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IMPROVED FUZZY-PI CONTROL SCHEME FOR POWER FLOW OF DISTRIBUTED GENERATION Azuki Abdul Salam1, Nik Azran Ab Hadi2, Fatimah Zaharah Hamidon3 and Ismail Adam4 1 Universiti Kuala Lumpur-British Malaysian Institute [email protected] 2 Universiti Teknikal Malaysia Melaka, [email protected] 3 Universiti Kuala Lumpur-British Malaysian Institute [email protected] 4 Universiti Kuala Lumpur-British Malaysian Institute [email protected] ABSTRACT This paper presents the mathematical model of the Proton Exchange Membrane Fuel Cell (PEMFC) and analyzes the structure of a grid connected PEMFC generation system. In order to get better waveforms of grid current, a Fuzzy-PI controller is introduced into the grid connected PEMFC generation system. The current control scheme for grid connected PEMFC generation system is a PI controller scheme, which would lead to large transient response due to the load increases. Thus, a Fuzzy-PI control scheme is proposed in order to improve the power flow control. The PI controller parameters automatically, according to changes of system parameters. When the proposed grid connected PEMFC generation system using the Fuzzy-PI controller is simulated in Matlab/Simulink, the results show that the proposed control scheme works effectively for the power flow grid. Keywords: PEMFC, Fuzzy-PI, Matlab/Simulink 1. Introduction The interest in Distributed Generation (DG) systems is rapidly increasing because larger power plants are becoming less feasible in many regions due to increasing fuel costs and stricter environmental regulations. Distributed generation systems (DGS) have mainly been used as a standby power source for improving the power quality[1]. The introduction of DG to the distribution system has a significant impact on the flow of power and voltage conditions to the customers and utility equipment. The impacts were mainly seen on voltage support and improved power quality,diversification of power sources, reduction in transmission and distribution losses, transmission and distribution capacity release and improved reliability[2]. The increasing number of DG requires a new technique for the operation and management of the electricity grid to enhance power supply reliability and power quality. Fuel Cell Distributed Generation (FCDG) systems can be strategically placed on any site in a power system at the distribution level for grid reinforcement. It will eliminate the need for system upgrades and improves system integrity, reliability and efficiency[3]. Therefore, proper controllers need to be designed for a FCDG system to make its performance characteristics as desired[4]. Fuel cells are of various types such as Proton Exchange Membrane Fuel Cell (PEMFC), Alkaline Fuel Cells (AFC),Molten Carbonate Fuel Cells (MCFC) and Solid Oxide Fuel Cells(SOFC). Among these fuel cell E-Journal of Artificial Intelligence & Computer Science (E-ISSN: 2289-5965), Vol 3, 2015. Published by http://WorldConferences.net 31 types, the Proton Exchange Membrane Fuel Cells and Solid Oxide Fuel Cells have great potential for distributed generation applications. The simulation model is developed for the grid-connected PEMFC generation system by combining the individual component models and the controllers designed for the power conditioning units. Matlab/Simulink SimPowerSys toolbox had been used to model the fuzzy logic controller. Hence, in this paper, the Fuzzy-PI control structure had been developed for a FCDG system with active power management and reactive power control capability. Simulation results are given to show the overall system performance for active and reactive power management. 2. PEM Fuel Cell Generation System The PEMFC consists of a solid polymer electrolyte sandwiched between two electrodes (anode and cathode). In the electrolyte, only ions can exit and electrons are not allowed to pass through. So, the flow of electrons needs a path like an external circuit from the anode to the cathode, to produce electricity because of a potential difference between the anode and cathode. The overall electrochemical reactions in PEMFC fed with a hydrogen containing anode gas and an oxygen containing cathode gas are as follows: At anode reaction, 2H 2 4H 4e (1) O2 4H 4e 2H 2 O (2) 2H 2 O2 2H 2O electricit y heat (3) At cathode reaction, Overall reaction The output stack voltage V is defined as a function of the stack current, reactant partial pressures, fuel cell temperature, and membrane humidity. The potential difference between the anode and cathode is calculated using the Nernst’s equation