INTERNATIONAL JOURNAL OF PROFESSIONAL ENGINEERING STUDIES Volume VI /Issue 1 / DEC 2015

IJPRES

Controller for Quasi-Z-Source Cascade Multilevel Inverter for renewable power generation

Mr .U.SWAMICHANDRA NAGESH Vishnu Institute of Technology

Bhimavaram (T); West Godavari Dist. A.P; India

P.NAVEEN Assistant Professor, Department of EEE,

Vishnu Institute of Technology Bhimavaram(T); West Godavari Dist.

A.P; India

Abstract In this paper, an advanced control method for quasi-Z-source cascade multilevel inverter (qZS-CMI) based grid-tie photovoltaic (PV) power system is proposed. It has advantages of Voltage boost and buck functions in a single stage, continuous input current and improved reliability. Quasi Z-source CMI seven level outputs are obtained for PI and fuzzy. The results shown here are better than the conventional method. The MATLAB/SIMULINK model is shown here with results Index Terms- Fuzzy logic controller (FLC), Cascade multilevel inverter (CMI), photovoltaic (PV) power system, quasi-Z-source inverter, space vector modulation (SVM). INTRODUCTION Renewable energy comes from renewable resources. It is different from fossil fuels as it does not produce as many greenhouse gases and other pollutants as fossil fuel combustion. There are a lot of traditional uses of wind power, hydropower, bio fuel and solar energy in developed and developing countries. But the mass production of electricity using renewable energy sources is now becoming more common. A recent upsurge in the study of photovoltaic (PV) power generation emerges, since they directly convert the solar radiation into electric power without hampering the environment. However, the stochastic fluctuation of solar power is inconsistent with the desired stable power injected to the grid, owing to variations of solar irradiation and temperature. To fully exploit the solar energy, extracting the PV panels’ maximum power and feeding them into grids at unity power factor become the most important. The contributions have been made by the cascade multilevel inverter (CMI). Nevertheless, the H-bridge inverter (HBI) module lacks boost function so that the inverter KVA rating requirement has to be increased twice with a PV voltage range of 1:2; and the different PV panel output voltages result in imbalanced dc-link voltages. The extra dc–dc boost converters were coupled to PV panel and HBI of the CMI to implement separate maximum power point tracking (MPPT) and dc-link voltage balance.

However, each HBI module is a two-stage inverter, and many extra dc–dc converters not only increase the complexity of the power circuit and control and the system cost, but also decrease the

efficiency .Recently, the Z-source/quasi-Z-source cascade multilevel inverter (ZS/qZS-CMI)-based PV systems were proposed in [5]–[8]. They possess the advantages of both traditional CMI and Z-source topologies. For example, the ZS/qZS-CMI: 1)has high-quality staircase output voltage waveforms with lower harmonic distortions, and Reduces/eliminates output filter requirements for the compliance of grid harmonic standards; 2) requires power semiconductors with a lower rating, and greatly saves the costs; 3) shows modular topology that each inverter has the same circuit topology, control structure and modulation ; 4) most important of all, which allows an independent control of the power delivery with high reliability; and 5) can fulfil the distributed MPPT. In order to properly operate the ZS/qZS-CMI, the power injection, independent control of dc-link voltages, and the pulse width modulation (PWM) are necessary. The work in [5] and [7] focused on the parameter design of the ZS/Qzs networks and the analysis of efficiency. The work in [8] presented the whole control algorithm, i.e., the MPPT control of separate quasi-Z-source H-bridge inverter (qZS-HBI) module, and the grid-injected power control, whereas the phase-shifted sine wave PWM (PS- PWM) is the only existing PWM technique for the single-phase ZS/qZS-CMI. The PS-SPWM consumes more resources to achieve the shoot-through states because two more references are compared with the carrier waveform. Additionally, the ZS/qZS-CMI based grid-tie PV system has never been modelled in detail to design the Controllers. The main contributions of this paper include: 1) a novel multilevel space vector modulation (SVM) technique for the single phase qZS-CMI is proposed, which is implemented without additional resources; 2) a grid-connected control for the qZS-CMI based PV system is proposed, where the all PV panel voltage references from their independent MPPTs are used to control the grid-tie current; the dual-loop dc-link peak voltage control is employed in every qZS-HBI module to balance the dc-link voltages; 3) the design process of regulators is completely presented to achieve fast response and good stability; and 4) simulation and experimental results verify the proposed PWM algorithm and control scheme.

