Near State Vector Selection Based Model Predictive Control with Common Mode Voltage Mitigation for a Three Phase Four Leg Inverter

19 

(1)energies Article. Near State Vector Selection-Based Model Predictive Control with Common Mode Voltage Mitigation for a Three-Phase Four-Leg Inverter Abdul Mannan Dadu 1 and Ben Horan 3 ID 1 2 3. *. ID. , Saad Mekhilef 1, *, Tey Kok Soon 2 , Mehdi Seyedmahmoudian 3. ID. Power Electronics and Renewable Energy Laboratory, Department of Electrical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; dadu_bpdb@yahoo.com Department of Computer System & Technology, University of Malaya, Kuala Lumpur 50603, Malaysia; koksoon@um.edu.my School of Engineering, Deakin University, Waurn Ponds, Victoria 3216, Australia; m.seyedmahmoudian@deakin.edu.au (M.S.); ben.horan@deakin.edu.au (B.H.) Correspondence: saad@um.edu.my; Tel.: +60-37-967-6851. Received: 25 October 2017; Accepted: 8 December 2017; Published: 14 December 2017. Abstract: A high computational burden is required in conventional model predictive control, as all of the voltage vectors of a power inverter are used to predict the future behavior of the system. Apart from that, the common mode voltage (CMV) of a three-phase four-leg inverter utilizes up to half of the DC-link voltage due to the use of all of the available voltage vectors. Thus, this paper proposes a near state vector selection-based model predictive control (NSV-MPC) scheme to mitigate the CMV and reduce computational burden. In the proposed technique, only six active voltage vectors are used in the predictive model, and the vectors are selected based on the position of the future reference vector. In every sampling period, the position of the reference current is used to detect the voltage vectors surrounding the reference voltage vector. Besides the six active vectors, one of the zero vectors is also used. The proposed technique is compared with the conventional control scheme in terms of execution time, CMV variation, and load current ripple in both simulation and an experimental setup. The LabVIEW Field programmable gate array rapid prototyping controller is used to validate the proposed control scheme experimentally, and demonstrate that the CMV can be bounded within one-fourth of the DC-link voltage. Keywords: near state vector selection based model predictive control; common mode voltage; ripple content; execution time; computational burden and three phase four leg inverter. 1. Introduction Photovoltaic (PV) energy has become an attractive renewable energy source due to its user-friendly operation, straightforward structure, easy installation, and close setup to the user. In PV energy conversion and distribution systems, three-phase four-leg inverters are becoming popular in specific applications such as standalone [1] and Uninterruptible power supply (UPS) systems [2], as well as in islanded mode when the grid supply has failed [3]. A standalone PV system has to provide uninterrupted and balanced/unbalanced power for utilities such as data communication, aircraft, home appliances, satellite stations, and railway systems [4]. However, delivering an unbalanced load is a commercial and industrial issue in an energy conversion system. A four-wire three-leg inverter can be used to deliver unbalanced power, but it has the drawback of low utilization DC-link voltage. The potential difference between the star point and the midpoint of DC-link capacitors is forced to zero; therefore, the star point of the load is not free [5,6]. Thus, four-leg inverters have been introduced. Energies 2017, 10, 2129; doi:10.3390/en10122129. www.mdpi.com/journal/energies.

(2) Energies 2017, 10, 2129. 2 of 19. that can provide zero sequence paths, and are thus preferred over three-leg inverters to distribute power to the balanced/unbalanced loads. Moreover, three-phase four-leg inverters can overcome the low voltage DC-link utilization of the three-phase four-wire system [7]. Due to the additional leg, the control vectors increase from 8 (23 ) to 16 (24 ), which increases the number of switching actions in every switching period [8–10]. However, the common mode voltage (CMV) between the load-neutral point and the midpoint of the DC-link capacitors of the three-phase four-leg inverter causes radiation in the electromagnetic interference, system losses, and threats to personal safety [11]. In order to overcome these problems, the CMV has to be mitigated or reduced. This can be achieved actively or passively. In active mitigation, active circuits such as transistors and capacitors are used. Meanwhile, passive components are used in passive mitigation [12]. Owing to hardware additions or modifications, these methods lead to increases in size and higher costs, and thus cannot be directly applicable to high-voltage systems. Thus, research is progressing towards software invention or modification through improving the control algorithm. The improvement in software can be divided into two main types. The first type is achieved with a modulation-based control algorithm. In a three-phase four-leg inverter, CMV can be kept constant at ± v2dc (vdc is the DC-link voltage) by improving the modulation mode with the Boolean logic function [13]. A pulse width modulation (PWM) block that avoids the zero-voltage vector has been introduced to alleviate the CMV in [14]. A control scheme with six switching states for the three-phase four-leg inverter is proposed to ensure the zero CMV in [15]. However, this switching scheme cannot be practically implemented for the unbalanced condition due to the utilization of the restricted voltage vector in a conventional method. The aforementioned PWM-based control algorithms consist of a modulation stage and an inner control loop with proportional–integral (PI) controllers. In the second type of improvement scheme, an easier and more powerful control method, the finite control set model predictive control (FCS-MPC) is used. The FCS-MPC considers a finite number of valid switching states to predict the behavior of the system through a discrete model in every sampling period [16–19]. The FCS-MPC uses a cost function to carry out the optimization in the prediction. The predefined cost function is used to compare each prediction with its respective reference, and the switching state that produces the minimum value of the cost function is applied to the inverter. This process is repeated in every sampling period as mentioned in [9,20–22], and thus, no modulation stage is required in this technique. The implementation of the FCS-MPC is very easy and simple, and it has fast a dynamic response in spite of its constraints and nonlinearity inclusion [8,23–25]. However, since the FCS-MPC algorithm predicts the control variables based on the system model, it puts a high computational burden on the controller [20,26,27]. This FCS-MPC-based control technique also can be used to restrict the CMV within ± v6dc by utilizing six non-zero voltage vectors in the three-phase three-leg inverter [28]. The FCS-MPC method can reduce the CMV and control the load current for the three-phase three-leg inverter, as mentioned in [28–30]. Meanwhile, in [29], the load current ripple and CMV are reduced, but the process of selecting two non-zero voltage vectors in each sampling period and determining each vector duration is very complex. This greatly increases the computation complexity. Guo et al. [31] proposed the use of four non-zero voltage vectors in order to reduce the CMV for three-phase three-leg inverters to ± v6dc , but the complicated switching selection between opposite voltage vectors increases the switching losses. In [32], the CMV factor is inserted into the cost function in order to reduce the CMV, but this increases the ripple content in the load current. In recent works, there is a lack of CMV mitigation with reduced computational burden, and most of the research studies have focused on the three-phase three-leg inverter. Thus, further research work is required for the three-phase four-leg inverter. In this paper, a near state vector selection-based MPC (NSV-MPC) is proposed in order to reduce the CMV with reduced computational burden for a three-phase four-leg inverter. Based on near state voltage vector, a cost function is used in the predictive model to determine the optimal switching voltage vector from the vectors that surround the future reference voltage vector. The reference voltage vector and its NSV are determined by the future reference currents. In order to reduce the CMV, the zero-switching voltage vector has to be eliminated from the switching state, because it is.

