PERFORMANCE OF FUZZY LOGIC BASED MICROTURBINE GENERATION SYSTEM CONNECTED TO GRID/ISLANDED MODE

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1 International Journal of Fuzzy Logic Systems (IJFLS) Vol.2, No.3, July 22 PERFORMANCE OF FUZZY LOGIC BASED MICROTURBINE GENERATION SYSTEM CONNECTED TO GRID/ISLANDED MODE Sanjeev K Nayak an D N Gaonkar 2 &2 Department of Electrical an Electronics Engineering, National Institute of Technology Karnataka, Surathkal Mangalore, INDIA nayaksanjeev82@gmail.com ngaonkar@gmail.com ABSTRACT The microturbine generation (MTG) system are becoming the popular source of istribute generation (DG) ue to their fuel flexibility, reliability an power uality. The MTG system is a complicate thermoynamic electromechanical system with high spee of rotation, freuency conversion an its control strategy. In spite of several techniues to control high spee of microturbine is not accurate an reliable ue to their anti-interference problem. This paper presents a fuzzy logic controlle MTG system for gri connecte an islane moe of operation. The evelopment of fuzzy logic base spee governor inclues input an output membership function with their respective members. The loa variation on MTG system is performe using conventional PI controller an fuzzy logic controller are implemente in Matlab/Simulink an their results are compare with each other. The simulation result of MTG system shows the performance improvement of fuzzy logic governor over a conventional governor. KEYWORDS Distribute Generation, Microturbine PMSM, Power Converter, an Fuzzy logic.. INTRODUCTION Distribute Generation (DG) is preicte to play an increasing attention in the electric power system of the near future. DG is limite size of generation (25kW to MW) an interconnecte at the substation, istribution feeers or consumer loa levels []. The microturbine generation (MTG) system is a typical an practical system of DG source, because of its small scale generation with fuel flexibility, reliability an power uality. It has been applie in various fiels, such as peak power saving, co-generation, remote an premium power applications [2]. The MTG system compose of microturbine as a prime mover, permanent magnet synchronous machine (PMSM), power interfacing circuit for freuency conversion between generation an loa. A microturbine is thermoynamically complex mechanical system cannot be moelle accurately an it is ifficult to control the spee [2]. A microturbine is integrate with PMSM prouces high freuency AC power, where the operating spee ranges generally between 5, to 2, rpm. The power prouce at the generator is aroun k to 2 khz of freuency, this high freuency AC power is converteto DC then inverte back to 5 or 6Hz AC power using suitable converter an inverter [3]. The nonlinear controlle methos base on moel cannot solve the various control problems on MTG system, some avance control metho as have been applie such as incremental fuzzy PI controller, fuzzy PID controller an neural network controllers. Controlle algorithm base on fuzzy logic have been implemente in many processes the application of such control techniues has been motivate by the following reasons, ) it improves the robustness over a conventional linear control algorithm, 2) Simplifie control esign for ifficult system moel 3) Simplifie implementation[4]. This paper presents a fuzzy DOI :.52/ijfls

2 International Journal of Fuzzy Logic Systems (IJFLS) Vol.2, No.3, July 22 logic controlle spee governor an conventional transfer function governor for a microturbine to preict the performance of fuzzy logic controller. The microturbine is use as MTG system once it is compare with nominal controller connecte to the gri an isolate moe of DG system. The simulation result shows the performance of fuzzy logic controller for ifferent loa on MTG system. 2. MICROTURBINE GENERATION (MTG) SYSTEM Microturbines are smaller version of heavy uty gas turbines compact in size an components like compressor, heat exchanger, burner an turbine. Basically there are two types of microturbines, classifie base on construction an location of its components. One is a high spee single shaft esign with compressor an the turbines are mounte on the same shaft usually the PMSM is use to generate the electrical power. Another is split shaft esign that uses a power turbine rotating at 36 rpm an a conventional generator (usually inuction generator o r synchronous generator) connecte via a gearbox [6]. The microturbine system presente in this paper is base on gas turbine moel presente by W I Rowen[5] which was successfully aopter as microturbine generation(mtg) system by Huang Wei[6] connecte in parallel to the microgri, an use as DG system by Gaonkar[7] an Gua S R[8] in isolate moe. The turbine moel was propose by W I Rowen[5] is to be aopte as single shaft microturbine with fuzzy logic spee governor implemente in MATLAB/Simulink is shown in Figure., an the same is use as prime mover for the MTG with PMSM an power electronics circuit interfacing system shown in Figure s.5s - Tempreture controller G5 st5 G4 st4.8 - Tr Reference Temp f(u) Exhaust system f2(u) Ref Spee(pu) - u t K K2 Spee Governor. s - L O W Acceleration controller u t X.23 K3 Rotor Spee JT Rotor Inertia Fuel & Combustion G st G2 st2 f3(u) f4(u) e-st Turbine ynamics e-st G3 st3 Figure.. Fuzzy logic controlle microturbine Ref, Spee PLL Fuel Valve Fuzzy Logic Burner Spee Controller L Rt Lt Compressor Turbine Rectifier C Inverter PMSM GRID Exhaust air Heat exchanger Ambient air Loa Figure.2 Fuzzy logic controlle MTG system 42

