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40220130405010 2-3
40220130405010 2-3
40220130405010 2-3
40220130405010 2-3
40220130405010 2-3
40220130405010 2-3
40220130405010 2-3
40220130405010 2-3
40220130405010 2-3
40220130405010 2-3
40220130405010 2-3
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40220130405010 2-3

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  • 1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING & ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME TECHNOLOGY (IJEET) ISSN 0976 – 6545(Print) ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), pp. 104-114 © IAEME: www.iaeme.com/ijeet.asp Journal Impact Factor (2013): 5.5028 (Calculated by GISI) www.jifactor.com IJEET ©IAEME APPLICATION OF HYBRID NEURO FUZZY CONTROLLER FOR AUTOMATIC GENERATION CONTROL OF THREE AREA POWER SYSTEM CONSIDERING PARAMETRIC UNCERTAINITIES CH. Ravi Kumar Dr. P.V.Ramana Rao Assistant Professor/E.E.E, University College of Engg & Tech. Acharya Nagarjuna University Guntur - 522 510, India Professor & H.O.D/E.E.E, University College of Engg & Tech. Acharya Nagarjuna University, Guntur - 522 510, India ABSTRACT This paper presents the application of an Adaptive Neuro Fuzzy Inference System (ANFIS) based intelligent hybrid neuro fuzzy controller for Load Frequency Control of a Three Area Power System considering parameter uncertainties. The designed controller is found to work satisfactorily for wide range of variation in parameters up to ±50%, meeting the required specifications. The dynamic response of the system has been studied for 1% and 10% step load perturbations in area2. The performance of the proposed Neuro Fuzzy Controller is compared against Fuzzy Integral controller. Comparative analysis demonstrates that the proposed intelligent Neuro Fuzzy controller is the most effective of all in improving the transients of frequency deviations against small step load disturbances. Simulations have been performed using Matlab/Simulink. Keywords: Automatic Generation Control, Area Control error, Fuzzy Integral Control, Artificial Neural Networks, ANFIS. I. INTRODUCTION Automatic Generation Control or Load Frequency Control is important in Electrical Power System design and operation. In the event of sudden load perturbation in any area the deviations of frequencies of all the areas and the tie-line powers occur, which have to be corrected to ensure generation and distribution of good quality electric power. This is achieved by AGC, the main objective of which is to keep the system frequency and inter area tie-line power as near to scheduled values as possible through suitable control action. Many researchers have applied different control strategies, such as classical control, optimal state feedback control etc. to the AGC problem in order 104
  • 2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME to improve performance. They were designed for one operating point only. The model is usually made of reduced Power System, which includes many generators, turbines and speed governors etc. Some parameters of the model change depending on the operating condition of Power System. Controllers which are designed based on a fixed plant model may not work when some system parameters have been varied. The advent of intelligent control techniques has solved this problem to a great extent. Neuro-Fuzzy systems for example have emerged from the fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) and form a popular frame work for solving real world control problems. There are several approaches to integrate ANN and FIS and very often choice depends on the application. One such important integration is the Adaptive Neuro Fuzzy Inference System which is presently available in Matlab. In this study an ANFIS based intelligent hybrid neuro fuzzy controller is proposed as the supplementary controller for AGC of three – area interconnected system. The dynamic response of the system has been studied for 1% and 10% step load perturbation in area-2. A comparison of the proposed controller is made with the Fuzzy Integral controller to show the relative goodness of the proposed control strategy. The settling times, overshoots and under shoots of the frequency deviations are taken as performance indices. Comparative analysis shows that the proposed hybrid neuro fuzzy controller is the most effective of all in improving the transients of frequency deviations against small step load disturbances. II. CONFIGURATION OF THREE-AREA POWER SYSTEM Tie-line Area 1 Area 2 Area 3 Fig.1 Configuration of Three area Interconnected system As shown in fig1, the three-area interconnected system is taken as a test system in this study. The conventional AGC scheme has two control loops: The primary control loop, which controls the frequency by self-regulating feature of the governor, however, frequency error is not fully eliminated; and the supplementary control loop, which has a controller that can eliminate the frequency error with the help of conventional integral action or any suitable controller. The main objective of supplementary control is to restore balance between each control area load and generation after a load perturbation so that the system frequency and tie-line power flows are maintained at their scheduled values. So the control task is to minimize the system frequency deviations in the three areas and the deviation in the tie-line power flow ∆Ptie between any two areas under the load disturbances ∆Pd1 or ∆Pd2 or ∆Pd3 in three areas. This is achieved conventionally with the help of a suitable integral control action. The supplementary controller of the ith area with integral gain Ki is therefore made to act on ACEi, given by (1), which is an input signal to the controller. 105