(3) Energies 2017, 10, 2129. 3 of 19. Energies 2017, 10, 2129. 3 of 20. causing a high CMV. Thus, only six switching states are required to predict the future voltage vector. CMV, the zero-switching voltage vector has be eliminated fromsampling the switching state, because it is the Consequently, only six repeated iterations aretorequired in every period, which reduces causing a high CMV. Thus, only six switching states are required to predict the future voltage vector. computational burden. Consequently, only six repeated iterations are required in every sampling period, which reduces the The rest of this paper is structured as follows: the mathematical models for the inverter-load computational burden. system are derived in Section 2. Then, the model predictive control for three-phase four-leg inverter is The rest of this paper is structured as follows: the mathematical models for the inverter-load explained the 2. proposed techniquecontrol is described in Section 4. The proposed systemin areSection derived3,inand Section Then, thecontrol model predictive for three-phase four-leg inverter system is then validated through simulation, and the experimental results are given in Section 5. is explained in Section 3, and the proposed control technique is described in Section 4. The proposed Afterwards, robustness performance analysis areexperimental discussed inresults Section Then,inthe appropriate system isthe then validatedand through simulation, and the are6.given Section 5. Afterwards, the robustness conclusions are drawn in Sectionand 7. performance analysis are discussed in Section 6. Then, the appropriate conclusions are drawn in Section 7.. 2. Three-Phase Four-Leg Inverter Model 2. Three-Phase Four-Leg Inverter Model. A three-phase four-leg inverter topology with the output resistive-inductive (R-L) filter is shown four-leg inverter topologytopology with the output resistive-inductive filter is shown in FigureA1.three-phase The neutral leg in the inverter is used to control the (R-L) zero-sequence current. in Figure 1. The neutral leg in the inverter topology is used to control the zero-sequence current. The The neutral inductor is introduced in the fourth leg to attenuate the switching current ripple to be the neutral inductor is introduced in the fourth leg to attenuate the switching current ripple to be the same as the other legs. Besides, the neutral inductor limits the fault current during short circuit or same as the other legs. Besides, the neutral inductor limits the fault current during short circuit or unbalanced loading conditions neutralinductance inductance Lis is connected the neutral f nconnected unbalanced loading conditions[15,33]. [15,33]. Therefore, Therefore, neutral at theatneutral leg inleg the applications [34,35]. in practical the practical applications [34,35]. Sy. Sx C1. vdc. vxo. Sz. vyo. Sx. iz. z. Sz. Sy. in. vno n. Lfx. Rfy Lfy. iy. y. C2. Rfx. ix. x. vzo. H. Sn. Rfz Lfz Lfn Rfn. Rx Ry Rz. m. Sn. Sx Sx Sy Sy Sz Sz Sn Sn Gate drives 8. vmo. Control Signals. ADC. FPGA Cost Function Optimization ADC. Predictive Model. Extrapolation. Current References Sine Wave Generator using LabVIEW FPGA. Figure 1. Three-phase four-leg inverter with a field programmable gate array (FPGA) control block. Figure 1. Three-phase four-leg inverter with a field programmable gate array (FPGA) control block. The paired insulated-gate bipolar transistor (IGBT) switches in each of the four legs turn on and off a complementary mode.bipolar If the upper switch(IGBT) of one leg is turned theoflower one islegs turned Theinpaired insulated-gate transistor switches inon, each the four turnoff, on and and vice versa. The CMV is the potential difference between the midpoint of the DC-link capacitors off in a complementary mode. If the upper switch of one leg is turned on, the lower one is turned off, and the load-neutral point (vmopotential ), as shown in Figurebetween 1. The CMV be expressed terms of the and vice versa. The CMV is the difference the can midpoint of the in DC-link capacitors voltage on each leg, as shown in [36]: and the load-neutral point (v ), as shown in Figure 1. The CMV can be expressed in terms of the mo. voltage on each leg, as shown in [36]:. =. +. + 4. +. (1). v xobetween + vyo +the vzoterminal + vno and the midpoint of the DC-link. , , , and are thev voltages where mo = 4 have either voltages level + or − . Based on the switching states, the phase voltages can. (1). Therefore, the 16 switching states of the three-phase four-leg inverter, the CMV can where v xo , vyo ,depending vzo , and von no are the voltages between the terminal and the midpoint of the DC-link. , 0, and . voltages can have either voltages level + vdc or − vdc . Therefore, have the values ± Based on the switching states, the±phase 2 2 According to Kirchhoff’s voltage the inverter’sfour-leg output voltages written follows: depending on the 16 switching states of law, the three-phase inverter,can thebeCMV canashave the values vdc vdc ± 2 , 0, and ± 4 . According to Kirchhoff’s voltage law, the inverter’s output voltages can be written as follows:   di v = R f + R i + L f + vmH (2) dt.

(4) Energies 2017, 10, 2129. 4 of 19. where: v=. h. v xH. i= Rf + R =. h. h. vyH ix. R f x + Rx. Lf =. h. iz in. iy. iT. R f y + Ry. Lfx. iT. vzH vnH. Lfy. R f z + Rz R f n. Lfz Lfn. iT. iT. in + i x + iy + iz = 0 where, v is the load voltage vector, i is the load vector current, R f is the filter resistance, R is the load resistance, L f is the filter inductance, and vmH is the voltage between the load-neutral and the DC-link negative point (H). The voltages of each leg from the DC-link negative point (H) can be written as: v jH = S j vdc , j = x, y, z, n. (3). where vdc is the DC-link voltage, and S j is the switching state of leg j. The derivative from Equation (2) can be written in continuous form in terms of the load current vector, as shown in Equation (4):  i di 1 h = (v − vmH ) − R f + R i dt Lf. (4). The load-neutral voltage (vmH ) can be expressed from Equations (3) and (4) as:. ∑. vmH = Leq vdc. k = x,y,z,n. Sk − Leq Lfk. ∑. k = x,y,z,n. R f k + Rk ik Lfk. (5).  −1 + L1f y + L1f z + L1f n . The system state space representation from Equation (2) is as follows, as seen in [26]:. with Leq =. . 1 Lfx. x = Ax + Bu. y = Cx. (6). h iT h iT with x = i x iy iz and u = v xn vyn vzn , where x is the state variable vector, u is the input variable vector, and y is the output variable vector. Matrix A, B, and C are as follows: .        A=      . R +R − f XL f X X. +. Leq  LfX. R f X +RX LfX R − L ff nn. .  Leq Lfx. . R. f y + Ry Lfy. −.  Leq Lfy. R. Leq Lfz. R. f x +Rx Lfx. −. Rfn Lfn. . −. Rfn Lfn. . R + Ry − f Ly f y. +. Leq  Lfy. Rfn Lfn. . R f y + Ry Lfy R − L ff nn. Leq Lfx. R. Leq Lfy. R. f z + Rz Lfz. −. Rfn Lfn. . −. Rfn Lfn. .  . f z + Rz. Lfz.  f x +Rx. Lfx.    B= . vdc Lfx. . Leq Lfz. 1−. − Lvdcf y − Lvdcf z. Leq Lfx Leq Lfx Leq Lfx. . R. f y + Ry. Lfy. −. L. Rfn Lfn. − Lvdcf x L eqf y   Leq vdc 1 − Lfy Lfy − Lvdcf z. Leq Lfy. . R + Rz − fLz f z. Leq Lfz L − Lvdcf y L eqf z   Leq vdc Lfz 1 − Lfz. − Lvdcf x. +.     . Leq  Lfz. R f z + Rz Lfz R − L ff nn.               .

(5) Energies 2017, 10, 2129. 5 of 19. . 1  C= 0 0.  0  0 . 1. 0 1 0. 3. Model Predictive Control Method of Three-Phase Four-Leg Inverter In order to implement the FCS-MPC algorithm on a microprocessor-based hardware, the discrete model has to be used in the analysis of the FCS-MPC algorithm. In the digital implementation, a discrete time model is used to predict the future current’s value at a sampling interval (k). The inverter output current i at kth and (k + 1)th instant with sampling time Ts can be calculated by using the solution of Equation (6) at the initial and final time as follows: i((k + 1) Ts ) = eA(k+1)Ts i(0) + eA(k+1)Ts i(kTs ) = eAkTs i(0) + eAkTs. Z kTs 0. Z (k+1) Ts 0. e−Aτ Bv(τ )dτ. (7). e−Aτ Bv(τ )dτ. (8). i((k + 1) Ts ) can be obtained by solving Equations (7) and (8) as below: i((k + 1) Ts ) = eATs i(kTs ) + eA(k+1)Ts. (k+1) Ts. Z 0. e−Aτ Bv(τ )dτ −. Z kTs 0. e−Aτ Bv(τ )dτ.  (9). Then, Equation (10) is obtained by changing the variable of integration in Equation (9): i((k + 1) Ts ) = eATs i(kTs ) + A−1 (G − I3×3 )Bv(k + 1) Ts. (10). The output current can be obtained at (k + 1)th from Equation (10) as: i(k + 1) = Gi(k) + Qv(k + 1). (11). where: G = eATs Q = A−1 (G − I3×3 )B The identity matrix I3×3 , the inverse of matrix A, and matrices G and Q are calculated offline in MATLAB (R2014a, MathWorks, Natick, MA, USA). The load current and DC-link voltage are required to predict the output current. Each predicted future current is compared with its respective current reference in order to select the optimal switching state by using the cost function according to the following equation:. =. ga (k + 1) = |i∗ (k + 1) − i(k + 1)| ∗ |ix (k + 1) − ix (k + 1)| + |i∗y (k + 1) − iy (k + 1)| + |i∗z (k + 1) − iz (k + 1)|. (12). The Sinusoidal current references are obtained by Sine Wave Generator using LabVIEW field programmable gate array (FPGA) at kth instant. Then, the required extrapolation can be achieved by using the fourth-order Lagrange extrapolation method [20]. i∗ (k + 1) = 4i∗ (k ) − 6i∗ (k − 1) + 4i∗ (k − 2) − i∗ (k − 3). (13). When the sampling period is very small (Ts < 20 µs), no extrapolation is required. In that case, i∗ (k + 1) = i∗ (k). The fourth leg has to change the switching state according to the switching state changes of three phases in order to control the zero-sequence current. Hence, the changing rate of the switching state in the fourth leg is higher, and it operates at a higher switching frequency as compared.

