From 2010 to 2011, he was a Researcher with the University of Campinas, under the FAPESP Postdoctoral Program. He is currently a Professor with the Federal University of ABC (UFABC), Santo André, Brazil, teaching in the areas of electrical machines, power electronics, and electrical drives. His research interests include machine drives, doubly fed induction generators, power control, and electrical power systems.

Carlos Eduardo Capovilla (M’10) was born in Vinhedo, Brazil, on March 6, 1977. He received the B.S. degree from the University of São Paulo, São Paulo, Brazil, in 2001 and the M.Sc. and Ph.D. degrees from the University of Campinas, Campinas, Brazil, in 2004 and 2008, respectively, all in electrical engineering (microelectronics). He is currently a Professor with the Federal University of ABC (UFABC), Santo André, Brazil. His current research interests include radio-frequency complementary metal–oxide–semiconductor integrated circuits, smart antennas, mobile systems design, smart grid applications, and telecommunications in complex environments.

Ernesto Ruppert Filho (M’00) received the B.S. degree in electrical engineering and the M.S. and Ph.D. degrees from the University of Campinas (UNICAMP), Campinas, Brazil, in 1971, 1974, and 1983, respectively. From 1972 to 1978, he was with the Electrical and Computer Engineering School, UNICAMP, as an Assistant Professor of electromechanical energy conversion. From 1979 to 1983, he was with General Electric, Brazil, designing large induction and synchronous motors and working as an Application Engineer dedicated to large motors and generators. From 1983 to 1989, he was with Vigesa Heavy Equipment, Brazil, designing very large hydrogenerators and also performing commissioning tests on hydropower plants in Brazil. He is currently a Full Professor with the Electrical and Computer Engineering School, UNICAMP, researching and teaching in the areas of electrical machines, power electronics, drives, and electrical power systems.

Ivan Roberto Santana Casella (S’01–M’04) received the M.S. and Ph.D. degrees in electrical engineering from the Polytechnic School of the University of São Paulo (EPUSP), São Paulo, Brazil. Part of his Ph.D. work was developed at the University of Toronto, Toronto, ON, Canada, where he acted as a Researcher and Teacher Assistant. He was an Electronic Designer with the Consumer Electronics Development Laboratories of Philips and a Researcher with the Automation and Informatics Research and Development Center of NEC (CPDIA) working on the design and analysis of analog and digital systems. He is currently an Adjunct Professor IV with the Federal University of ABC (UFABC), Santo André, Brazil, where he is the Chair of the Communication and Information Laboratory (LIC). His current interests are in the areas of wireless communication and smart grid.

Tarcio Andre dos Santos Barros (S’10) was born in Petrolina, Brazil, on September 19, 1987. He received the bachelor’s degree in electrical engineering in 2000 from the Federal University of Vale Sao Francisco, Petrolina, Brazil, and the master’s degree in 2012 from the University of Campinas (UNICAMP), Campinas, Brazil, where he is currently working toward the Ph.D. degree under the FAPESP program from 2012 to 2015. He works in the areas of electrical machines, power electronics, and electrical drives. His research interests include machine drives, switched reluctance machines, and doubly fed induction generators.