3 International Journal of Fuzzy Logic Systems (IJFLS) Vol.2, No.3, July 22 The linear pattern is utilizes as generator moel applicable for transient analysis an the moel comprises temperature controller, fuel control, turbine ynamics, spee governor an acceleration controller blocks [7]. The spee governor for a microturbine can be ivie into roop regulation an non-rop regulation, which is utilize an esigne for the purpose of aopting the reuirement of ifferent loa characteristics. Spee controller is usually moelle using lea-lag transfer function or by PID controller. In this work a fuzzy logic base governor has been use as spee an change in spee are input member ship function, regulate spee as output membership function. The membership function can ajuste so that the governor can act with roop or as isochronous governor [7][8]. The fuel flow is controlle as a function of Vce are shown in a series of blocks incluing the valve position an flow ynamics, the output of low value selector (represente by Min in MATLAB/Simulink) is the lowest of the three inputs an results in the amount of fuel to the compressor-turbine, an Vce represents the final amount of fuel eman for that particular operating point an is an input to the fuel system [6][8]. The slow ynamics performance of the MTG system is focuse here base on the simplifie moel is built in with an assumption, i.e. MTG system is operating uner normal conition by neglecting the fast ynamics of microturbine (start up, shut own, internal faults, suen loss of power etc). In aition to that the propose moel employees per unit system to represents the microturbine as its control system with expectation of temperature, each important control block is iscusse as subsection in[7] [8]. The moel presente in this paper is concentrates on slow ynamics of MTG system. 3. FUZZY LOGIC Fuzzy logic controller is rule base controller where a set of rules represents a control ecision mechanism to correct the effect of certain cause use for generation systems. In fuzzy logic, the linguistic variables are expresse by fuzzy sets efine on their respective universe iscourse, to overcome the ifficulties of soft controlling fuzzy logic foun to be effective alternating to conventional control techniues[9]-[]. The configuration of fuzzy logic base system into four parts they are, Fuzzification, Knowlege Base, Interface Mechanism an Defuzzification. The lea lag transfer function compensator is replace by the euivalent fuzzy logic base spee governor, the esign of PI-like fuzzy knowlege base controller works on the area control error (ACE) an change in area control error ( ACE) is consiere 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) an linguistic variables as VHS: Very High Spee, HS: High Spee, NC: No Change, NS: Normal Spee, LS: Low Spee, VLS: Very Low Spee, 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 an U are consiere in a symmetrical triangular membership function. The membership function of ACE over the operating range of minimum an maximum values of ACE is shown in Figure.3 (a) (b) an (c). The membership function woul perform a mapping from the crisp values to a fuzzifie value, One such particular crisp input ACE is converte to fuzzifie value i.e..8/vhs.2/hs where.8 an.2 are membership grae membership function..8 VHS HS NS LS VLS VL L Z H VH LN N NC P LP Figure.3 a) Spee b) Change in spee c) Reg, spee 43