INTERNATIONAL JOURNAL OF PROFESSIONAL ENGINEERING STUDIES Volume VI /Issue 1 / DEC 2015

IJPRES

This paper is organized as follows. An overview of the whole system with control strategy is presented in Section II. Section III focuses on the system modelling and grid-connected control. Section IV addresses the proposed multilevel SVM technique. Section V designs the regulators; the proposed strategy is verified by simulation and experimental results in Section VI. Finally, a conclusion is given in Section VII. Note that, in the derivations of the paper, all of the symbols with “^” denote the amplitude and those with “−” denote the average.

DESCRIPTION OF QZS-CMI-BASED GRID-TIE PV POWER SYSTEM

Fig. 1 shows the discussed qZS-CMI-based grid-tie PV power system. The total output voltage of the inverter is a series summation of qZS-HBI cell voltages. Each cell is fed by an independent PV panel. The individual PV power source is an array composed of identical PV panels in parallel and series. A typical PV model in [12] is performed by considering both the solar irradiation and the PV panel temperature.

A. qZS-CMI The qZS-CMI combines the qZS network into each HBI module. When the k th qZS-HBI is in non shoot-through states, it will work as a traditional HBI. Here are

vDCK =1

1− 2Du = B u v

= v^ (1) while in shoot-through states, the qZS-HBI module does not contribute voltage. There are

V^DCK=0 u = 0 (2)

For the qZS-CMI, the synthesized voltage is

u = S V^DCK (3)

where V ( ) is the output voltage of the th PV array; V ( )is the dc-link voltage of the K th qZS-HBI module; D and B represent the shoot-through duty ratio and boost factor of the K th qZS-HBI, respectively, V HK is the output voltage of the K th module, and S ∊ {−1 ; 0; 1} is the switching function of the K th qZS-HBI

B. Control Strategy The control objectives of the qZS-CMI based grid-tie PV system are: 1) the distributed MPPT to ensure the maximum power extraction from each PV array; 2) the power injection to the grid at unity power factor with low harmonic distortion; 3) the same dc-link peak voltage for all qZS-HBI modules. The overall control scheme of Fig. 1 is proposed to fulfil these purposes

In this paper control is done by the fuzzy logic controller. The control scheme consists of a Fuzzy controller, a limiter, and a three phase sine wave generator for the generation of the internal structure of the control circuit. The control scheme consists of a Fuzzy controller, a limiter, and a three phase sine wave generator for the generation of reference currents and switching signals. The peak value of the reference current is estimated by regulating the DC link voltage. The actual capacitor voltage is compared with a set reference value. The error signal is then processed through a Fuzzy controller, which contributes to the zero steady error in tracking the reference current signal. A fuzzy controller converts a linguistic control strategy into an automatic control strategy, and fuzzy rules are constructed either by expert experience or with a knowledge database. Firstly, the input Error ‘E’ and the change in Error ‘4E’ have been placed with the angular velocity to be used as the input variables of the fuzzy logic controller. Then the output variable of the fuzzy logic controller is presented by the control Current Imax. To convert these numerical variables into linguistic variables, the following seven fuzzy levels or sets are chosen: NB (negative big), NM (negative medium), NS (negative small), ZE (zero), PS (positive small), PM (positive medium), and PB (positive big) as shown in below fig

Rule Base: The elements of this rule base table are determined based on the theory that in the transient state, large errors need coarse control, which requires coarse input/output variables, while in the steady state, small errors need fine control, which requires fine input/output variables. Based on this, the elements of the rule table are obtained as shown in below fig

ABOUT FUZZY LOGIC CONTROLLER

INTERNATIONAL JOURNAL OF PROFESSIONAL ENGINEERING STUDIES Volume VI /Issue 1 / DEC 2015

IJPRES

A fuzzy inference system (or fuzzy system) basically consists of a formulation of the mapping from a given input set to an output set using fuzzy logic. This mapping process provides the basis from which the inference or conclusion can be made. A fuzzy inference process consists of the following steps:

Step 1: Fuzzification of input variables Step 2: Application of fuzzy operator (AND, OR, NOT) in the IF (antecedent) part of the rule Step 3: Implication from the antecedent to the consequent (THEN part of the rules) Step 4: Aggregation of the consequents across the rules Step 5: Defuzzification

The crisp inputs are converted to linguistic variables in fuzzification based on membership function (MF). An MF is a curve that defines how the values of a fuzzy variable in a certain domain are mapped to a membership value μ (or degree of membership) between 0 and 1. A membership function can have different shapes. The simplest and most commonly used MF is the triangular-type, which can be symmetrical or asymmetrical in shape. A trapezoidal MF has the shape of a truncated triangle. Two MFs are built on the Gaussian distribution curve: a simple Gaussian curve and a two sided composite of two different Gaussian distribution curves. The bell MF with a flat top is somewhat different from a Gaussian function. Both Gaussian and bell MFs are smooth and non-zero at all points. The implication step helps to evaluate the consequent part of a rule. There are a number of implication methods in the literature, out of which Mamdani and TS types are frequently used. Mamdani proposed this method which is the most commonly used implication method. In this, the output is truncated at the value based on degree of membership to give the fuzzy output.

For achieving the first two goals, the closed loops are employed, as Fig. 1(a) shows

1) Total PV array voltage loop adjusts the sum of PV array voltages tracking the sum of PV array voltage references by using fuzzy logic controller (FLC) Each PV array voltage reference is from its MPPT control independently. 2) Grid-tie current loop ensures a sinusoidal grid-injected current in phase with the grid voltage. The total PV array voltage loop outputs the desired amplitude of grid-injected current. A Proportional + Resonant (PR) regulator enforces the actual grid current to track the desired grid-injected reference. The current loop output’s total modulation signal subtracts the modulation signal sum of the second, third, , and th qZS-HBI modules to get the first qZS-HBI module’s modulation signal.

3) The separate PV array voltage loops regulate the other PV array voltages to achieve their own MPPTs through the FLC’s, such as to, respectively. With the total PV array voltage loop control, the PV arrays fulfil the distributed MPPT. In addition, the voltage feed forward control is used to generate each qZS-HBI module’s modulation signal, which will reduce the regulators’ burden, achieve the fast dynamic response, and minimize the grid voltage’s impact on the grid-tie current. For the third goal, the dc-link peak voltage is adjusted in terms of its shoot-through duty ratio for each qZS-HBI module, as Fig. 1(b) shows. A proportional regulator is employed in the inductor current loop to improve the dynamic response, and a PI regulator of the dc-link voltage loop ensures the dc-link peak voltage tracking the reference. Finally, the independent modulation signals u and shoot through duty ratiosD of the qZS-CMI, K∊ {1,2,3, . . n} are combined into the proposed multilevel SVM to achieve the desired purposes.

Fig.1. (a) qZS-CMI based grid-tie PV power system.

(b) DC-link peak voltage control.

INTERNATIONAL JOURNAL OF PROFESSIONAL ENGINEERING STUDIES Volume VI /Issue 1 / DEC 2015

IJPRES

SYSTEM MODELING AND CONTROL

Fig. 2 shows block diagram of the proposed grid-tie control with the system model for the qZS-CMI based PV power system. The details will be explained as follows.

A. Grid - Tie current Loop

The k th qZS-HBI module has following dynamic:

퐢퐋ퟏ퐤 = 퐢퐏퐕퐤 − 퐂퐩퐝퐮퐏퐕퐤퐝퐭

(ퟒ)

Where 퐢퐋ퟏ퐤 is the current of qZS inductor L , 퐢퐏퐕퐤 is the Kth PV array’s current, and퐂퐩 is the capacitance of PV array terminal capacitor.

The qZS-CMI based grid-tie PV system has

v = V + Ldidt + r i (5)

Where V is the grid voltage, i is the grid-injected current, L is the filter inductance, r and is its parasitic resistance. The transfer function of the grid-injected current can be

G ( ) =I ( )

V ( ) ( )

=1

L ( ) (6)

A PR regulator is employed to enforce the actual grid-injected current to track the desired reference

v = v′ + v (s)G (s) (7)

G (s) =1

nG (s),

G (s) =V (S)V (s) = v (8)

Where v′ is the regulated modulation signal from the separate voltage control of the th module, as Fig. 2 shows.

