SIMULATION OF FUZZY LOGIC CONTROLLED GRID INTERACTIVE INVERTER

Transcription

1 30 SIMULATION OF FUZZY LOGIC CONTROLLED GRID INTERACTIVE INVERTER İbrahim SEFA, Necmi ALTIN Department of Electrical Education, Faculty of Technical Education, GEMEC Group, Gazi University, Besevler, AnkaraTURKEY Keywords: Grid interactive inverter, fuzzy logic controller, current control Abstract: In this study, a voltage source grid interactive inverter is modeled and simulated in MATLAB/Simulink. Inverter is designed as current controlled and a fuzzy logic controller used to shape the inverter output current. A line frequency transformer is used to obtain the galvanic isolation and output of the inverter is filtered by a LC type filter. Results of the MATLAB/Simulink simulation show that inverter output current is in sinusoidal waveform and in phase with line voltage, and current harmonics are in the limits of international standards (<5%). 1. INTRODUCTION Clean, economic and secure energy production gains importance with the increasing of the word s power demand. So, new type of energy sources has gained popularity. The sun and the wind energy are more common alternative energy sources. Previously, they were used to supply local loads in remote areas, outside the national grid. Later, they have become some of main sources [13]. Since the energy produced from the wind and the solar are transferred to grid line, the way of the transforming direct current (DC) energy to alternating current (AC) is very important. This process has generally been achieved by an inverter. Alternative energy sources are operates in stand alone mode or grid interactive mode. It is difficult to obtain an efficient operation in stand alone mode and usually high capacity battery groups are required. In grid interactive mode, inverter supplies the local loads and if the generated energy higher than the demand the excess energy is export to the grid. So battery groups are unneeded. On the other hand, control of the grid interactive inverter is more complex than the stand alone one [3]. Grid interactive inverters can be designed as current controlled or voltage controlled. However small synchronization errors are more than dangerous in voltage controlled inverter so current controlled inverter is recommended to control the export of power to the grid [4]. Conventional PI or PID control strategies have been commonly used in grid interactive inverter applications. Acceptable performance can be achieved by using a fixed PI controller designed to work around a determined operating point, but poor transient performance is often obtained when changes between different operating points occur because of the changing dynamics of the plant. Operating points of the grid interactive inverters varies with the natural conditions such as solar radiation or wind speed. Also grid specifications may change while the inverter is operating. Fuzzy logic control theory is a mathematical discipline based on vagueness and uncertainty. It allows one to use nonprecise or illdefined concepts. Fuzzy logic control is also nonlinear and adaptive in nature that gives it robust performance under parameter variation and load disturbances. Many control approaches and applications of fuzzy logic control have appeared in the literature since Mamdani published his experiences using a fuzzy logic