(6) Energies 2017, 10, 2129. 6 of 19. with the average switching frequency. Therefore, the switching loss of the fourth leg is higher. In order to compensate for the losses caused by the neutral-leg switching frequency, its constrain has been included in the cost function as follows: gb (k + 1) = wswc × swcn. (14). where wswc is the weighting factor. The guidelines of weighting factor selection have been given in [37]. Equation (14) must be achieved in order to improve the performance in reference current tracking and reduce the switching losses. Hence, wswc is important in order to empirically achieve the improvement in performance. The number of switching in the fourth leg can be achieved as follows [38]:  swcn = |Sn (k + 1) − Sn k opt |. (15).  where Sn (k + 1) is the predicted neutral leg gate signal, and Sn k opt is the optimal gate signal in the previous sample, k. The objective of Equation (15) is to force the predicted switching signal to remain at the same signal as the previous state. Then, the overall cost function can be expressed as follows: g ( k + 1) = g a ( k + 1) + gb ( k + 1). (16). The objective of this cost function is to optimize the error close towards zero. 4. Near State Vector Selection-Based Model Predictive Control The lines to neutral voltages for all of the 16 switching vectors of a three-phase four-leg inverter are shown in Table 1. The lines to neutral voltages are transformed from abc into αβγ coordinates by using Equation (17). The results of the transformation are shown in Table 2. . 2 3.  T= 0 1 3. − 13 √1 3 1 3. − 31 − √1 1 3. 3.  (17).  . Table 1. The switching states with phase voltages in the abc coordinate. Switching States PPPP NNNP PNNP PPNP NPNP NPPP NNPP PNPP. Phase Voltage in abc Coordinate vxn. vyn. vzn. 0 −vdc 0 0 −vdc −vdc −vdc 0. 0 −vdc −vdc 0 0 0 −vdc −vdc. 0 −vdc −vdc −vdc −vdc 0 0 0. Switching States PPPN NNNN PNNN PPNN NPNN NPPN NNPN PNPN. Phase Voltage in abc Coordinate vxn. vyn. vzn. vdc 0 vdc vdc 0 0 0 vdc. vdc 0 0 vdc vdc vdc 0 0. vdc 0 0 0 0 vdc vdc vdc. Table 2. The switching states with phase voltages in the αβγ coordinate. Switching States PPPP NNNP PNNP PPNP NPNP NPPP NNPP PNPP. Phase Voltage in αβγ Coordinate vα. vβ. vγ. 0 0. 0 0 0. 0 −vdc − 2v3dc − v3dc. 2vdc 3 vdc 3 − v3dc − 2v3dc − v3dc vdc 3. vdc √ 3 vdc √ 3. 0. vdc −√ 3 √ − vdc 3. − 2v3dc − v3dc − 2v3dc − v3dc. Switching States PPPN NNNN PNNN PPNN NPNN NPPN NNPN PNPN. Phase Voltage in αβγ Coordinate vα. vβ. vγ. 0 0. 0 0 0. vdc 0. 2vdc 3 vdc 3 − v3dc − 2v3dc − v3dc vdc 3. vdc √ 3 vdc √ 3. 0 vdc −√ 3 √ − vdc 3. vdc 3 2vdc 3 vdc 3 2vdc 3 vdc 3 2vdc 3.

(7) 60° on the αβ plane, with sector I in the range of 330° to 30°, and followed by the other sectors. In order to minimize the current and harmonic content, near state switching vectors should be selected that are adjacent to the reference vector. It is seen that two prisms in each sector consist of two tetrahedrons, and there are eight switching vectors adjacent to the reference vector, four of which are selected based on the minimization of CMV and switching loss. The position of the reference vector Energies 2017, 10, 2129 7 of 19 is determined by the NSV-MPC at every sampling period. The active voltage vectors surrounding the determined reference vector are selected based on the position of the reference voltage vector. The optimal vector candidate is included among the selected voltage vectors. As shown in Figure 2c, In the three-dimensional coordinate system of a three-phase four-leg inverter, there are six prisms if the reference switching vector is in sector I, four non-zero switching vectors are required to to represent the switching voltage vectors. These prisms can be divided into six sectors (from I to synthesize the reference vector. Therefore, two sets (Set-a and Set-b) of four switching vectors are VI) such thatineach half of the two adjacent prisms, shown selected eachsector sector.is It combined is clear thatwith two switching vectors (PNNP and PNNN)as are chosenininFigure both 2a. The projection of the reference vector on the αβ coordinate is used to determine the sector of the sets. Hence, there are six different switching vectors in one sector.. reference vector. There are six active vectors and two zeros vectors in each sector.. Energies 2017, 10, 2129. 8 of 20. (a). (b). PPNN. PNPN. PNPN PNNN. PPNN PNN N. PNNN. PPNX. Vref PNNX SECTOR I PNPP Set-a. PPNP. PNPP. PPNP PNPX X=N,P. PNNP. PNNP. PNNP. Set-b. SECTOR I (c) Figure 2. (a) Switching vectors and prisms (b) Projection of a sector on the αβ coordinate (c) Sector Figure 2. (a) Switching vectors and prisms (b) Projection of a sector on the αβ coordinate (c) Sector identification with near state vector selection (NSV). identification with near state vector selection (NSV). The voltage vector closest to the reference voltage vector is selected by evaluating the six active vectors through the predictive mentioned Section 3.voltage, The optimal voltageswitching vector is selected In order to reduce the CMVmodel and utilize highinDC-link six active vectors are fromtothe six active vectors by usinginthe costsector. function Equation selected synthesize the reference each Asinshown in (15). Figure 2b, all of the sectors occupied control technique based voltageby vector can besectors. employed 60◦ on theThe αβmodel plane,predictive with sector I in the range of 330◦on tothe 30◦near , andstate followed the other In order to find the closest voltage vector, which reduces the error between the desired and reference currents. to minimize the current and harmonic content, near state switching vectors should be selected that are From the predefined sectors, the proposed control method predicts the reference vector in each adjacent to the reference vector. It is seen that two prisms in each sector consist of two tetrahedrons, sampling time. Six active vectors are selected from the 14 active vectors that surround the reference and there are eight switching vectors adjacent to the reference vector, four of which are selected based vector based on the near state vectors listed in Table 3. This ensures that the CMV is confined within on the of CMV and switching loss. The position of only the reference vector is ± minimization , and at the same time reduces the computational burden, as six active vectors aredetermined used,. by the NSV-MPC at every sampling period. Thethe active surrounding determined instead of 14 vectors. However, this increases ripplevoltage contentvectors marginally in the loadthe current as reference vector are selected based on the position of the voltage vector. The optimal vector conventional MPC. In order to overcome the problem, onereference zero vector—either PPPP or NNNN—can candidate istogether included among selected voltage As shown Figure if the reference be used with the sixthe active vectors at eachvectors. control cycle, and thisincauses the2c, CMV to vary switching vector is in sector I, four non-zero switching vectors are required to synthesize the between + and − or − and + . Therefore, seven voltage vectors are selected to determinereference the voltage vector that is closest to the reference vector in the proposed NSV-MPC. In both cases, the computational burden is also reduced due to the reduction of switching vectors from 16 to six (considering only active vectors), and seven (including one zero vector), as shown Figure 3. As a result, NSV-MPC demonstrates the better performance with reduced computational burden..