and Ohm’s law and can be written as follows: RT V N0 E0 2F PH PO 0.5 ln 2 2 B ln CI Rint I PH 2O (4) Where: No is the number of cells connected in series; Eo is the voltage associated with the reaction free energy; R is the universal gas constant; T is the temperature; is the current of the fuel cell stack; F is the Faraday's constant. The relationship between molar gas flow through the valve is proportional to its partial pressure and can be expressed as, qH K an (5) K 2 PH 2 qH 2 O PH 2 O M H2 H2 K an K H 2O M H 2O (6) Where, qH2 qH2O PH2 PH2O PO2 : Molar flow of hydrogen : Molar flow of water : Partial pressure of hydrogen : Partial pressure of water : Partial pressure of oxygen E-Journal of Artificial Intelligence & Computer Science (E-ISSN: 2289-5965), Vol 3, 2015. Published by http://WorldConferences.net 32 KH2 KH2O Kan MH2 MH2O : Hydrogen valve molar constant : Water valve molar constant : Anode valve molar constant : Molar mass of hydrogen : Molar mass of water In this work, the model used to simulate the PEMFC generation system is given as shown in Fig.1. Using the equations derived for the PEMFC model, a simulation model is developed by using the SimPowerSystems toolbox of Matlab/Simulink. The parameters of the simulation model are given in Table 1. Fig.1 Model of PEMFC Table 1: Parameters of PEMFC Generation System Parameters Faraday’s constant, F Universal gas constant, R Values 96484600 C/mol 8314.47 J/kmol-2 K No load voltage, E0 1.229 V Number of cells, N 420 Constant, K 1.0883 x 10-7kmol/(s-1 A) Valve molar constant for hydrogen, KH2 4.22 x 10-5kmol/(s-1atm) Valve molar constant for oxygen, KO2 2.11 x 10-5kmol/(s-1atm) Valve molar constant for water, KH2O 7.716 x 10-6 kmol/(s-1 atm) Response time for hydrogen flow, 3.37 sec E-Journal of Artificial Intelligence & Computer Science (E-ISSN: 2289-5965), Vol 3, 2015. Published by http://WorldConferences.net 33 TH2 Response time for water flow, TH2O 18.418 sec Response time for oxygen flow, TO2 6.74 sec Utilization factor , U 0.8 Reformer time constant , τ1, τ2 2 Conversion factorenergydensity, CV 2 Activation current constant, B 0.04777 Activation voltage constant, C 0.0136 V Stack internal resistance, Rint 0.00303 PI gain constants,K5 ,K6 10 Ratio of hydrogen to oxygen, rHO 1.168 Methane reference signal, Qmethref 0.000015 3. Fuzzy-PI Control Strategy The Fuzzy-PI controller used in the outer voltage control loop to replace the conventional PI controller would greatly reduce the overshoot and get a faster response. The Fuzzy-PI controller can follow nonlinear load since it can adjust the control parameters for different operation[5]. The two input arguments of fuzzy controller are voltage error and the derivative of the voltage error. According to the fuzzy rules, the fuzzy logic, reasoning part would adjust the PI parameters and send to the PI controller. With the real time updating of the PI parameters,the control process can be more accurate than conventional PI controllers with fixed PI gains.Fuzzy logic control is the evaluation of a set of simple linguistic rules to determine the control action. To develop the rules of the fuzzy logic, a good understanding of the process to be controlled is needed, and it does not require a complicated mathematical model[6]. Fig.2 shows the schematic diagram of Fuzzy-PI generation system. In the proposed work, the Fuzzy-PI control technique is used to generate the gating signals. After proper amplification and isolation, the switching signals obtained are given to the switching devices of the PWM converter. The DC link capacitor voltage is remaining constant by a fuzzy logic controller. This controller is designed using the fuzzy inference system, which is the FIS editor,as is shown in Fig 3. The input and output membership functions of the each variable error, change in error and change in firing angle are shown in Fig.4, Fig.5 and Fig.6 respectively. Fuzzy logic is characterized by five fuzzy sets are Negative Big (NB), Negative Small (NS), Zero (Z), Positive Small (PS), and Positive Big (PB) for each input and output variables. The triangular membership function is used for simplicity. The implication is performed using Mamdani type min-operator. Defuzzification is employed using the centroid method. The Fuzzy rule based of the proposed fuzzy-PI is shown in Table 2. E-Journal of Artificial Intelligence & Computer Science (E-ISSN: 2289-5965), Vol 3, 2015. Published by http://WorldConferences.net 34 Fig.2 Fuzzy-PI generation system Fig.3 FIS editor Fig.4 Input Membership function of FLC for error(e) Fig.5 Input Membership function of FLC for