4 International Journal of Fuzzy Logic Systems (IJFLS) Vol.2, No.3, July 22 Corresponing to the linguistics variables VHS an HS in fuzzy system a membership graes are zero for all other linguistic values except VHS an HS. Here the crisp value input to the system will be converte to fuzzifie value consiering several membership graes corresponing to each linguistic variable. Same way the other input of ACE an U are fuzzifie. The output of the inference mechanism is a fuzzy value, an hence it is necessary to convert the fuzzy value to (crisp) real value sine, the physical process can t eal with fuzzy values. This operation which is inversely fuzzifie is known as efuzzification. The well known centre of efuzzification metho has been use for its simplicity. The major rawback of Mean of Maximum (MOM) metho is that it oes not use all the information converte by the fuzzy comman an hence it becomes ifficult in generating commans that run the system smoothly. U = Σ j * Uj Σ j U = Σ ( Membership I P * Output ( Membership correspon of ) U is etermine using the centre of gravity metho µj is the membership value of the lingivustic variables recommening the fuzzy control action, Uj is the precise numerical value corresponing to the fuzzy control action. The U obtaine from (9) is ae with the existing previous signal to obtain the actual output signal U which is given to the microturbine []. I P ing I P ) () Table. Fuzzy logic tables S p e e S p e e V L L Z H V H V L S L N L N L N N N C L S L N L N N N C P N S L N N N C P L P H S N N C P L P L P 3. MODELLING OF PMSM V H S N C P L P L P L P Base on the - reference frame, the stator voltage euitation of PMSM can be escribe as [2]. u u = = t t r i s r i s (2) Where, u, & are an components of the stator wining voltage, ψ & ψ are an components of stator flux, i,i are component of stator wining current, ω is 44

5 International Journal of Fuzzy Logic Systems (IJFLS) Vol.2, No.3, July 22 the angular spee, r s is the stator resistance. The flux linkage of PMSM can be expresse as, = L i PM = L i (3) Where, L, L are an axis inuctances, ψ PM is the permanent magnet rotor flux linkage, The electromagnetic torue euation of the PMSM is given as [2], T e = 2 3 p ( i i ) (4) Where p is the number of pole pairs, the motion euation is expresse as, T = J Ω t B Ω Ω = p e T L (5) (6) Where, ψ PM is the rotor flux linkage, J is the moment of inertia, B is the amping torue an T L is the motor loa. 3. POWER ELECTRONICS CONVERTER The power electronics interfacing is a critical component in the single shaft microturbine esign an represents significant esign challenge, especially in matching turbine output power to the connecte loa or gri. There are ifferent configurations available to interface the MTG power to loa. One possible is to use a three phase ioe rectifier, voltage source invert an filter. This reuires a separate start-up arrangement for the microturbine. The configuration use in this paper is assume to be brought to rate spee for the isolate moe of operation an biirectional power converter has been use for the gri connecte moe of operation. For a single shaft microturbine the power interfacing circuit is use to convert high freuency AC power prouce by the PMSM into usable electrical power. The power conitioning circuit is one of the critical components in a single shaft microturbine esign an represents significant challenge in esign. Especially in matching turbine output to reuire loa. The control structure for gri connecte moe of operation is shown in Figure 4. The gri sie converter operates as a controlle power source. The stanar PI controller is use to regulate the gri current in the synchronous frame in the inner control loops an the DC voltage regulator in the outer loop. It is seen that a PI controller regulates the DC bus voltage by imposing an i current component. I represents the active power component of the injecte current into the gri an i is it reactive component. In orer to obtain only a transfer of active power only a transfer of active power, the i current reference set to zero. The ecoupling terms are use to obtain inepenent control of i an i in. A PLL is use to synchronize the converter with the gri. The philosophy of the PLL is that the ifference between the gri phase an inverter phase angel can be reuce to zero using PI controller an locking the line sie inverter phase to gri [8]. 45

6 International Journal of Fuzzy Logic Systems (IJFLS) Vol.2, No.3, July 22 Figure 4: Line sie converter control for gri connecte moe For the isolate moe of operation the power conitioning unit consist of a three phase ioe rectifier, a voltage source inverter (VSI) an LC filter are use. The control system for a voltage source inverter is shown in Figure 5. A 66 Hz, voltage source fees a 5Hz, 5kW loa through an AC/DC/AC converter. The 48V, 66Hz AC power is first rectifie by six pulse ioe brige rectifier an filtere. DC link voltage is given to an IGBT two level inverter generating 5Hz. The IGBT inverter uses Pulse With Moulation (PWM) with 2 khz carrier freuency. The voltage is regulate at p.u.(48 Vrms) by a PI voltage regulator using abc to an to abc transformation. The first output voltage regulator is a vector containing the three moulation signals use by the PWM generator to generate six IGBT pulses [8]. Figure 5: Line sie converter control for isolate moe 3. SIMULATION RESULTS Before connection the MTG system to an electrical loa, the performance of microturbine controller is verifie by the fuzzy logic an nominal PI controller in Matlab/simulink. Form the performance test of microturbine it is ascertaine that, the fuzzy logic base controller settling time is less than nominal PI controller an also the oscillation are eliminate in fuzzy logic controller. The comparison stuy of fuzzy logic an nominal PI controller are consiere for the fuel eman, turbine torue, spee an power. The simulation is performe for a total uration of time t=8 secon. The turbine is loae in steps of half loa an full loa respectively. The variation of fuel eman an turbine torue with fuzzy logic an nominal PI controller are shown in Figures 6 an 7 respectively. From the reporte Figures 6 an 7 it is observe that, the settling 46