(8) Energies 2017, 10, 2129. 8 of 19. vector. Therefore, two sets (Set-a and Set-b) of four switching vectors are selected in each sector. It is clear that two switching vectors (PNNP and PNNN) are chosen in both sets. Hence, there are six different switching vectors in one sector. The voltage vector closest to the reference voltage vector is selected by evaluating the six active vectors through the predictive model mentioned in Section 3. The optimal voltage vector is selected from the six active vectors by using the cost function in Equation (15). The model predictive control technique based on the near state voltage vector can be employed to find the closest voltage vector, which reduces the error between the desired and reference currents. From the predefined sectors, the proposed control method predicts the reference vector in each sampling time. Six active vectors are selected from the 14 active vectors that surround the reference vector based on the near state vectors listed in Table 3. This ensures that the CMV is confined within ± v4dc , and at the same time reduces the computational burden, as only six active vectors are used, instead of 14 vectors. However, this increases the ripple content marginally in the load current as conventional MPC. In order to overcome the problem, one zero vector—either PPPP or NNNN—can be used together with the six active vectors at each control cycle, and this causes the CMV to vary between + v4dc and − v2dc or − v4dc and + v2dc . Therefore, seven voltage vectors are selected to determine the voltage vector that is closest to the reference vector in the proposed NSV-MPC. In both cases, the computational burden is also reduced due to the reduction of switching vectors from 16 to six (considering only active vectors), and seven (including one zero vector), as shown Figure 3. As a result, NSV-MPC demonstrates the better performance with reduced computational burden. Table 3. Corresponding common mode voltage (CMV) based on the near state vector (NSV) of each sector. SECTOR I. v xo vyo vzo vno vmo. SECTOR II. PNPP. PNNP. PNNN. PPNN. PNPN. PPNP. PNNP. PPNP. PPNN. NPNN. PNNN. NPNP. vdc 2 − v2dc vdc 2 vdc 2 vdc 4. vdc 2 − v2dc − v2dc vdc 2. vdc 2 − v2dc − v2dc − v2dc − v4dc. vdc 2 vdc 2 − v2dc − v2dc. vdc 2 − v2dc vdc 2 − v2dc. vdc 2 vdc 2 − v2dc vdc 2 vdc 4. vdc 2 − v2dc − v2dc vdc 2. vdc 2 vdc 2 − v2dc vdc 2 vdc 4. vdc 2 vdc 2 − v2dc − v2dc. − v2dc vdc 2 − v2dc − v2dc − v4dc. vdc 2 − v2dc − v2dc − v2dc − v4dc. − v2dc. 0. 0. 0. v xo vyo vzo vno vmo. 0. SECTOR III. v xo vyo vzo vno vmo. NPNP. NPNN. NPPN. PPNN. NPPP. vdc 2 vdc 2 − v2dc vdc 2 vdc 4. − v2dc. − v2dc. − v2dc. vdc 2 vdc 2 − v2dc − v2dc. − v2dc. 0. vdc 2 − v2dc − v2dc − v4dc. vdc 2 vdc 2 − v2dc. 0. 0. vdc 2 vdc 2 vdc 2 vdc 4. v xo vyo vzo vno vmo. NPPP. NPPN. NNPN. NPNN. NNPP. − v2dc. − v2dc. − v2dc. − v2dc − v2dc. − v2dc. − v2dc − v2dc. vdc 2 − v2dc vdc 2. 0. vdc 2 vdc 2 vdc 2 vdc 4. NNPP. NNPN. PNPN. NPPN. PNPP. − v2dc. − v2dc − v2dc. − v2dc − v2dc. − v2dc. 0. − v2dc − v4dc. vdc 2 − v2dc vdc 2 − v2dc. vdc 2 − v2dc vdc 2 vdc 2 vdc 4. vdc 2 vdc 2. vdc 2. vdc 2 vdc 2 − v2dc. − v2dc − v4dc. vdc 2 − v2dc − v2dc − v4dc. vdc 2. 0. vdc 2 vdc 2. 0. SECTOR VI. NPPP vdc 2 vdc 2 vdc 2 vdc 4. 0. NPNP. SECTOR V. v xo vyo vzo vno vmo. vdc 2. SECTOR IV. PPNP. vdc 2 − v2dc vdc 2. 0. vdc 2. − v2dc. 0. vdc 2 vdc 2 − v2dc. 0. v xo vyo vzo vno vmo. NNPP. PNPP. PNPN. PNNN. NNPN. PNNP. − v2dc − v2dc. vdc 2 − v2dc vdc 2 vdc 2 vdc 4. vdc 2 − v2dc vdc 2 − v2dc. vdc 2 − v2dc − v2dc − v2dc − v4dc. − v2dc − v2dc − v2dc. − v2dc − v2dc. − v4dc. 0. vdc 2. − v2dc 0. 0. vdc 2. vdc 2. vdc 2. A step-by-step implementation procedure for NSV-MPC is summarized below: Step 1. Measure the load currents i(k) and calculate the reference currents i*(k + 1) by using Equation (13) Step 2. Identify the sector on the αβ plane in the αβγ coordinate and the corresponding voltage vectors at every sampling period. Step 3. Predict the future voltage vector for all of the possible switching states from the identified sector from Table 3. Step 4. Predict the load currents i(k + 1) of all of the possible switching states from the identified sector at the next sampling time by using Equation (11)..

(9) Energies 2017, 10, 2129. 9 of 19. Step 5. Evaluate the cost function g(k + 1) by using Equation (16). Step 6. 7Select the switching state that optimizes the cost function. Step 7. 7Apply the selected switching action to fire the inverter switches. Energies 2017, 10, 2129. i(k) Measured Current. Transformation abc to αβγ. Sine Wave Generator using i*(k) LabVIEW FPGA Reference Current. Cost Function Optimization for selecting optimal voltage vector. Sx Inverter. The sector determination using projection on the αβ plane. i(k+1). Voltage Vector Selection. Lfx. Rx. Rfy Lfy. Ry. Rfx. Main loop. Sy Sz. Rfz Lfz Rz L Rfn fn. Sn. i*(k+1). vdc. Future Current v(k+1) Prediction Model 6/7. 9 of 20. m. Extrapolation. Figure 3. Proposed near state vector selection-based model predictive control (NSV-MPC) block diagram.. Figure 3. Proposed near state vector selection-based model predictive control (NSV-MPC) block diagram.. 5. Simulation Experimental Results Table and 3. Corresponding common mode voltage (CMV) based on the near state vector (NSV) of each sector.. The proposed NSV-MPC has been validated in simulation by using Matlab/Simulink. Then, it is SECTOR I SECTOR II also validated through experimental implementation with a three-phase four-leg inverter laboratory PNPP PNNP PNNN PPNN PNPN PPNP PNNP PPNP PPNN NPNN PNNN NPNP prototype. The parameters of the inverter are as specified in Table 4. The experimental test was − − carried out using a field programmable gate array (FPGA)-based controller, and it can be used to − − − − − − implement the parallel processing technique of the model predictive controller to enhance the system − − − − − − − − − − dynamic response. −. − Table 4.0. 0 PPNP. NPPP −. −. − vdc. −. Parameters of the experimental results.0 0 0. SECTOR III NPNP Variable NPNN NPPN −. −. −. PPNN. −. NPPP NPNP Parameter −. −. NPPP −. −. −. −. −. SECTOR IV NPPN ValueNNPN −. NPNN. 0 NNPP. −. −. −. −. −. −. DC-link voltage 320 V − C DC-link capacitance 2.2 mF − L−f 15 mH − − Filter inductance− − Ts Sampling time 50 µs − − − − − − R Load resistance 12 Ω − 0 0 Filter resistance 0 0 0 00.1 Ω − R−f SECTOR V SECTOR f* Reference nominal frequency 50 HzVI NNPP NNPN PNPN NPPN nominal PNPP peak NNPP PNPP PNPN i* Reference current 10 A PNNN NNPN PNNP Lfn − − − − − Neutral-leg inductance 8 mH f−s Sampling − − − − kHz − − − − − frequency. 5.1. Experimental Setup. −. − were references 0. −. −. −. −. −. − connected − which The current read NI LabVIEW0FPGA 2015 module, to a 0 by the 0 0 0 host computer. Figure 4 depicts the project setup in the laboratory. A National Instrument Single-Board RIO General Purpose Inverter Controller (GPIC) NI-sbRIO9606 with mezzanine card NI 9683 on-board was utilized to acquire the analogue input and generate the digital control signal output to the gate drive of the inverter. The prescaled current sensor LA25NP and voltage transducer LV25N were utilized to acquire appropriate analogue signals before sending them to the simultaneous analogue input from the GPIC board. The CMV was measured by using a Pintek DP-25 Differential Probe. The waveforms of the three-phase load currents and the CMV were measured with a LeCroy Wave Runner oscilloscope..