change in error(de) Fig.6 Output Membership function of FLC for firing angle(dalpha) E-Journal of Artificial Intelligence & Computer Science (E-ISSN: 2289-5965), Vol 3, 2015. Published by http://WorldConferences.net 35 Table 2: Fuzzy rule base Change of error (de) Error (e) NB NS Z PS PB NB NB NB NB PS Z NS NB NB NS Z PS Z NB NS Z PS PB PS NS Z PS PB PB PB Z PS PB PB PB 4. Simulation Results and Discussion The model of a three-phase grid-connected VSI system and the proposed controller are simulated using MATLAB/Simulink environment. In this model, the proposed controller of the DG unit is designed based on PQ control mode, and the Fuzzy-PI controller is incorporated to find the optimum parameters of this controller. In order to illustrate the power flow control of the DG unit and to verify the performance of the proposed controller, the model is simulated for power exchange between the grid connected PEMFC generation system and the utility. Thus, the control strategy can use the Fuzzy-PI technique. In this instance, the load is supplied by the DG unit and its excess power is automatically fed to the grid. This demonstrates that the proposed controller offers stable and satisfactory power flow grid-connected PEMFC generation system. In Fig.7, the load is increased to 2.63 p.u active power at 0.65s. In this case the balance power is supplied by the grid, while the DG unit still injects a sustained output power. Now the load is decreased to 0.9p.u active power at 0.9s. In Fig.8, the load is increased to 0.53p.u inductive reactive power at 0.65s. In this case the balance power is supplied by the grid, while the DG unit still injects a sustained output power. Now the load is decreased to 0.17p.u inductive reactive power at0.9s. The Total Harmonic Distortion (THD) is calculated using FFT analysis tool which is provided in Matlab/Simulink software. The nominal THD value is <5%. By using the Fuzzy - PI Controller, the THD is calculated as 4.44% in Fig.9. Fig.7 Active power flow in the grid connected PEMFC generation system E-Journal of Artificial Intelligence & Computer Science (E-ISSN: 2289-5965), Vol 3, 2015. Published by http://WorldConferences.net 36 Fig.8 Reactive power flow in the grid connected PEMFC generation system Fig.9 THD Waveform using Fuzzy PI controller 5. Conclusion In this paper, the power flow grid connected PEMFC generation system was presented. The main objective is to control and meet the power demand variations occurring on load side. For this purpose, a Fuzzy-PI controller is applied to the system to generate the required gate pulses for IGBTs of VSI for proper switching to meet the load demand. The Fuzzy-PI controller is used in inner loop current control on the VSI, which can regulate parameters of the PI controller automatically. The proposed control strategy for this kind of distribution system helps in the proper operation of each power source under power quality disturbances. The effectiveness of the proposed system can be verified by using the MATLAB/SIMULINK environment. Acknowledgement This work is financially supported by the UniKL grant UniKL/IRPS/str11046 to conduct this research. References 1. J. W Jung, and A. Keyhani, “Control of a Fuel Cell Based Z-Source Converter”, IEEE Trans. Energy Conversion, Vol. 22, Issue. 2, pp. 467-476, 2007. 2. A. A. Salam, S Salim, F. Z. Hamidon, I. Adam, “Fuzzy Logic Controller for Photovoltaic Generation System in Islanding Mode Operation”, International Conference on Artificial Intelligence and Computer Science (AICS2013),2013. 3. T. Choi and K. Y. Lee, “Interface of a fuel cell distributed generator with distribution system network”, Proc. of the IEEE Power Energy Society General Meeting,Calgary, Canada, 2009 4. M. N. Arafat, S. Palle, I. Husain, Y. Sozer, “Transition control strategy between standalone and grid connected operation of voltage source inverters”, IEEE Energy Conversion Congress and Exposition (ECCE), pp.1994-2000, Sept. 2011. E-Journal of Artificial Intelligence & Computer Science (E-ISSN: 2289-5965), Vol 3, 2015. Published by http://WorldConferences.net 37 5. S. M. Ayob, Z. Salam and N.A. Azli, “Inverter control using simplified Fuzzy-PI controller”, IET International Conference on Power Electronics, Machines and Drives, Brighton, UK, April 19-21, 2010, pp.1-6. 6. A. A. Salam, N. A. A. Hadi, “Fuzzy Logic Controller for Shunt Active Power Filter”,4th International Conference on Engineering Technology and Technopreneuship (ICE2T), 2014 E-Journal of Artificial Intelligence & Computer Science (E-ISSN: 2289-5965), Vol 3, 2015. Published by http://WorldConferences.net 38