1Fuzzy Coordination of FACTS Controllers for              Power systems                ELECTRICAL & ELECTRONICS ENGINEERIN...
2ABSTRACT                      This paper concerns the    of a multi-machine power system subjected tooptimization    and ...
3                                                          other power system controllers is very                         ...
4                                                  1) VAR control mode: the reference input is                            ...
53. CONTROL SCHEME                                      3.2. POD Controller                                               ...
6control performance in multimachine systems,          installed. This objective can be formulated asthe hybrid fuzzy coor...
7          Equation (1) is a general parameter-          system. Therefore the coordination betweenconstrained nonlinear o...
8through the UPFC1 and UPFC2. The outputsignals are command signals adjusted to theUPFC controllers 1 and 2. In this way, ...
9also be optimized by using the simulation          The    output    of    the   fuzzy-coordinationresults.               ...
105.2. Simulation in multi-machine system       Using      the   multi-machine   powersystem shown in Fig. 1, different di...
11Case 3: Changing of network parameters(Three-phase fault at Bus 9)       In order to verify the performance ofthe fuzzy ...
126. CONCLUSIONS        The paper presents a new fuzzy-coordination controller for the FACTSdevices in a multi-machine pow...
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Fuzzy coordination of facts controllers for

  1. 1. 1Fuzzy Coordination of FACTS Controllers for Power systems ELECTRICAL & ELECTRONICS ENGINEERING K.Sravani Srinija (3/4 EEE) G.Annapurneswari (3/4 EEE) Email: sravanisrinija@gmail.com Email: alekyagupta@yahoo.co.in Ph: 9963160279 Ph: 9290196174 Narasaroapet Engineering College
  2. 2. 2ABSTRACT This paper concerns the of a multi-machine power system subjected tooptimization and coordination of the a wide variety of disturbances and differentconventional FACTS (Flexible AC structures validate the efficiency of the newTransmission Systems) damping controllers in approach.multimachine power system. Firstly, theparameters of FACTS controller are Keywords:optimized. Then, a hybrid fuzzy logiccontroller for the coordination of FACTS FACTS,controllers is presented. This coordination Fuzzy Logic,method is well suitable to series connected Coordination,FACTS devices like UPFC, TCSC etc. in Fuzzy- Coordination Controller,damping multi-modal oscillations in multi- Damping,machine power systems. Digital simulations Stability
  3. 3. 3 other power system controllers is very important. Fuzzy-coordination controller is1. INTRODUCTION presented in this paper for the coordinated of Nowadays, FACTS devices can be traditional FACTS controllers. The fuzzyused to control the power flow and enhance logic controllers are rule-based controllers insystem stability. They are playing an which a set of rules represents a controlincreasing and major role in the operation and decision mechanism to adjust the effect ofcontrol of power systems. The UPFC (Unified certain cases coming from power system.Power Flow Controller) is the most versatile Furthermore, fuzzy logic controllers do notand powerful FACTS device .The parameters require a mathematical model of the system.in the transmission line, i.e. line impedance, They can cover a wider range of operatingterminal voltages, and voltage angle can be conditions and they are robust.controlled by UPFC. It is used for This paper focuses on theindependent control of real and reactive power optimization of conventional powerin transmission lines. Moreover, the UPFC oscillation damping (POD) controllers andcan be used for voltage support and damping fuzzy logic coordination of them. By usingof electromechanical oscillations. In this fuzzy-coordination controller, thepaper, a multimachine system with UPFC is coordination objectives of the FACTS devicessimulated. are quite well achieved. Damping of electromechanicaloscillations between interconnected 2. SYSTEM MODELsynchronous generators is necessary forsecure system operation. A well-designed 2.1. Power System ModelFACTS controller can not only increase thetransmission capability but also improve the A three machine nine bus interconnectedpower system stability. A series of approaches power system is simulated in this paper. Therehave been made in developing damping are two UPFCs in the power system: betweencontrol strategy for FACTS devices. The Bus2 Bus3 and, Bus6 Bus7. The diagram ofresearches are mostly based on single machine the power system model is shown in Fig. 1.system. However, FACTS devices are alwaysinstalled in multi-machine systems. Thecoordination between FACTS controllers and
  4. 4. 4 1) VAR control mode: the reference input is an inductive or capacitive Var request; 2) Automatic voltage control mode: the goal is to maintain the transmission line voltage at the connection point to a reference value. By the control of series voltage, UPFC can be operated in four different ways2.2. UPFC Model (UPFC Theory) 1) Direct voltage injection mode: the Basically, the UPFC have two reference inputs are directly the magnitudevoltage source inverters (VSI) sharing a and phase angle of the series voltage;common dc storage capacitor. It is connected 2) Phase angle shifter emulation mode: theto the system through two coupling reference input is phase displacement betweentransformers. One VSI is connected in shunt the sending end voltage and the receiving endto the system via a shunt transformer. The voltage;other one is connected in series through a 3) Line impedance emulation mode: theseries transformer. The UPFC scheme is reference input is an impedance value to insertshown in Fig. 2. in series with the line impedance; 4) Automatic power flow control mode: the reference inputs are values of P and Q to maintain on the transmission line despite system changes. Generally, for damping of power system oscillations, UPFC will be operated in the direct voltage injection mode. The mathematic model of UPFC for the dynamic simulation is shown in Fig.3The UPFC has several operating modes. Twocontrol modes are possible for the shuntcontrol:
  5. 5. 53. CONTROL SCHEME 3.2. POD Controller Commonly the POD controllers3.1. Traditional FACTS Damping Control involve a transfer function consisting of anScheme amplification link, a washout link and two Under a large disturbance, line lead-lag links. A block diagram of theimpedance emulation mode will be used to conventional POD controller is illustrated inimprove first swing stability. For damping of Fig. 5. In this paper the active power of thethe subsequent swings, as suggested before, transmission line is used as input signalUPFC will be operated in the direct voltageinjection mode. In this mode, the UPFCoutput is the series compensation voltage Vse. This voltage is perpendicular to the linecurrent I line and the phase angle of I line isahead of V se. Thus, as shown in Fig.4, thedamping control of the UPFC is the same as a The UPFC POD controller worksTCSC POD control scheme. By the control of effectively in single machine system. In orderthe magnitude of V se, the series to improve the dynamic performance of acompensation damping control can be multi-machine system, the behavior of theachieved. controllers must be coordinated. Otherwise the power system will be deteriorated. 3.3. Fuzzy Logic Control In order to keep the advantage of the existing POD controller and to improve its
  6. 6. 6control performance in multimachine systems, installed. This objective can be formulated asthe hybrid fuzzy coordinated controller is the minimization of a nonlinear programmingsuggested in this paper. problem expressed as follows: Fuzzy logic controller is one of themost practically successful approaches forutilizing the qualitative knowledge of asystem to design a controller .In this paper themain function of the fuzzy logic control is to where f(x) is the objective function, x are thecoordinate the operation of FACTS parameters of the POD controller. A(x) arecontrollers. In section 4 the design of the the equality functions and B(x) are thefuzzy logic coordinated controller is presented inequality functions respectively. Particularlyin detail. B(x) indicate the restrictions of the POD parameter. (i.e. the restrictions of lead-lag links and wash-out links). In this simulation, only the inequality functions B(x) are4. PARAMETER OPTIMIZATION AND necessary.CONTROLLER DESIGN The objective function is extremely important4.1. Parameter Optimization for a Single for the parameter optimization. In this paperMachine POD Controller the objective function is defined as follows: In order to work effectively underdifferent operating conditions, manyresearches are made on the controllerparameter optimization. Parameters of the where, δ(t, x) is the power angle curve of thePOD controller can be adjusted either by trial generator and t1 is the time range of theand error or by optimization technique. In this simulation. With the variation of thepaper the parameters of the POD controller controller parameters x, the δ(t, x) will also be are optimized using a nonlinear programming changed. The power system simulationalgorithm. program PSD (Power System Dynamic) is Originally, the aim of the employed in this simulation to evaluate theparameter optimization is to damp oscillations performance of the POD controller.of power systems where the UPFCs are
  7. 7. 7 Equation (1) is a general parameter- system. Therefore the coordination betweenconstrained nonlinear optimization problem POD controllers must be taken into account.and can be solved successfully. In this paperthe Matlab Optimization Toolbox is applied. The optimization starts with the pre-selected initial values of the POD controller.Then the nonlinear algorithm is used toiteratively adjust the parameters, until theobjective function (2) is minimized. These sodetermined parameters are the optimalsettings of the POD controller. The flow chart of the parameteroptimization is shown in Fig. 6 the proposedoptimization algorithm was realized in asingle machine power system. In thisoptimization the prefault state and post-faultstate are the same where δ(0)= δ(∞) . Theoptimized parameters are given in Appendix To cope with the coordination2. problem, the optimization based coordination and the feedback signal based coordination4.2. Fuzzy Logic Coordinated Controller have been developed. Also fuzzy logic hasDesign successfully been applied to coordination. The Most of the FACTS POD controllers method used in is using the fuzzy logicbelong to the PI (proportional integral) type controller to coordinate the input signal of theand work effectively in single machine FACTS controller.system. Especially, after the parameter In this paper the fuzzy logicoptimization, the damping of power system controller is to coordinate the parameters ofoscillations is perfectly achieved. However FACTS controllers. The structure of thethe performance of the above mentioned POD proposed fuzzy-coordination controller iscontrollers deteriorates in multi-machine shown in Fig. 7. Where the inputs P UPFC1 and P UPFC2 are the active power flow
  8. 8. 8through the UPFC1 and UPFC2. The outputsignals are command signals adjusted to theUPFC controllers 1 and 2. In this way, theconventional POD controllers are tuned byusing fuzzy-coordination controllers. Thefuzzy coordination controller involvesFuzzification, Inference and Defuzzificationunit. The membership function of the small set is: Where x, namely P UPFC1 or P UPFC2, is the input to the fuzzy controller. Similarly the big set membership function is: and the medium set membership function is:4.2.1 Fuzzification Fuzzification is a process wherebythe input variables are mapped onto fuzzyvariables (linguistic variables). Each fuzzifiedvariable has a certain membership function. The parameters L and K, as shown inThe inputs are fuzzified using three fuzzy Appendix 3, are determined basing upon thesets: B (big), M (medium) and S (small), as rated values of UPFCs. These parameters canshown in Fig. 8.