From (8) and (9), we have

V (S) = v G (s)

∑ [v′( )

+ v ( ) ( ) ( )] (9)

At the dc-link peak voltage balance control, all dc-link peak voltages are the same. The n qZS-HBI modules have the same transfer function, and we assume

G (s) = G (s), k ∊ {1; 2; … … . , n} (10)

Using 6-11, the grid injected current will be

I(S)=G (s)G (s)∑ v′ ( )

Then, the current loop of Fig. 2 is simplified to Fig. 3, and the open-loop transfer function can be obtained as

G (s) =I (s)v′ (s) = G (s)G (s)

=v^dck

L (s) + r (12)

With the compensation of the PR regulator, the transfer function becomes

G (s) = G (s)G (s)

= ^ (13)

Fig.3. Simplified block diagram of the grid-current closed loop

A. PV Voltage Loop

V (s) =1

C (s) [I (s)− I (s)] (15)

In addition, the output power of each qZS-HBI module equals to its input power in the non shoot-through state, the kth qZS-HBI module has the power equation

ı̂ v2

= v ı̅ = v ı̅ _ (16)

Where ı̅ _ is the average current of inductor in nonshoot-through state. Using (1), ı̅ _ can be solved as

ı ̅ _ =ı̂ v

2v ( ) (17)

In the shoot-through state, the average current of inductor L can be expressed as

ı ̅ _ = iPvK = ip̅vk (18)

ı̅ = D ı̅ + (1− D ) (19)

=D i + (1− D )I ̅ (20)

INTERNATIONAL JOURNAL OF PROFESSIONAL ENGINEERING STUDIES Volume VI /Issue 1 / DEC 2015

IJPRES

Then, the block diagrams of total and separate PV voltage loops can be obtained in Figs. 4 and 5.

Fig.4. Block diagram of total PV voltage loop.

Fig.5. Block diagram of separate PV voltage loop.

Fig.6. Block diagram of the th module’s dc-link peak voltage control

The closed-loop transfer function is

G (s) =V t(S)

V∗ t(S) =−G ( )

1− G ( ) (14)

Similarly, from Fig. 5, the transfer function of the kth qZS-HBI module’s PV voltage loop,k€{2,.....,n},is Also, the closed-loop transfer function is obtained as

G (s) =V (S)

V∗ =−G ( )

1−G ( ) (15)

DC – link voltage controller

The independent dc-link peak voltage control based on the inductor- current and the capacitor- voltage is performed for each qZS-HBI module, as Fig. 1(b) shows. From [14], the th qZS-HBI module’s transfer functions from the shoot-through duty ratio to the dc-link peak voltage, , and from the shoot-through duty ratio to the inductor- current, can be obtained, respectively With the employed proportional regulator at the

coefficientK for the inductor current loop, as the block diagram of Fig. 6 shows, the closed-loop transfer function of inductor L current can be obtained by

G (s) =d (s)

I∗ (s) =K

1 + k G ( ) (16)

PROPOSED MULTILEVEL SVM FOR QZS- CMI As the qZS network is embedded to the HBI module, the SVM for each qZS-HBI can be achieved by modifying the SVM technique for the traditional single-phase inverter. Using the first qZS-HBI module of Fig. 1 as an example, the voltage vector reference U is created through the two U vectors and,U by

U = UTT + U

TT (17)

Where and is the carrier frequency; the time interval is the duration of active vectors, and is the duration of traditional zero voltage space vectors. Thus, the switching times for the left and right bridge legs in traditional HBI are. However, the shoot-through states are required for the independent qZS-HBI module.

