# Design and Simulation of Fuzzy Logic controller for DSTATCOM In Power System

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2 Now a day there has been growing interest in applying fuzzy theory [5] to controller design in many engineering fields. The fuzzy controller has very attractive features over conventional controllers. It is easy to be implemented in a large scale nonlinear dynamic system and not so sensitive to the system models, parameters and operation conditions. In particular human knowledge can be included in control rules with ease. Therefore investigation of fuzzy theory application in power system control grows rapidly [7]. The present paper is an attempt to understand what the fuzzy PI controller is and how well it can perform in comparison with the conventional PI controller. The fuzzy PI controller is better than the conventional for the non-linear systems. 2. System configuration Fig.1. shows the basic circuit diagram of three phase three wire distribution system with unbalanced and nonlinear load and the DSTATCOM with fuzzy logic controller. Fig. 1. Block diagram of system The DSTATCOM consists of a three-phase pulse width modulated (PWM) voltage-source converter (VSC) using six insulated-gate bipolar transistors (IGBTs), three interface inductors, and one dc capacitor. The DSTATCOM injects currents into the point of common coupling in such a way so as to maintain balancing and harmonic elimination in the source currents. The VSI operation is supported by the dc storage capacitor with voltage across it. 3 Modeling of control scheme (SRF algorithm) of DSTATCOM SRF theory is based on the transformation of currents in synchronously rotating d-q frame. The d-q component of load currents are computed from following relations. ISSN : Vol. 3 No.10 October

3 Fig.2. Block diagram of SRF algorithm The average values of i Ld and i Lq are obtained form the low pass filters as i Lddc and and i Lqdc and i d and iq are the output of the DC voltage PI controller and AC voltage PI controller. The desired reference source currents in d-q frame are obtained as: I sd = i lddc +i d (1) Isq= i lqdc +i q (2) Where i sd and i sq are the estimated DC components of active and reactive currents of reference source currents in d-q frame. 4 Design of self tuned fuzzy PI controller The fuzzy PI controller is driven by a set of control rules rather than by two constants proportional and integral gains. Fuzzy controller has data base and rule base, the function of data base is to provide necessary information to the rule base in the form of membership functions. The basic function of rule base is to represent the control policy of control engineer in the form of set of production rules as If (process operator) then (control output) Because of these rules and some formulas, which change the gains automatically and continuously with the outputs, the controller becomes self tuning and can handle non-linear systems effectively. As far as stability is concerned, a fuzzy PI controller maintains the same stability as the conventional controller. It has two inputs Error(e) and change of error ( e) and one output u. The membership functions for the inputs and output is shown in fig. where in which the domain is divided into 7 equal regions, denoted by NB(negative big), NM(negative medium), NS(negative small), ZE(zero), PS(positive small), PM(positive medium), PB(positive big).the shape of each membership function is triangular. ISSN : Vol. 3 No.10 October

4 Fig.3. Membership Functions The table shows the rule base. The rule is represented such as If e is NM and e and PS then u is NS. Fig. 4. Fuzzy rule Similarly membership function for gain updating factor is obtained Fig. 5. Membership function for Beta The rule for gain updating factor is represented as If e is E and e is E then β is β. The output scaling factor is modified by self tuning mechanism. The normalized values of error, change in error and output is given by e N =N e e, e N =N e e, and u=(β N u ) u N (3) where Ne and N e are the input scaling factor of error and change of error respectively and Nu is the output scaling factor. The rule base is shown in fig 6 ISSN : Vol. 3 No.10 October

5 Fig. 6. Fuzzy rule 5. Simulation Results and Discussions To verify the proposed control method, digital simulation using MATLAB SIMULINK has been carried out. Simulation model is shown in Fig 7 and Simulink model of fuzzy logic based AC and DC voltage controllers are shown in Fig 8 & 9 respectively. The Fuzzy logic based self tuned PI controller is used to generate gate pulses for each leg of Voltage source converter of DSTATCOM. Fig.7. Simulink model of proposed system Fig.8. Simulink model of fuzzy logic based AC voltage controller ISSN : Vol. 3 No.10 October

6 , Fig.9. Simulink model of fuzzy logic based DC voltage controller The simulation is done firstly with unbalanced R-L load. The Fig 10. shows that the Fuzzy logic based controller is able to force the source to deliver the balanced currents by compensating reactive power. \ Fig.10. Waveforms of Load Voltage, unbalanced load current, balanced source current, source voltage with unbalanced load ISSN : Vol. 3 No.10 October

8 Appendix SOURCE PARAMETERS Line Voltage Source Frequency Ripple filter 415V 50Hz Rr=4 Ω, Cr= 35μF DSTATCOM PARAMETERS DC Bus voltage Switching Frequency DC Bus Capacitor 800V 10kHz 8000 μf.. ISSN : Vol. 3 No.10 October

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