(10) Energies 10, 2129 2129 Energies 2017, 2017, 10,. 11 10 of of 20 19. Energies 2017, 10, 2129. 11 of 20. Oscilloscope. Power Analyzer. Oscilloscope. Power Analyzer. Inverter. Gate driver. Inverter. Gate driver. GPIC Board GPIC Board. Currentand and Current Voltage Sensor Voltage Sensor Laptop. Laptop. Resistive Load. DC Source. Resistive Load. DC Source. Inductive Load. Inductive Load Figure 4. Experimental setup in the FPGA platform.. Figure 4. Experimental setup in the FPGA platform. Figure 4. Experimental setup in the FPGA platform. 5.2. Results and Performance Analysis. 5.2. Results Results and and Performance Analysis Analysis 5.2. All of Performance the experimental results in this section were obtained by using the weighting factor 0.5 experimental and sampling time = 50 μs.section The same experimental out forfactor All of of=the the results in in this section were obtainedvalidation by using usingwas thecarried weighting factor All experimental results this were obtained by the weighting conventional MPC and NSV-MPC. Ideally, the DC-link capacitors of was a two-level four-leg wswc both ==0.5 and sampling time T = 50 µs. The same experimental validation carried out for s 0.5 and sampling time = 50 μs. The same experimental validation was carried outboth for inverter share equal voltages due to utilizing the fullcapacitors DC-link voltage by adopting the switching conventional MPC and NSV-MPC. Ideally, DC-link a two-level inverter share both conventional MPC and NSV-MPC.the Ideally, the DC-link of capacitors offour-leg a two-level four-leg Three cases were considered in this work: equalscheme. voltages to voltages utilizing the adopting the switching scheme. cases inverter share due equal duefull to DC-link utilizingvoltage the fullbyDC-link voltage by adopting theThree switching Case 1. All available voltage vectors in conventional MPC at every sampling time. were considered in this work: scheme. Three cases were considered in this work: Case 2. One zero vector—either PPPP or NNNN—with six active vectors in proposed NSV-MPC at. voltage vectors Case 1. All Allavailable available voltage vectorsin inconventional conventionalMPC MPCatatevery everysampling samplingtime. time. every sampling time. 3. Six active vectors in proposed at everysix sampling time. 2. One zero vector—either PPPP NNNN—with active in proposed NSV-MPC at Case Case One zero vector—either PPPP orNSV-MPC NNNN—with six active vectors every every sampling time. In thesampling first case, atime. conventional MPC utilized 16 switching vectors to predict the future reference Case vector Sixin active vectors inproposed proposed NSV-MPC every sampling 3. Six active vectors in NSV-MPC every sampling each control cycle. Hence, the CMV wasatat high, and requiredtime. atime. large computational burden due to the high calculation time required to select the desire voltage vector. The peak value of CMV. Current (A). In the MPC utilized In the first first case, case, aa conventional conventional MPC utilized 16 16 switching switching vectors vectors to to predict predict the the future future reference reference oscillated between – and + due to the alternating use of two zero vectors in the control vector in each control cycle. Hence, the CMV was high, and required a large computational vector in each control cycle. Hence, the CMV was high, and required a large computational burden burden algorithm, as illustrated in Figure 5. due due to to the the high high calculation calculation time time required required to to select select the the desire desire voltage voltage vector. vector. The The peak peak value value of of CMV CMV vdc and v+dc oscillated between – due to the alternating use of two zero vectors in the control 15 15 oscillated between − and + due to the alternating use of two zero vectors in the control algorithm, 2. 10. 2. 0 -5 -10 -15. 10 5 0 -5 -10 15 -15. 200. 2005. 100. 100. 0. 0 -10. -100. -15 -100. 15 10 5 0 -5 -10 -15. Voltage (v). Current (A). algorithm, as5 in illustrated as illustrated Figure 5.in Figure 5.. Voltage (v). 200. -200. 10 0 -5. 0. 0.01. 0.02. 0.03. 0.04. 0.05. 0.06. 0.07. 0.08. 0.09. 100. 0.1. (a). 200 -200 100. 0. 0. -100. -100. -200. 0. 0.01. 0.02. 0.03. 0.04. 0.05. 0.06. 0.07. 0.08. 0.09. 0.1. -200. (a) Figure 5. Cont..

(11) Energies 2017, 10, 2129. 11 of of 20 19 12. Energies 2017, 10,i2129 x iy. iz. 12 of 20. ix iy iz 160 V. 160 V. CMV -160 V. CMV. (b). -160 V. (b) Figure Figure 5. 5. Each Each phase phase current current and and common common mode mode voltage voltage (CMV) (CMV) for for Case Case 11 (a) (a) Simulation Simulation result, result, (b) (b) Experimental result (I = 10 A/div. CMV = 200 V/div. and 10 ms/div.). Figure 5. Each phase and CMV common mode voltage (CMV) for Case 1 (a) Simulation result, (b) Experimental result (I =current 10 A/div. = 200 V/div. and 10 ms/div.). Experimental result (I = 10 A/div. CMV = 200 V/div. and 10 ms/div.).. Current (A). 15 10 5 0 -5 -10 -15. 15. 15 10 5 0 -5 -10 -15. 15 10 10 5 5 0 0 -5 -5 -10-10 -15-15. 200. 200. 200 200. 100. 100. 100. 0. 0. -100. -100. Voltage (v). Voltage (v). Current (A). In the second case, the NSV−MPC scheme utilized only one zero vector—either PPPP or In the second case, the NSV−MPC scheme schemeutilized utilized only one zero vector—either PPPP or In the second the NSV−MPC only vector—either or NNNN—together withcase, six active vectors at every sampling time.one As zero shown in Figure 6, PPPP the one-sided NNNN—together with sixsix active vectors at samplingtime. time. As shown in Figure the one-sided active vectors at every every sampling shown in−Figure 6, the6,one-sided orAs between peakNNNN—together value of CMV iswith large, and varies between vdcand − vdc vdcand vdc respectively, peakpeak value of CMV is large, and varies between and − or between − and and − or between − and respectively, value of CMV is large, and varies between 4 2 respectively, due to the use of one zero vector. The computational4 burden is2 reduced since the number of switching due due to the use of one zero vector. The computational burden is reduced since the number of switching to the use of one zero vector. The computational burden is reduced since the number of switching voltage vectors is reduced. voltage vectors is reduced. voltage vectors is reduced.. 0. -100 -200. -200. 0. 100 0. -100. 0. 0.01. 0.01. 0.02. 0.02. 0.03. 0.03. 0.04. 0.04. 0.05. 0.06. Time 0.05 (s) 0.06. Time (s). 0.1. 0.07. 0.08. 0.09. 0.07. 0.08. 0.09. -200. 0.1. -200. (a). (a). ix iy iz. ix iy iz. 80 V. 80 V CMV. CMV. -160 V. (b) Figure 6. Cont.. (b). -160 V.

(12) Energies 2017, 10, 2129. 13 of 20. 15 10 5 0 -5 -10 -15. 100 0. Voltage (v). Voltage (v). 200. Current (A). Current (A). Energies 10, 2129 Energies 2017, 2017, 10, 2129. 15 10 5 0 -5 -10 -15. -5 200 -10 -15. 100. 200. 200. 100. 100. 0. -100 -200. 13 of 20 12 of 19. 15 10 5 150 -5 10 -105 0 -15. -100 0. 0. 0 -100 0.01 -200. 0. 0.02 0.01. 0.03 0.02. 0.04 0.03. 0.05. Time (s) 0.04. 0.06. 0.05. Time (s). 0.07. 0.06. 0.07. 0.09. 0.08 0.08. 0.09. 0.1 0.1. -200. -100 -200. (c) (c). ix iy iz. ix iy iz. 160 V. 160 V. CMV CMV -80 V-80 V. (d). (d) Figure 6. Each phase current and CMV for Case 2, first considering PPPP as the switching state: (a). Figure 6.6.Each switching state: state: (a) (a) Figure Eachphase phase current current and and CMV CMV for for Case Case 2, 2, first first considering considering PPPP PPPP as the switching Simulation result, (b) Experimental result; and considering NNNN as the switching state (c). Simulation result, (b) Experimental result; and considering NNNNNNNN as the switching state (c) Simulation Simulation result, (b) result; considering as 10 thems/div.). switching state (c) Simulation result, (d)Experimental Experimental result (I = and 10 A/div., CMV = 200 V/div. and result, (d) Experimental result (I = 10 A/div., CMV = 200 V/div. and 10 ms/div.). Simulation result, (d) Experimental result (I = 10 A/div., CMV = 200 V/div. and 10 ms/div.). In the third case, the proposed NSV-MPC was applied by using only six active vectors in each sampling instant tothe significantly reduce the peak-to-peak value the CMV. the In the case, proposed NSV-MPC was applied by using only six Consequently, active vectors in each In the third third case, the proposed NSV-MPC was applied byofusing only six active vectors in calculation burden is also reduced through using a reduced number of switching vectors. Even sampling instant to significantly reduce the peak-to-peak valuevalue of theofCMV. Consequently, the each sampling instant to significantly reduce the peak-to-peak the CMV. Consequently, though burden the ripple in the load current is marginally higher than in it is vectors. still in anEven calculation is content also through using a reduced number of Case switching the calculation burden is reduced also reduced through using a reduced number of2, switching vectors. , as shown in Figure 7. allowable range. The CMV is confined within ± though the ripple content in the load current is marginally higher than in Case 2, it is still in an. 15 10 5 0 -5 -10. Current (A). Current (A). Even though the ripple content in the load current is marginally higher than in Case 2, it is still vdc in Figure 7. allowable range.range. The CMV is confined withinwithin ± , as in an allowable The CMV is confined ±15shown 15 4 , as shown in Figure 7.. Voltage (v). -15. Voltage (v). 200 100. 0. 10 5. 10 5 0 -5 -10. 15 0 10 -5 -105. -15. -150. 200. 200 -10. 100. 100 -15. -5. 0. 0. 200. -100 -200. -100. 100. 0. 0.01. 0.02. 0.03. 0.04. 0.05. Time (s). 0.06. 0.07. 0.08. 0.09. -100 -200. 0.1. -200. 0. (a) -100 0. 0.01. 0.02. 0.03. 0.04. 0.05. Time (s). 0.06. 0.07. 0.08. 0.09. 0.1. -200. (a) Figure 7. Cont..