CONTROL PARAMETER DESIGN The prototype specifications of qZS-CMI based PV power system are shown in Table I. For the grid-connected current Parameters Value

퐯퐩퐯,퐦퐢퐧 60v 퐯퐩퐯,퐦퐢퐧 120v

퐋ퟏ퐚퐧퐝 퐋ퟐ 1.8m H 퐂ퟏ퐚퐧퐝 퐂ퟐ 3300μF

퐂퐏 1100μF 퐋퐟 1mH 퐟퐜 5 kHz

CONTROLLER VALUES

Parameters Value 퐤퐝퐩퐤 0.0068 퐤퐯퐝퐩퐤 0.0281 퐤퐯퐝퐢퐤 2.5254 퐤퐢퐩,퐤 퐢퐑 5.15e-3,0.1592

SIMULATION RESULTS A seven-level qZS-CMI for grid-connected PV power system is prototyped. Two Agilent E4360A Solar Array Simulators (SAS) are used to emulate the electrical behaviour of PV arrays. Each SAS has two channel outputs, and each channel is with maximum 120-V maximum power point (MPP) voltage and 5-A MPP current

INTERNATIONAL JOURNAL OF PROFESSIONAL ENGINEERING STUDIES Volume VI /Issue 1 / DEC 2015

IJPRES

Fig.11. Simulation results of qZS-CMI at different PV array voltages.

DC – link voltage balance test The different PV array voltages are performed for the three qZS-HBI modules. The second module’s PV voltage is set to 70 V and the others are at 90 V. A 50- resistor is used as ac load in this test. All of the voltages in experimental results are 100V/div. From (1), the 136-V dc-link voltage of qZS-HBI module is required to support the 230-V grid. Fig. 11 shows the simulation results, where the second module’s dc-link peak voltage is boosted to the same voltage value when compared with other modules, but with a longer shoot-through time interval. Also the qZS-CMI outputs the seven-level voltage with equal voltage step from one level to another level Grid - Tie Investigation The qZS-CMI is connected to the grid in order to test the proposed grid-tie control. Fig. 13 shows the PV array’s power voltage characteristics. The measured PV array voltage and current of each module are used to calculate the actual PV power and the MPPT algorithm searches for the PV voltage reference at the MPP, which is refreshed every 0.05 s. Here, the perturbation and observation (P&O) MPPT strategy is applied in considering the excellent tracking efficiency and easy implementation [16]. At first, the three modules are all working at 900 W/m , and all of the initial voltage references of MPPT algorithms are given at 105 V from Fig. 13. The second module’s irradiation decreases to 700 W/m from 1 to 2 s in simulation. Fig. 14 shows the simulation results. In the experiments, the same test conditions of irradiation and temperature can be implemented by setting the curves of Agilent SAS. Fig. 14(a) shows the total PV voltage (sum of three PV panel voltages) and reference, PV panel voltages and references of modules 2 and 3, respectively. Fig. 14(b) is the enlarged detail of Fig. 14(a). It can be seen that the excellent tracking performance is achieved during 0– 1 s;

Fig.13. PV array power–voltage characteristic

Fig.14. Simulation results at the grid-tie case. (a), (b) For PV voltages at MPPT. (c), (d) For qZS- CMI output voltage, grid voltage, and current.

CONCLUSION

This paper proposed a control method for qZS-CMI based single-phase grid-tie PV system. The grid-injected power was full filled at unity power factor,

INTERNATIONAL JOURNAL OF PROFESSIONAL ENGINEERING STUDIES Volume VI /Issue 1 / DEC 2015

IJPRES

all qZS-HBI modules separately achieved their own maximum power points tracking even if some modules’ PV panels had different conditions. Moreover, the independent dc-link voltage closed-loop control ensured all qZS-HBI modules have the balanced voltage, which provided the high quality output voltage waveform to the grid. The control parameters were well designed to ensure system stability and fast response. A multilevel SVM integrating with shoot through states was proposed to synthesize the staircase voltage waveform of the single-phase qZS-CMI. Recently Z Source inverter is in demand because of its attractive power conversion. The simulation was carried out on the seven-level qZS-CMI prototype. The qZS-CMI based grid-tie pV system was tested. The simulation results verified the proposed qZS-CMI based grid-tie PV power system and the proposed control method. In principle, the proposed system can work with the weak grid, even though this paper did not address this topic.