2 İ. SEFA, N. ALTIN Simulation of Fuzzy Logic Controlled Grid Interactive Inverter 31 controller on a testbed plant in a laboratory [5]. An extensive introduction to the historical development, current state and concepts involving fuzzy control systems can be found in [68]. The fundamental advantage of the fuzzy logic controller over the conventional controller is a less dependence of the mathematical model and system parameters as known widely [9]. In this paper, a current controlled grid interactive inverter is simulated using MATLAB/Simulink software. A Fuzzy Logic Controller (FLC) is used to shape the inverter output current. The rule based FLC is track the current reference under varying conditions such as inverter output level and grid impedance. LC filter as an output filter and the line frequency transformer to prevent DC current injection to the grid line are also preferred in this system. The simulation results show that inverter output current is in phase with the line voltage and in sinusoidal waveform. Current harmonics are also found in the limits of international standards (<5%). This paper organized as fallows: Section II describes the grid interactive inverter. Section III describes the FLC. Section IV presents the simulation results of fuzzy logic controlled grid interactive inverter. Section V is the conclusions. connected systems. Currentcontrolled mode has been chosen in this study and the inverter operates as a current source. This minimizes the effect of voltage harmonics on the output current and improves the power quality and current controlled inverter much less sensitive to phase errors. The equivalent model of the gridconnected inverter system operated in currentcontrolled mode is shown in Fig. 1. The current injected into grid can match the grid voltage on frequency and phase, so the power can be injected into grid with unity power factor. Phase locked loop (PLL) control is employed to match the frequency and phase of grid voltage. The reference current is generated by the sine generator based on the voltage phase and the given amplitude of output current. The FLC generates the switching pattern for single phase full bridge VSI and used to control the inverter output current according to reference current. V DC S 1 C DC S 2 S 3 S 4 G 1 G 2 G 3 G 4 ~ Line Frequency Transformer L f C f Lg V g NOT NOT 2. GRID INTERACTIVE INVERTER The grid interactive inverter provides the interface between the renewable energy sources and the utility. A typical grid interactive inverter injects a sinusoidal current to the grid, and must meet the international standards like IEC61727, IEEE1547 and EN , and radio frequency interference due to high frequency switching should be under control [10]. The grid interactive inverter can be designed as voltage or current controlled [11]. When the inverter is designed as a voltage controlled it operates as voltage source, and the gridconnection system is equivalent to the parallel connection of two voltage sources. In this mode the inverter is controlled to generate a sine waveform voltage in the same frequency and phase with grid. The output current, injected into grid, depends on the grid voltage quality and the small phase errors can overload the inverter. So voltage control is not a good choice for grid V tri V sin V tri V sin 1 d cd Fuzzy Logic Controller e d/dt ce Ref. Current Gen. Fig. 1. Proposed grid interactive inverter PLL Some type of inverters use a high frequency transformer embedded in DCDC converter or DCAC inverter, others are interconnected to the grid line via a line frequency transformer, and some inverters do not include transformer. Line frequency transformer can prevent DC current injection problem, provide galvanic isolation between the DC source and the grid line and makes the grounding easier. Although they have disadvantages like size, weight and price, the line frequency transformer is a natural solution of DC current injection and preferred in this study [1,3].

3 32 UNIVERSITY OF PITESTI ELECTRONICS AND COMPUTERS SCIENCE, SCIENTIFIC BULLETIN, No. 8, Vol.2, 2008 As seen from Fig. 1 the system consists of a renewable energy source, a DCAC voltage source inverter, a line frequency transformer and a LC filter. The renewable energy source can be photovoltaic modules, fuel cells or a small wind turbine. A fuzzy logic controller is used for the current control. Inverter output voltage is boosted to the line voltage with a line frequency transformer. The line frequency transformer also prevents DC ripple injection in current waveform and provides galvanic isolation between the inverter and the grid line. In addition the transformer simplifies the grounding of the DC energy source. A LC filter is employed to reduce the high frequency harmonic components in current waveform due to PWM switching and to reduce the output current THD. 3. FUZZY LOGIC CONTROLLER Fuzzy Logic Controller is one of the most successful applications of fuzzy set theory, introduced by Zadeh in 1965 [12]. Its major features are the use of linguistic variables rather than numerical variables. The general structure of the FLC is shown in Fig 2. As seen from Fig. 2, a FLC is comprises four principal components. where u m is the mth input variable, v is the output, A m n is the nth membership set and B i is the output membership set belongs to ith rule. Inference engine simulates the human decision process. This unit infers the fuzzy control action from the knowledge of the control rules and the linguistic variable definitions. Therefore, the knowledge base and the inference engine are in interconnection during the control process. Firstly active rules are detected by substituting fuzzified input variables into rule base. Then these rules are combined by using one of the fuzzy reasoning methods. MaxMin and MaxProduct are most common fuzzy reasoning methods. The defuzzifier converts the fuzzy control action that infers from inference engine to a nonfuzzy control action. Different defuzzification methods are used such as center of gravity, mean of maxima and min max weighted average formula. Center of gravity is the most common defuzzification method and given in Eq. (2): z * = µ ( z). z µ ( z) (2) where µ(z) is the grade of membership that obtained inference engine, z is the outputs of each rules and z * is the defuzzified output [13]. Fig. 2. Structure of FLC The fuzzifier converts input data into suitable linguistic values by using fuzzy sets. The fuzzy sets are introduced with membership functions such as triangle, sigmoid or trapezoid. The knowledge base consists of a data base with the necessary linguistic definitions and control rule set. The rule set of knowledge base consists of some fuzzy rules that define the relations between inputs and outputs. Usually, fuzzy rules are expressed in the form of IF THEN fuzzy conditional statements; R i :IF u m =A n n m and u m1 =A m1 THEN v=b i (1) FLC for Grid Interactive Inverter The first important step in the fuzzy controller definition is the choice of the input and output variables. In this study, the output voltage error and its rate of change are defined as input variables and change in duty cycle is the controller output variable. The three variables of the FLC, the error, the change in error and the change in duty cycle, have seven triangle membership functions for each. The basic fuzzy sets of membership functions for the variables are as shown in the Figs. 3 and 4. The fuzzy variables are expressed by linguistic variables positive large (PL), positive medium (PM), positive small (PS), zero (Z), negative small (NS), negative medium (NM), negative large (NL), for all three variables. Table 1 shows the rule base for the FLC. A rule in the rule base can be expressed in the form: If (e is NL) and (de is NL), then (cd