(13) Energies 2017, 10, 2129. 14 13 of of 20 19. Energies 2017, 10, 2129. 14 of 20. ix iy iz ix iy iz. 80 V 80 V. CMV CMV. -80 V -80 V. (b) (b). Figure 7. phase current and CMV for Case 33 (a) (a)3Simulation Simulation result, (b) Experimental result = 10 Figure 7.Each Each phase current and CMV Case (a) Simulation result, (b) Experimental result Figure 7. Each phase current and CMV forfor Case result, (b) Experimental result (I = 10(I A/div., CMV = 200 V/div. and 10 ms/div.). A/div., CMV = 200=V/div. and 10and ms/div.). (I = 10 A/div., CMV 200 V/div. 10 ms/div.).. Fourier Transform(FFT) (FFT) analysis analysis of using the the powergui/FFT The The FastFast Fourier Transform of CMV CMVwas wasobtained obtained using powergui/FFT The Fast Toolbox Fourier Transform (FFT) analysis of CMV was obtained using the powergui/FFT Analysis Analysis of Matlab/Simulink software. The results of the mentioned cases are illustrated in Analysis Toolbox of Matlab/Simulink software. The results of the mentioned cases are illustrated in Toolbox of Matlab/Simulink software. The results of the mentioned cases are illustrated in Figure 8. Figure 8. As shown in the figures, the CMVs of the proposed NSV-MPC are significantly reduced as Figure 8. As shown in the figures, the CMVs of the proposed NSV-MPC are significantly reduced as As shown in the figures,conventional the CMVs of the which proposed NSV-MPC are significantly reducedvectors as compared compared MPC, is due number of voltage in compared withwith the the conventional MPC, which is due to tothe thereduced reduced number of voltage vectors in with NSV-MPC. the conventional MPC, which is due to the reduced number of voltage vectors in NSV-MPC. NSV-MPC. 40. 40. 35. 40. 35. 30. 30 25. 30. Magnitude Magnitude (V) (V). Magnitude (V). Magnitude (V). 40. 30. 20 25. 20. 20 15. 20. 10 15. 10. 10. 0. 0. 0. 0. 10. 10. 20 30 Frequency (kHz). 20 (a) 30 Frequency (kHz). 40. 40. 5. 0. 0. 50. (a). 35. 510 0. 50. 10. 0. 20 30 Frequency (kHz). 10. 40. 50. (b) 20 30 Frequency (kHz). 40. 50. (b). 50. 30. 20. 25. 15. 20 15 10 5 0. (V) MagnitudeMagnitude (V). 25. Magnitude (V). Magnitude (V). 30. 35. 10 5 0. 0. 10. 20 30 Frequency (kHz). 40. 50. 40. 50. 30. 40 20. 30 10. 20. 0. 10. (c). 0. 10. 20 30 Frequency (kHz). 40. 50. (d) 0. Figure 8. results MPC, NSV-MPC with PPPP 0 10 FFT analysis 20 30 of CMV 40 (a) Conventional 50 0 (b) Proposed 20 30 Figure 8. FFT analysis results CMV (a) Conventional MPC, (b) 10 Proposed NSV-MPC with40PPPP 50 Frequency (kHz)ofwith Frequency vector, (c) Proposed NSV-MPC NNNN vector, (d) Proposed NSV-MPC with active(kHz) vectors only. vector, (c) Proposed NSV-MPC with NNNN vector, (d) Proposed NSV-MPC with (c) (d) active vectors only.. Figure 8. FFT analysis results of CMV (a) Conventional MPC, (b) Proposed NSV-MPC with PPPP vector, (c) Proposed NSV-MPC with NNNN vector, (d) Proposed NSV-MPC with active vectors only..

(14) Energies 2017, 10, 2129 Energies 2017, 10, 2129. 14 of 19 15 of 20. Figure 9 illustrates the unbalanced loading condition and the transient analysis of the proposed NSV-MPC method with balanced loads and filters. Figure 9a represents the unbalanced references connected to inin the fourth legleg is to the the balanced balancedload loadand andfilter filterparameters. parameters.The Theunbalanced unbalancedcurrent currentflow flow the fourth known as the zero-sequence current, which is detected by adding the vectors of three of phase currents. is known as the zero-sequence current, which is detected by adding the vectors three phase Figure 9b Figure shows 9b theshows response a step change in change phase -yinand -z references from 10 A from to 5 A.10During currents. the of response of a step phase -y and -z references A to 5 the transient theinstant, inverter currents corresponding referencereference very wellvery for A. During theinstant, transient theload inverter loadtracked currentstheir tracked their corresponding the In these cases, NSV-MPC showed theshowed fast-dynamic response during the wellstep for change the stepcondition. change condition. In these cases, NSV-MPC the fast-dynamic response transient state. during the transient state.. ix iy iz. in. (a). ix iy iz. CMV. (b) Figure 9. 9. (a) (a) Unbalanced Unbalanced references in phase phase -y -y and and -z -z from from 10 10 A A to to 5A 5A with with balanced balanced resistive resistive loads loads Figure references in and filters, and (b) Step change in phase -y and -z references from 10 A to 5A with the proposed NSVand filters, and (b) Step change in phase -y and -z references from 10 A to 5A with the proposed MPC technique. NSV-MPC technique.. 6. Robustness Analysis and Performance Evaluation.

(15) Energies 2017, 10, 2129. 15 of 19. Energies 2017, 10, 2129. 16 of 20. 6. Robustness Analysis and Performance Evaluation The performance and robustness analyses of the proposed control scheme are performed in the The performance and robustness analyses of the proposed control scheme are performed in the following section. following section.. 6.1. Robustness of Model Model Parameter Parameter Variations Variations 6.1. Robustness Analysis Analysis of The controller controller accuracy accuracy depends depends on The on the the system system parameters parameters and and the the discrete discrete predictive predictive model. model. The effectiveness of the NSV-MPC was tested through robustness analysis by varying the system system The effectiveness of the NSV-MPC was tested through robustness analysis by varying the parameters. The The response of the the proposed system against against the the inductive filter variation variation was was validated validated parameters. response of proposed system inductive filter through both simulation and an experiment. Two cases were carried out: (a) CF: Change in inductive through both simulation and an experiment. Two cases were carried out: (a) CF: Change in inductive filter but but no no change change in in controller, where the the filter’s filter’s inductance inductance changed changed from from 66 mH mH to to 18 18 mH mH without filter controller, where without providing the information of the filter changes to the controller. (b) CCF: Changes to both the filter providing the information of the filter changes to the controller. (b) CCF: Changes to both the filter as well as to the controller, where the changed filter values are given to the controller to evaluate the as well as to the controller, where the changed filter values are given to the controller to evaluate the effectiveness. In harmonic distortion to the the effectiveness. In Figure Figure 10a, 10a, the the percentages percentages of of total total harmonic distortion (THD) (THD) with with respect respect to variation of inductive value have been shown. variation of inductive value have been shown. 14 12 10 8 6 4 2 0. 6 mH. 9 mH. 12 mH. 15 mH. 18 mH. Reference and Measured Currents (A). (a). ix. ix. iy. iy. iz. Time (Seconds) (b) Figure 10. Cont.. iz.