REFERENCES

[1]Yushan Liu, Student Member, IEEE, BaomingGe, Member, IEEE, Haitham Abu-Rub, Senior Member, IEEE,and Fang Z. Peng, Fellow, IEEE’’An Effective Control Method for Quasi-Z-Source Cascade Multilevel Inverter-Based Grid-Tie Single-Phase Photovoltaic Power System’’IEEE transactions on industrial informatics, vol. 10, no. 1, february 2014 [2] J. Chavarria, D. Biel, F. Guinjoan, C. Meza, and J. J. Negroni, “Energy balance control of PV cascaded multilevel grid-connected inverters under level-shifted and phase-shifted PWMs,” I E E E Tr an s . I n d . E l e c t ro n ., vol. 60, no. 1, pp. 98–111,Jan.2013. [3] S. Kouro, C. Fuentes, M. Perez, and J. Rodriguez, “Single DC-link cascaded H-bridge multilevel multistring photovoltaic energy conversion system with inherent balanced operation,” in Proc. IECON 38th Annu. Conf. IEEE Ind. Electron. Soc., Oct. 25–28, 2012, pp. 4998 [4] S. Rivera, S. Kouro, B. Wu, J. I. Leon, J. Rodriguez, and L. G. Franquelo, “Cascaded H-bridge multilevel converter multistring topology for large scale photovoltaic systems,” in Proc. IEEE Int. Symp. Ind. Electron., Jun. 27–30, 2011, pp. 1837–1844. [5] B. Ge, “Energy Stored Cascade Multilevel Photovoltaic Grid-Tie Power Generation System,” China Patent ZL201010234877.0, Jul. 2010 [6] Y. Zhou, L. Liu, and H. Li, “A High-performance photovoltaic module integrated converter (MIC) based on cascaded quasi-Z-source inverters (qZSI) using eGaN FETs,” IEEE Trans. Power Electron. , vol. 28, no. 6, pp. 2727–2738, Jun.2013.

[7] H. Abu-Rub, A.. Igbal, Sk. Moin Ahmed, F. Z. Penz, Y. Li, and B. Ge, “Quasi-Z-source inverter-based photovoltaic generation system with maximum power tracking control using ANFIS,” IEEE Trans. Sustain. Energy, vol. 4, no. 1, pp. 11–20,Jan.2013. [8] B. Ge, H. Abu-Rub, F. Peng, Q. Lei, A. de Almeida, F. Ferreira, D. Sun, and Y. Liu, “An energy stored quasi-Z-source inverter for application to photovoltaic power system,” IEEE Trans. Ind. Electron., vol. 60, no. 10, pp. 4468–4481,Oct2013. [9] H. Abu-Rub, M. Malinowski, and K. Al-Haddad, Power Electronics for Renewable Energy Systems, Transportation and Industrial Applications. Hoboken, NJ, USA: Wiley, 2014. [10] Y. A. Mahmoud, W. Xiao, and H. H. Zeineldin, “A parameterization approach for enhancing PV model accuracy,” IEEE Trans. Ind. Electron., vol. 60, no. 2, pp. 5708–5716, Dec. 2013. [11] D. N. Zmood and D. G. Holmes, “Stationary frame current regulation of PWM inverters with zero steady-state error,” IEEE Trans. Power Electron., vol. 18, no. 3, pp. 814–822, May 20.

Mr. U SWAMICHANDRA NAGESH Completed B.Tech. In Electrical & Electronics Engineering in 2012 from SRI VASAVI ENGINEERING COLLEGE, Tadepalligudem Affiliated to Jawaharlal Nehru Technological University,

Kakinada and M.Tech in Electrical & Power Engineering in 2015 from VISHNU INSTITUTE OF TECHNOLOGY, Bhimavaram affiliated to Jawaharlal Nehru Technological University, Kakinada. His areas of interest include Power System Protection, Power Electronics.E-mailid:swaminagesh256@gmail.com

Mr. NAVEEN POTHARAJU Completed B.Tech in Electrical & Electronics Engineering in 2002 from N.B.K.R Institute of Technology, Vidyanagar, Nellore(dt) affiliated to Sri Venkateswara University,

Tirupathi. and M.Tech in POWER ELECTRONICS in 2006 from R.G.M.C.E.T, Nandyal,Kurnool(dt) affiliated to JNTUA, Ananthapur . He is presently Working as Assistant Professor at Vishnu institute of technology, bhimavaram west godavari district, Andhrapradesh, India. E-mail id: callmenaveen@gmail.com