  9. 9. 9also be optimized by using the simulation The output of the fuzzy-coordinationresults. controller is4.2.2 Inference Control decisions are made based onthe fuzzified variables. Inference involvesrules for determining output decisions. Due tothe input variables having three fuzzified where i u corresponds to the value of controlvariables, the fuzzy-coordination controller output for which the membership values in thehas nine rules for each UPFC controller. The output sets are equal to unity.rules can be obtained from the systemoperation and the knowledge of the operator. 5. SIMULATION RESULTSTable 1 shows the inference system. 5.1. Parameter optimization To determine the degree of The parameter optimization is made inmemberships for output variables, the Min- single machine system. Fig. 9 demonstratesMax inference is applied. Both of the two the improvement in damping of power systemUPFC controllers use the same inference oscillation. The initial and optimized values ofsystem. Only the inputs of them are the POD controller are given Appendix 2. Fig.exchanged. (as shown in Fig. 7) 9. Parameter optimization in a single machine infinite bus system.4.2.3 Defuzzification The output variables of the inferencesystem are linguistic variables. They must beconverted to numerical output. The fuzzy-coordination controller uses centroid method.
  10. 10. 105.2. Simulation in multi-machine system Using the multi-machine powersystem shown in Fig. 1, different disturbancesand different network parameters aresimulated. The performance of the fuzzy-coordination controller for UPFC in dampingpower system oscillations is examined. Thefollowing simulations are made for evaluatingthe performance of the proposed controller. In Case 2: Changing of operation conditionsthis paper machine G3 is taken as the (Three-phase fault at Bus 3)reference. To validate the robustness of fuzzy-Case 1: Three-phase fault at Bus 2 coordination controller the pre-fault operating conditions of the power network is changed to A three-phase fault of 100 ms duration P1=0.195, P2=0.28. Moreover the fault type isis simulated at Bus 2. Fig. 10 presents the also different: a three-phase fault of 110 msresults of the examined power system with duration is simulated at Bus 3. Fig. 11 showsfuzzy-coordination controller. From Fig. 10 it the results of the simulation. The proposedcan be seen that with the proposed controller, controller acts pretty well with the variation ofthe dynamic performance of the power system operation condition.is quite improved. The pre-fault operatingcondition (in p.u.) is: P1=0.105, P2=0.185.
  11. 11. 11Case 3: Changing of network parameters(Three-phase fault at Bus 9) In order to verify the performance ofthe fuzzy coordination controller for thechanging of system parameters, the reactanceof transformers T1 and T2 are increased by20%. A three-phase fault of 100 ms issimulated at Bus 9. The simulation results, asshown in Fig. 12, illustrate that the proposedcontroller is robust in parametric change. Thepre-fault operating condition (in p.u.) is:P1=0.10, P2=0.120.
  12. 12. 126. CONCLUSIONS The paper presents a new fuzzy-coordination controller for the FACTSdevices in a multi-machine power system todamp the electromechanical oscillations. Thefuzzy coordination controller is designedbased on the conventional POD controllers.The amplification part of the conventionalcontroller is modified by the fuzzycoordination controller. The performance ofthe proposed method is simulated over a widerange of operating conditions anddisturbances and its robustness is proved.Both inter-area and local modes oscillationsare quite damped using this new controller.The proposed control scheme adopts theadvantages of the conventional PODcontroller and it is not only robust but alsosimple and being easy to be realized in powersystem.REFERENCES: • HVDC Transmission and distribution systems by Gupta, • V. Sitnikov, W. Breuer, D. Povh, D. Retzmann, E. Teltsch, benefits of Powe r electronics for • Transmission Enhancement・ • Load-Flow Analysis with Respect to a possible synchronous Interconnection of Networks of • UCTE and IPS/UPS.

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