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International Journal of Fuzzy Logic Systems (IJFLS) Vol.2, No.3, July 2012 The linear pattern is utilizes as generator model applicable for transient analysis and the model comprises temperature controller, fuel control, turbine dynamics, speed governor and acceleration controller blocks [7]. The speed governor for a microturbine can be divided into droop regulation and non-drop regulation, which is utilized and designed for the purpose of adopting the requirement of different load characteristics. Speed controller is usually modelled using lead-lag transfer function or by PID controller. In this work a fuzzy logic based governor has been used as speed and change in speed are input member ship function, regulated speed as output membership function. The membership function can adjusted so that the governor can act with droop or as isochronous governor [7][8]. The fuel flow is controlled as a function of Vce are shown in a series of blocks including the valve position and flow dynamics, the output of low value selector (represented by Min in MATLAB/Simulink) is the lowest of the three inputs and results in the amount of fuel to the compressor-turbine, and Vce represents the final amount of fuel demand for that particular operating point and is an input to the fuel system [6][8]. The slow dynamics performance of the MTG system is focused here based on the simplified model is built in with an assumption, i.e. MTG system is operating under normal condition by neglecting the fast dynamics of microturbine (start up, shut down, internal faults, sudden loss of power etc). In addition to that the proposed model employees per unit system to represents the microturbine as its control system with expectation of temperature, each important control block is discussed as subsection in[7] [8]. The model presented in this paper is concentrates on slow dynamics of MTG system. 3. FUZZY LOGIC Fuzzy logic controller is rule based controller where a set of rules represents a control decision mechanism to correct the effect of certain cause used for generation systems. In fuzzy logic, the linguistic variables are expressed by fuzzy sets defined on their respective universe discourse, to overcome the difficulties of soft controlling fuzzy logic found to be effective alternating to conventional control techniques[9]-[10]. The configuration of fuzzy logic based system into four parts they are, Fuzzification, Knowledge Base, Interface Mechanism and Defuzzification. The lead lag transfer function compensator is replaced by the equivalent fuzzy logic based speed governor, the design of PI-like fuzzy knowledge base controller works on the area control error (ACE) and change in area control error (∆ACE) is considered as input to the fuzzy logic controller. For the automatic generation control problem the input to fuzzy controller for ith area at a particular instant are ACEi(t) and linguistic variables as VHS: Very High Speed, HS: High Speed, NC: No Change, NS: Normal Speed, LS: Low Speed, VLS: Very Low Speed, LN: Large Negative, N: Negative, P: Positive, LP: Large Positive, VL: Very Low, L: Low, Z: Zero, H: High, VH: Very High- respectively. All the variables of ACE, ∆ACE and ∆U are considered in a symmetrical triangular membership function. The membership function of ACE over the operating range of minimum and maximum values of ACE is shown in Figure.3 (a) (b) and (c). The membership function would perform a mapping from the crisp values to a fuzzified value, One such particular crisp input ACE is converted to fuzzified value i.e. 0.8/VHS + 0.2/HS where 0.8 and 0.2 are membership grade membership function. VHS HS NS LS VLS VL L Z H VH 111 0.8 0.5 0.2 LN N NC P LP 0.5 000 0.5 Figure.3 a) Speed b) Change in speed c) Reg, speed 43PDF Image | PERFORMANCE OF FUZZY LOGIC BASED MICROTURBINE GENERATION SYSTEM CONNECTED TO GRID/ISLANDED MODE
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