(16) Energies 2017, 10, 10, 2129 Energies 2017, 2129. Current tracking error (A). 16 of 1719 of 20. Sample =2001 (c) Figure 10. The results of proposed NSV-MPC: (a) The percentages of THD with respect to variation Figure 10. The results of proposed NSV-MPC: (a) The percentages of THD with respect to variation in in inductive (b) Reference and measured currents, (c) Current tracking error of phase x. inductive value,value, (b) Reference and measured currents, (c) Current tracking error of phase x. 6.2. Reference Current Tracking Error 6.2. Reference Current Tracking Error Each leg of the three-phase four-leg inverter generated an independent voltage to control the Each leg of the three-phase four-leg inverter generated an independent voltage to control the zero-sequence voltage or current. The reference current tracking error of the proposed control scheme zero-sequence voltage or current. The reference current tracking error of the proposed control scheme is very accurate. Figure 10b shows the reference and measured currents, and Figure 10c shows that is very accurate. Figure 10b shows the reference and measured currents, and Figure 10c shows that the tracking error of current ( ∗ ( ) − ( )) in phase x is confined within ±0.82 A. The percentage of the tracking error of current (i∗x (k ) − i x (k)) in phase x is confined within ±0.82 A. The percentage of reference tracking error with respect to the load current can be calculated from the reference and load reference tracking error with respect to the load current can be calculated from the reference and load current as follows: current as follows: 1 a ∗ ∑ 1|i (k ) − ib (k)| (18) ierror_b (%) = a k ∑b | ∗ ( ) − ( )| (18) ib (k)rms _ (%) = ( ) where b = x, y, z and a = 2001 is the number of samples used in simulation. where b = x, y, z and avalue = 2001 the number of of samples used inand simulation. The peak-to-peak of is CMV, THD (%) load current, execution time of the proposed The peak-to-peak value of CMV, THD (%) of load current, and execution time of the proposed system were collected to assess the performance of the system. The proposed NSV-MPC technique system were collected to assess the performance of the system. The proposed NSV-MPC technique improved the processing time by reducing the computational burden through using fewer numbers of improved theinprocessing timecycle. by reducing the computational through using fewer numbers voltage vectors every control The number of ticks (1 tickburden = 25 ns) required by the proposed of voltage vectors in every control cycle. The number of ticks (1 tick = 25 ns) required by the proposed controller in completing each iteration of the control loop with a LabVIEW FPGA-based platform controller completing eachcontroller. iteration ofThe thecomparisons control loop with a LabVIEW platform was was less thaninthe conventional of NSV-MPC andFPGA-based conventional MPC are less than the conventional controller. The comparisons of NSV-MPC and conventional MPC are illustrated in Tables 5 and 6. illustrated in Tables 5 and 6. Table 5. Execution time measurement. Table 5. Execution time measurement. Conventional MPC. Strategy. Strategy. No. of Switching Vectors. No. of Switching Vectors. Execution. Proposed NSV-MPC. Execution. Improvement. Conventional MPC Time NSV-MPC Improvement Execution Execution (tick) Time (1 Proposed Time (tick) (1 tick = 25 ns) tick = 25 ns) (%) (tick) Time (tick) (1 tick = 25 ns) (1 tick = 25 ns) (%) 6 187 44.34 6 187 44.34 16 336 7 201 40.17 16 336 7 201 40.17. Table 6. CMV, percentage of THD, and current tracking error variation at different sampling times. Table 6. CMV, percentage of THD, and current tracking error variation at different sampling times. Strategy. Strategy. CMV Variation CMV. Variation. THD THD (%). Current Tracking THD Current Tracking THD Current Tracking Current Tracking (%) THD Current Current Error (%) Error (%)Tracking (%) THD Error (%) Tracking. (%) Error (%) Sampling Frequency 50 kHz Sampling Frequency. Conventional MPC. −160 to 160. 3.47%. NSV-MPC with Conventional MPC PPPP vector. −160 −160 to to 160 80. 3.47% 3.24%. 50 kHz 3.58% 3.36%. −160 to 80. 3.24%. 3.36%. NSV-MPC with PPPP vector. 3.58%. (%) Error (%) Sampling Frequency. (%) Frequency Error (%) Sampling. 20 kHzFrequency Sampling 3.90%. 4.68%. 3.90% 3.83%. 20 kHz 4.68% 4.26%. 3.83%. 4.26%. 10 kHz Frequency Sampling 6.65%. 6.59%. 6.34%6.65%. 6.11% 6.59%. 6.34%. 6.11%. 10 kHz.

(17) Energies 2017, 10, 2129. 17 of 19. Table 6. Cont.. Strategy. CMV Variation. THD (%). Current Tracking Error (%). THD (%). Current Tracking Error (%). THD (%). Current Tracking Error (%). Sampling Frequency. Sampling Frequency. Sampling Frequency. 50 kHz. 20 kHz. 10 kHz. NSV-MPC with NNNN vector. −80 to 160. 3.24%. 3.36%. 3.83%. 4.26%. 6.33%. 6.13%. NSV-MPC with active vectors. −80 to 80. 3.62%. 3.22%. 4.37%. 4.05%. 6.58%. 5.87%. 7. Conclusions In this paper, the NSV-MPC method was proposed to mitigate CMV as well as reduce computational burden. The proposed NSV-MPC method determined the active vectors based on the reference vector position, and was able to confine CMV within ± v4dc . Moreover, this control technique reduced the computational burden by using a minimum number of usable voltage vectors in every sampling period. The experimental results for all of the possible combinations of switching vector selection were demonstrated to show the effectiveness of the proposed control method as compared with the conventional model predictive control. Besides, the load currents were able to track their references accurately. The steady state and transient state response showed the robustness and the effectiveness of the proposed control method. Author Contributions: Abdul Mannan Dadu has contributed to the theoretical analysis, simulation and experimental verification, and preparing the article; Saad Mekhilef has contributed to preparing the article; Tey Kok Soon, Mehdi Seyedmahmoudian and Ben Horan has contributed to the theoretical analysis and preparing the article. Conflicts of Interest: The authors declare no conflict of interest.. References 1. 2. 3.. 4. 5. 6. 7. 8.. 9.. 10.. Philip, J.; Jain, C.; Kant, K.; Singh, B.; Mishra, S.; Chandra, A.; Al-Haddad, K. Control and Implementation of a Standalone Solar Photovoltaic Hybrid System. IEEE Trans. Ind. Appl. 2016, 52, 3472–3479. [CrossRef] Pichan, M.; Rastegar, H. Sliding Mode Control of Four-Leg Inverter with Fixed Switching Frequency for Uninterruptible Power Supply Applications. IEEE Trans. Ind. Electron. 2017, 64, 6805–6814. [CrossRef] Rodriguez, C.T.; Fuente, D.V.D.L.; Garcera, G.; Figueres, E.; Moreno, J.A.G. Reconfigurable Control Scheme for a PV Microinverter Working in Both Grid-Connected and Island Modes. IEEE Trans. Ind. Electron. 2013, 60, 1582–1595. [CrossRef] Singh, B.; Sharma, S. Design and Implementation of Four-Leg Voltage-Source-Converter-Based VFC for Autonomous Wind Energy Conversion System. IEEE Trans. Ind. Electron. 2012, 59, 4694–4703. [CrossRef] Liang, J.; Green, T.C.; Feng, C.; Weiss, G. Increasing Voltage Utilization in Split-Link, Four-Wire Inverters. IEEE Trans. Power Electron. 2009, 24, 1562–1569. [CrossRef] Karanki, S.B.; Geddada, N.; Mishra, M.K.; Kumar, B.K. A Modified Three-Phase Four-Wire UPQC Topology With Reduced DC-Link Voltage Rating. IEEE Trans. Ind. Electron. 2013, 60, 3555–3566. [CrossRef] Kim, J.-H.; Sul, S.-K. A carrier-based PWM method for three-phase four-leg voltage source converters. IEEE Trans. Power Electron. 2004, 19, 66–75. [CrossRef] Yaramasu, V.; Rivera, M.; Narimani, M.; Bin, W.; Rodriguez, J. Model Predictive Approach for a Simple and Effective Load Voltage Control of Four-Leg Inverter With an Output Filter. IEEE Trans. Ind. Electron. 2014, 61, 5259–5270. [CrossRef] Rivera, M.; Yaramasu, V.; Rodriguez, J.; Bin, W. Model Predictive Current Control of Two-Level Four-Leg Inverters Part II: Experimental Implementation and Validation. IEEE Trans. Power Electron. 2013, 28, 3469–3478. [CrossRef] Brando, G.; Dannier, A.; Del Pizzo, A.; Di Noia, L.P.; Spina, I. Quick and high performance direct power control for multilevel voltage source rectifiers. Electr. Power Syst. Res. 2015, 121, 152–169. [CrossRef].