4 i İ. SEFA, N. ALTIN Simulation of Fuzzy Logic Controlled Grid Interactive Inverter 33 is NL). The rules are set based upon the knowledge of the system and the working of the system. The rule base adjusts the duty cycle for the PWM of the inverter according to the changes in the input of the FLC. The number of rules can be set as desired. The numbers of rules are 49 for the seven membership functions of the error and the change in error (inputs of the FLC). The rule base of the FLC is shown in Table 1. Fig. 3. Membership functions for error and change in error Fig. 4. Membership functions for change in duty cycle The commonly used Min Max inference method is implemented. Defuzzification is done using center of gravity method to generate nonfuzzy control signal for change in duty ratio of the PWM switching of grid interactive inverter [14]. Change in error (ce) Table 1. Rule base of FLC Error (e) NL NM NS Z PS PM PL NL NL NL NL NL NM NS Z NM NL NL NL NM NS Z PS NS NL NL NM NS Z PS PM Z NL NM NS Z PS PM PL PS NM NS Z PS PM PL PL PM NS Z PS PM PL PL PL PL Z PS PM PL PL PL PL 4. SIMULATION RESULTS A single phase fuzzy logic controlled grid interactive inverter is simulated in MATLAB/Simulink [15]. Simulink model of the simulated system is shown in Fig. 5. Inverter is designed as current controlled to prevent overloading the inverter because of short time synchronization errors and fuzzy logic controller is used to shape the inverter output current. [Iref] Goto [Sin] From5 20 Gain2 [Iinv] From7 u Abs u Abs1 e K Gain4 K Gain3 0<Id<0.1 e Out1 Subsystem Fuzzy Logic Controller 1 s Integrator 0<Id<0.999 Product >= oolea NOT Goto9 [Pulses] Triangle1 Goto6 v [Vgrid] V2 T Vdc = 400 V VSI g A B <,,, > From17 L v V_inv V1 1 2 L2 C i I4 I1 [Iinv] Goto4 L1 Goto8 [Igrid] i I3 K Gain1 Freq V (pu) wt Sin_Cos 1phase PLL AC Voltage Source T1Goto10 [Sin] T2 1 2 Discrete, Ts = 5e006 s. Goto3 [Vinv] MultiWinding Transformer Goto5 [Iload] v V3 [Vload] Goto1 Fig. 5. Simulink model of the system

5 34 UNIVERSITY OF PITESTI ELECTRONICS AND COMPUTERS SCIENCE, SCIENTIFIC BULLETIN, No. 8, Vol.2, 2008 The FLC is designed with Fuzzy Logic Toolbox [16]. A grid voltage sample is sensed with PLL and is used to generate the current reference signal to provide the current injected to the grid in the same phase and same frequency with the grid voltage. A line frequency transformer which prevents the DC current injection and a LC filter is used at output of the inverter. Inverter is the classic IGBT equipped voltage source full bridge inverter. Fig. 6 shows the reference current and inverter output current waveforms. It is shown that fuzzy logic controlled inverter current tracks the reference current waveform and two waveforms are close to each other. The inverter output current and grid voltage is shown in Fig. 7, and inverter output current harmonics are seen Fig 8. Line frequency transformer is used to boost the output voltage up to the line voltage, prevent DC ripple injection and obtain galvanic isolation between the line and renewable energy source. Fig. 8. Grid Interactive inverter output current harmonics 4. CONCLUSIONS Fig. 6. Reference and inverter output current Fig. 7. Inverter output current and grid voltage As seen from the figures that inverter current is sinusoidal waveform and synchronized with grid voltage frequency and phase. Grid interactive inverter output current THD is 3.35% and this is in the limits (3.35<5) of international standards. In this study a fuzzy logic controlled grid interactive inverter is designed and simulated. The inverter consists of the VSI structure, the line frequency transformer, the LC output filter and the FLC. The FLC is designed with Fuzzy Logic Toolbox and the simulations are performed in MATLAB/Simulink. Simulation results show that Fuzzy logic controlled inverter output current tracks the reference current and is in phase with the line voltage with FLC. Also, the current harmonics are in the limits of international standards (<5%). This controller can be easily implemented with a controller board, which can integrate the MATLAB/Simulink simulations and hardware control such as dspace controller boards. ACKNOWLEDGEMENT This work has been supported by Gazi University Academic Research Projects Unit.