7 International Journal of Fuzzy Logic Systems (IJFLS) Vol.2, No.3, July 22 time of fuzzy logic controller is less then PI controller. With respect to fuel flow, the variation of turbine spee an power are shown in Figures 6 an 7. Fuel eman (p.u) Figure.6 Fuel eman Fuzzy logic controler PI contrller Turbine torue (p.u) Fuzzy logic controller PI controller Turbine spee(p.u.) Figure.7 Turbine torue Figure 8. Turbine spee Fuzzy controller PI controller Turbine power (p.u) Fuzzy logic controller PI controller Figure 9. Turbine power GRID CONNECTED MODE The MTG system in connecte to the gri is supplying a power to the gri is preicte on the basis of active an reactive power. The active an reactive power supplie by the fuzzy logic controlle MTG system is shown in Figure.. From the reporte Figure. it is observe that, the active power is 5kW an reactive power is almost zero. Hence it is ascertaine that the PMSM supplies only active power. The DC link voltage an turbine torue are shown in Figures an 2 respectively. From the reporte Figure2 it is observe that the turbine torue is 47

8 International Journal of Fuzzy Logic Systems (IJFLS) Vol.2, No.3, July 22 negative in per unit, it means that, the PMSM is running as generator. To generate the turbine toue the proportional fuel eman an spee are shown in Figures 4 an 5. The inverter output voltage an loa voltage are shown in Figures 6 an 7 respectively. From the Figures 6 an 7 it is observe that, the MTG system is supplying uality power to the gri. Active & reactive power x 4 - Active power in watts Reactve power in Var Figure. Actvie an reactive power Turbine spee (p.u) Time(s) Figure.4 Turbine spee DC link voltage (V) Figure. DC link voltage Turbine torue(p.u) Figure.5 Turbine torue 4 Troue (p.u) -.5 Inverter voltage (V) Fuel flow (p.u) Figure.2 Turbine torue Figure.3 Fuel flow Loa voltage (p.u.) Figure.6 Inverter voltage Figure.7 Loa voltage 3.2 Isolate moe The fuzzy logic controlle MTG system is connecte to an isolate moe an it performance is simulate for the total uration of 3sec. The variation of turbine fuel flow an torue are shown in Figure.8 an 9 respectively. From the reporte Figures 8 an 9 it is observe that, the fuel eman an turbine torue increase with increase in loa an ecreases with ecrease in loa. 48

9 International Journal of Fuzzy Logic Systems (IJFLS) Vol.2, No.3, July 22 The variation of turbine spee ue to the increase an ecrease of loa on the MTG system is shown in Figure. 2. The active an reactive power is one of the important parameters of MTG system in connecte to the loa. Since the MTG system power generation by PMSM, it can supply only the active power. it is observe from the reporte Figure.22. It has been measure between phase to groun voltage an current. The variation of loa voltage an current between phases to groun is shown in Figures 23 an 24 respectively. From the reporte Figures 23 an 24 it is observe that, the variation of loa voltage an current maintains the power uality. Fuel eman (p.u) Figure.8 Fuel eman Active & reactive power Active power in kw Reactive power in Var Figure.22 Active an reactive power Turbine torue (p.u) Turbine spee (p.u) Figure.9 Turbine torue Figure.2 Turbine spee Loa voltage (V) Loa current (A) Figure.23 Loa voltage Figure.24 Loa current CONCLUSIONS The Fuzzy logic controlle MTG system connecte to the gri an isolate loa has been implemente an simulate using Matlab/Simulink. Initially the microturbine controller verifie by the both nominal PI controller an fuzzy logic controller. Form the simulation stuy, the fuzzy logic controller is preferre over a nominal PI controller ue to minimum oscillation an steeling time. The fuzzy logic base verifie microturbine is use for the MTG system in connecte to the gri an isolate moel of operation. The evelope moel of MTG system inclues microturbine, PMSM an power electronics interfacing circuit. From the simulation stuy of the MTG system is supplying power to the gri an the performance of loa fallowing character is ascertaine. The fuzzy logic controlle MTG system will maintain the uality power an fewer harmonic. From the simulation stuy it is observe that, the active an reactive power injecte by the MTG system for gri connecte as well as isolate moe of operation. 49