(18) Energies 2017, 10, 2129. 11.. 12. 13. 14. 15. 16. 17. 18. 19.. 20.. 21.. 22. 23. 24.. 25. 26. 27.. 28. 29. 30.. 31. 32.. 18 of 19. Salem, A.; Abdallh, A.; Rasilo, P.; Belie, F.D.; Ibrahim, M.N.; Dupré, L.; Melkebeek, J. The Effect of Common-Mode Voltage Elimination on the Iron Loss in Machine Core Laminations of Multilevel Drives. IEEE Trans. Mag. 2015, 51, 1–4. [CrossRef] Hedayati, M.H.; Acharya, A.B.; John, V. Common-Mode Filter Design for PWM Rectifier-Based Motor Drives. IEEE Trans. Power Electron. 2013, 28, 5364–5371. [CrossRef] Guo, X.; He, R.; Jian, J.; Lu, Z.; Sun, X.; Guerrero, J.M. Leakage Current Elimination of Four-Leg Inverter for Transformerless Three-Phase PV Systems. IEEE Trans. Power Electron. 2016, 31, 1841–1846. [CrossRef] Un, E.; Hava, A.M. A Near-State PWM Method With Reduced Switching Losses and Reduced Common-Mode Voltage for Three-Phase Voltage Source Inverters. IEEE Trans. Ind. Appl. 2009, 45, 782–793. [CrossRef] Liu, Z.; Liu, J.; Li, J. Modeling, Analysis, and Mitigation of Load Neutral Point Voltage for Three-Phase Four-Leg Inverter. IEEE Trans. Ind. Electron. 2013, 60, 2010–2021. [CrossRef] Bakeer, A.; Ismeil, M.; Orabi, M. A Powerful Finite Control Set-Model Predictive Control Algorithm for quasi Z-Source Inverter. IEEE Trans. Ind. Inform. 2016, 12, 1371–1379. [CrossRef] Geyer, T.; Quevedo, D.E. Multistep Finite Control Set Model Predictive Control for Power Electronics. IEEE Trans. Power Electron. 2014, 29, 6836–6846. [CrossRef] Chen, Q.; Luo, X.; Zhang, L.; Quan, S. Model Predictive Control for Three-Phase Four-Leg Grid-Tied Inverters. IEEE Access 2017, 5, 2834–2841. [CrossRef] Dadu, A.; Tey, K.S.; Mekhilef, S.; Nakaoka, M. Lyapunov law based model predictive control scheme for grid connected three phase three level neutral point clamped inverter. In Proceedings of the 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia, Kaohsiung, Taiwan, 3–7 June 2017. Yaramasu, V.; Rivera, M.; Bin, W.; Rodriguez, J. Model Predictive Current Control of Two-Level Four-Leg Inverter Part I: Concept, Algorithm, and Simulation Analysis. IEEE Trans. Power Electron. 2013, 28, 3459–3468. [CrossRef] Chen, Y.; Liu, T.H.; Hsiao, C.F.; Lin, C.K. Implementation of adaptive inverse controller for an interior permanent magnet synchronous motor adjustable speed drive system based on predictive current control. IET Electr. Power Appl. 2015, 9, 60–70. [CrossRef] Zhang, Y.; Yang, H.; Xia, B. Model predictive torque control of induction motor drives with reduced torque ripple. IET Electr. Power Appl. 2015, 9, 595–604. [CrossRef] Geyer, T.; Quevedo, D.E. Performance of Multistep Finite Control Set Model Predictive Control for Power Electronics. IEEE Trans. Power Electron. 2015, 30, 1633–1644. [CrossRef] Rodriguez, J.; Kazmierkowski, M.P.; Espinoza, J.R.; Zanchetta, P.; Abu-Rub, H.; Young, H.A.; Rojas, C.A. State of the Art of Finite Control Set Model Predictive Control in Power Electronics. IEEE Trans. Ind. Inform. 2013, 9, 1003–1016. [CrossRef] Uddin, M.; Mekhilef, S.; Rivera, M. Experimental validation of minimum cost function-based model predictive converter control with efficient reference tracking. IET Power Electron. 2015, 8, 278–287. [CrossRef] Rivera, M.; Yaramasu, V.; Llor, A.; Rodriguez, J.; Wu, B.; Fadel, M. Digital Predictive Current Control of a Three-Phase Four-Leg Inverter. IEEE Trans. Ind. Electron. 2013, 60, 4903–4912. [CrossRef] Akter, M.P.; Mekhilef, S.; Tan, N.M.L.; Akagi, H. Modified Model Predictive Control of a Bidirectional AC-DC Converter Based on Lyapunov Function for Energy Storage Systems. IEEE Trans. Ind. Electron. 2016, 63, 704–715. [CrossRef] Hoseini, S.K.; Adabi, J.; Sheikholeslami, A. Predictive modulation schemes to reduce common-mode voltage in three-phase inverters-fed AC drive systems. IET Power Electron. 2014, 7, 840–849. [CrossRef] Kwak, S.; Mun, S.K. Model Predictive Control Methods to Reduce Common-Mode Voltage for Three-Phase Voltage Source Inverters. IEEE Trans. Power Electron. 2015, 30, 5019–5035. [CrossRef] Vazquez, S.; Leon, J.I.; Franquelo, L.G.; Rodriguez, J.; Young, H.A.; Marquez, A.; Zanchetta, P. Model Predictive Control: A Review of Its Applications in Power Electronics. IEEE Ind. Electron. Mag. 2014, 8, 16–31. [CrossRef] Guo, L.; Zhang, X.; Yang, S.; Xie, Z.; Cao, R. A Model Predictive Control Based Common-Mode Voltage Suppression Strategy for Voltage Source Inverter. IEEE Trans. Ind. Electron. 2016, 63, 6115–6125. [CrossRef] Chen, X.; Chen, W.; Han, Y.; Sha, Y.; Qi, H.; Yang, X. Predictive current control method to reduce common-mode interference for three-phase induction motor. In Proceedings of the IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia), Hefei, China, 22–25 May 2016; pp. 2859–2862..

(19) Energies 2017, 10, 2129. 33.. 34. 35. 36.. 37.. 38.. 19 of 19. Pettersson, S.; Salo, M.; Tuusa, H. Improving the performance of a three-leg four-wire active power filter with a neutral wire filter inductor. In Proceedings of the IEEE Compatibility in Power Electronics, Gdynia, Poland, 1 June 2005; pp. 101–107. Bayhan, S.; Abu-Rub, H.; Balog, R. Model Predictive Control of Quasi-Z Source Four-Leg Inverter. IEEE Trans. Ind. Electron. 2016, 63, 4506–4516. [CrossRef] Zhang, R.; Prasad, V.H.; Boroyevich, D.; Lee, F.C. Three-dimensional space vector modulation for four-leg voltage-source converters. IEEE Trans. Power Electron. 2002, 17, 314–326. [CrossRef] Yaramasu, V.; Bin, W.; Rivera, M.; Narimani, M.; Kouro, S.; Rodriguez, J. Generalised approach for predictive control with common-mode voltage mitigation in multilevel diode-clamped converters. IET Power Electron. 2015, 8, 1440–1450. [CrossRef] Cortes, P.; Kouro, S.; La Rocca, B.; Vargas, R.; Rodriguez, J.; Leon, J.I.; Vazquez, S.; Franquelo, L.G. Guidelines for weighting factors design in Model Predictive Control of power converters and drives. In Proceedings of the IEEE International Conference Industrial Technology, Gippsland, Australia, 10–13 February 2009; pp. 1–7. Jose, R.; Patricio, C. Cost Function Selection, Predictive Control of Power Converters and Electrical Drives; Wiley: Hoboken, NJ, USA, 2012; pp. 145–161. © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)..

(20)

New documents

HMM, Product Specification Prod Spec, Application Note Appl Note, Operator's Manual Oper Man, Reference Manual Ref Man, Media Manual Med Man, Oper Guide, Instl-Oper., Special Options

Hence, the types of ECG signals used in the training and testing of the hybrid system are suitable for visual recognition by human expert without the aid of any signal processing tools

From the fuzzy inputs, outputs and their membership functions, 139 fuzzy If-Then rules are generated for identifying and classifying sag, swell and interruption MswellXrwell Nswell

Non-dimensional pressure gradient R isosurfaces of mixed fluid equation 4.1 in the local strain rate tensor eigenvector coordinate system for a R = 5 and b R = 10 at the highest

Designator Section II - Hardware Manuals Listed Under Product Number Section III - Software Manuals Listed Under Operating Systems Table of Software Abbreviations Section IV -

Chapter 1: The Effects of Electrical Activity on Cell Growth 1.1 Evidence that Electrical Activity Affects Neuronal Development 1.1.1 Synapse Regulation by Electrical Activity 1.1.2

Each participating public university library, and some research libraries such as the Malaysian Palm Oil Board MPOB, Standards and Industrial Research Institute of Malaysia SIRIM [now

Table 3 Comparison of mean torque grip strength of right and left hands between men and women Mean torque SD [Nm] Men Women Right-hand dominant group Right hand Left hand Ratio

In this paper, causes of deterioration of concrete as well as repairing by using cement grout, mortar, concrete, sprayed concrete or shotcrete, epoxy, ferrocement with mortar, Fiber