6 İ. SEFA, N. ALTIN Simulation of Fuzzy Logic Controlled Grid Interactive Inverter 35 REFERENCES [1] S.B. KJAER, J.K. PEDERSON, F. BLAABJERG, A review of singlephase gridconnected inverters for photovoltaik modules, IEEE Transactions on Industry Applications, September/October, Vol. 41, No.5, 2005, pp [2] S.bSAHA, V.P. SUNDARSINGH, Novel gridconnected photovoltaic inverter, IEE Proc. Gener. Trunsm. Distrib. March, Vol. 143, No. 2, 1996, pp [3] İ. SEFA, N. ALTIN, Simulation of current controlled grid interactive inverter, TPE2006 3rd Conference on Technical and Physical Problems in Power Engineering, Ankara (Turkey), May 2006, pp [4] K. MASOUD, G. LEDWICH, Aspects of grid interfacing: current and voltage controllers, d99.pdf [5] E.H. MAMDANI, Application of fuzzy algorithms for control of a simple dynamic plant, IEEE Proc., Vol. 121, No. 12, 1974, pp [6] C.C. LEE, Fuzzy logic in control systems: fuzzy logic controller part 1, IEEE Trans. Syst. Man Cybern., Vol. 20, No. 2, 1990, pp [7] C.C. LEE, Fuzzy logic in control systems: fuzzy logic controller part 11, IEEE Trans. Syst. Man Cybern., Vol. 20, No. 2, 1990, pp [8] M. BERENGUEL, E.F. CAMACHO, F.R. RUBIO, P.C.K LUK, Incremental fuzzy PI control of a solar power plant, Control Theory and Applications, IEE Proceedings, Vol. 144, Issue 6, 1997, pp [9] S. PREMRUDEEPREECHACHARN, T. POAPORNSAWAN, Fuzzy logic control of predictive current control for gridconnected single phase inverter, TwentyEighth IEEE Photovoltaic Specialists Conference, 2000, pp [10] M. CALAIS, J. MYRZIK, T. SPOONER, V.G. AGELIDIS, Inverters for singlephase grid connected photovoltaic systems an overview, 33 rd IEEE Annual Power Electronics Specialists Conference, PESC 02, Cairns (Australia), Vol. 2, 2002, pp [11] H. GU, Z. YANG, D. WANG, W. WU, Research on control method of doublemode inverter with gridconnection and standalone, 5 th International Power Electronics and Motion Control Conference, IPEMC '06, Vol. 1, 2006, pp.1 5. [12] L.A. ZADEH, Outline of a new approach of the analysis of complex system and decision processes, IEEE Trans. Syst., Man, Cybern., Vol. SMC3, No. 1, 1963, pp [13] I. ATACAK, O.F. BAY, Bulanık mantık denetimli seri aktif güç filtresi kullanarak harmonik gerilimlerin bastırılması, Gazi Üniv. Müh. Mim. Fak. Der. Cilt 19, No 2, 2004, pp [14] L.X. WANG, Stable adaptive fuzzy control of nonlinear systems, IEEE Transactions on Fuzzy Systems, May, Vol. 1, No. 2, 1993, pp [15] The Mathworks Inc., MATLAB/SIMULINK Release Notes for Release 14 with Service Pack 3. [16] Fuzzy Logic Toolbox 2 User Guide, The Mathworks, Inc., 2005.