10 REFERENCES International Journal of Fuzzy Logic Systems (IJFLS) Vol.2, No.3, July 22 [] P. P. Barker, an R. W. e Mello, 2, Determining the impact of istribute generation on power systems: Part - Raial Distribution systems, in Proc. IEEE Power Eng. Society. Summer Meeting, vol. 3, pp [2] Shijie Yan an Xu Wang Fuzzy Controlle with Tracking Differentiator for Microturbine Sixth international conference on fuzzy systems an Knowlege Discovery,FSKD-29 Tianjin,4th -6th August 29, pp. 97. [3] A. Al-Hinai an A. Feliachi, Dynamic moel of microturbine use as a istribute generation in Proc 34th South Eastern Symposiums on system theory, Huntsville, Alabama, 22,pp [4] Jenica Ileana Corcau an Eleonor Stoenescu Fuzzy logic controlle as power system stabilizer International Journal of Circuits, System an Signal Processing, Vol., Issue.3, 27, pp [5] W. I. Rowen, Simplifie Mathematical Representations of Heavy Duty Gas Turbines, Journal of Engineering for Power, Trans. ASME, vol. 5, no. 4, pp , Oct, 983. [6] Hung Wei, Wu Ziping, Niu Ming, Zhang Jianhua, Guo Yuanbo an Wu Chong Dynamic Moelling an Simulation of Microturbine Generation System for the parallel operation of microgri International Conference on Sustainable Power Generation an Supply,29 SUPERGEN-9, Nanjing, 6th -7th April 29, pp.-8. [7] D. N. Gaonkar an R N Patel Moeling an Simulation of Microturbine Base Distribute Generation System IEEE Power Inia Conference 26, New Delhi, 5th June 26. pp.5. [8] S. R. Gua. C.Wang an M H. Neherir, Moeling of Microturbine Power Generation Systems Electric Power Components an systems,532-56, Vol.34, Issue.9,pp.27-4,26. [9] U. Yolac an T Yalcinoz Comparison of fuzzy logic an PID controller for TCSC using MatLab 39th IEEE International Conference on Power Engineering, Vol.,24,pp [] B Anan an Ebenezer Jeyakumar Fuzzy logic base loa freuency control of hyro thermal system with non-linearity International Journals of Electrical an Power Engineering, Vol.3,Issue.2,29,pp.2-8. [] Shijie Yan an Xu Wang Active Disturbance Rejection Fuzzy Control of MTG Fuel pressure IEEE International Conference on Mechatronics an automation, 9th -2th August 29 Changchum, China. [2] J. B Ahn, Y. H. jeong, D.H. Kang an J.H.Park Development of High Spee PMSM for Distribute Generation Using Microturbine 3th IEEE Annual Conference on Inustrial Electronics Society, November 2-6,24,Busan, Korea. Authors Sanjeev K Nayak receive his (M Tech egree from UBDT College of Engineering D avengere, Kuvempu University Shimoga Karnataka, INDIA in the year 27, He worke as a faculty in the Department of Electrical an Electronics Engineering, at Nitte Meensakashi Institute of Technology, Bangalore for a uration of three years. Currently he is pursuing his Ph.D research in the Department of Electrical an Electronics Engineering at National Institute of Technology Karnataka Surathkal. His areas of research interests are Distribute Generation-Microturbine, Fuel cell an Power Quality. He publishe paper in national an international conference, in INDIA, Saui Arabia an Thailan. D. N. Gaonkar (M 28) has receive his Ph.D. egree from the Inian Institute of Technology Roorkee, Inia; in the year 28. He was a visiting research scholar at the University of Saskatchewan Canaa in the year 28. He has eite an written a chapter in the book title DISTRIBUTED GENERATION, which is publishe by INTECH publication Austria. He has publishe many papers in international journals an conferences. Presently he is working as an Assistant Professor in the Department of Electrical Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, INDIA. His research areas of interest are Power System Operation an Control, Power Electronics an Distribute Generation Systems. 5