  • 3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME n ACEi = ∑ ∆Ptie,ij + Bi ∆f i (1) j =1 Where ACEi is area control error of the ith area ∆f i = Frequency error of ith area ∆Ptie,ij = Tie-line power flow error between ith and jth area Bi = frequency bias coefficient of ith area II. FUZZY LOGIC CONTROLLERS The concept of fuzzy logic was developed to address uncertainty and imprecision which widely exists in engineering problems. Fuzzy logic controllers are rule based controllers. The design of fuzzy logic controllers involves four stages. i. Fuzzification ii. Knowledge base iii. Inference engine iv.Defuzzification Fuzzification: The process of converting a real number into a fuzzy number is called fuzzification. Knowledge base: This includes, defining the membership functions for each input to the fuzzy controller and designing necessary rules which specify fuzzy controller output using fuzzy variables. Inference engine: This is mechanism which simulates human decisions and influences the control action based on fuzzy logic. Defuzzification: This is a process which converts fuzzy controller output, fuzzy number, to a real numerical value. III. FUZZY INTEGRAL CONTROLLER This is a combination of Conventional integral controller and Fuzzy controller. For the proposed controller the mamdani fuzzy inference engine is used and the inference mechanism is realized by seven triangular membership functions (MFs) for each of the three linguistic variables (ACEi, dACEi/dt, Ci) with suitable choice of intervals of the MFs as shown in figs 2,3 & 4. Fig.2 Input Membership Function for ACE Fig.3 Input Membership Functions for d (ACE)/dt 106
  • 4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME Fig.4 Output Membership Functions for Ci Here ACEi and dACEi/dt act as the inputs of the fuzzy logic controllers and Ci is the output of fuzzy logic controller. The number of linguistic terms used for each linguistic variable determines the quality of control which can be achieved using fuzzy logic controller. Generally as the number of linguistic terms increases, the quality of control improves but this improvement comes at the cost of increased complexity on account of computational time and memory requirements due to increased number of rules. Therefore, a compromise between quality of control and complexity involved is needed to choose the number of linguistic terms, each one of which is represented by membership function, for each linguistic variable. In this study seven linguistic terms have been chosen for each of the three variables. The appropriate fuzzy linguistic terms used in this study are given as table 1. Table 1. Fuzzy Linguistic terms NB Negative Big NM Negative Medium NS Negative Small ZE Zero PS Positive Small PM Positive Medium PB Positive big d/dt(ACE) Defuzzification has been performed by using bisector of area method. The control rules for the proposed controller are very simple and have been developed from view point of practical systems operation and by trial and error methods. The fuzzy rules as used in this study are given in table 2. NB NM NS ZE PS PM PB Table 2. Rule base for Fuzzy Integral Controller ACE NB NM NS ZE PS PM NB NB NB NB NM NS NB NB NM NM NS ZO NB NM NM NS ZO PS NM NM NS ZO PS PM NM NS ZO PS PM PM NS ZO PS PM PM PB ZE PS PM PB PB PB 107 PB ZO PS PM PM PB PB PB
  • 5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME Fig.5 shows Simulink Model for Three area Power System with Fuzzy Integral Control Gain6 20.6 Gai n 1 s Integrator2 20 Gai n7 0.2 1 Subtract8 0.5s+1 Governor1 Subtract 1 0.2s+1 Fuzzy Logic Controller Turbi ne1 1 10s+0.6 Generator1 Subtract1 AREA1 du/dt Derivative Gain2 Scope1 1 s 2 Gain8 1 s Subtract4 Integrator 0.2 AREA2 Integrator3 Scope 1 Fuzzy Logic Controller2 Subtract9 1 0.3s+1 du/dt Gain5 Derivative1 1 0.6s+1 Governor2 Subtract2 Turbi ne2 8s+0.9 Subtract3 Gain1 Step1 16 16.9 Gain3 Gain9 1 s Scope6 Generator2 1 s 2 -K- AREA3 Integrator1 Subtract6 Integrator4 1 du/dt Subtract7 Fuzzy Logic Controller1 1 0.2s+1 Subtract10 0.5s+1 Governor3 Turbine3 1 10s+0.6 Subtract5 Generaor3 Gai n4 Derivati ve2 Gain10 20 20.6 Fig 5. Simulink Model for Three area Power System with Fuzzy Integral Control IV. THE PROPOSED HYBRID NEURO FUZZY CONTROLLER In this work an Adaptive network based inference system (ANFIS) is proposed in order to generate fuzzy membership functions and control rules for the hybrid neuro fuzzy controller. A fuzzy integral controller is used to provide the required training data. The controller design process consists of generating input – output data pairs to identify the control variables range and fuzzy membership functions and then to tune or adapt them using an ANFIS network structure. The controller inputs are area control error (ACE), and the rate of change of area control error d(ACE)/dt and the output is the control signal. Steps to design Hybrid Neuro fuzzy controller: 1. Draw the simulink model of power system under consideration with Fuzzy integral controller and simulate it with the given rulebase. 2. Collect the training data while simulating with fuzzy integral controller. The two inputs ACE and d(ACE)/dt and the output signal of the controller form the training data. The training data gives as much information as possible about the plant behavior for different load perturbations. 3. Use anfisedit to create .fis file. 108
  • 6. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME 4. Load the training data collected in step2 and generate the FIS with suitable (like gaussian/gbell etc.) membership functions. 5. Train generated FIS with the collected data up to a certain number of epochs. In this study ANFIS is trained with back propagation algorithm, using ten epochs and step loads of 1% and 10%. Fig.6 shows Simulink Model for Three area Power System with ANFIS Control Gain6 20.6 Gain 20 1 1 1 0.2s+1 Subtract8 Subtract 0.5s+1 Governor1 Turbine1 10s+0.6 Generator1 Subtract1 ANFIS Control ler 1 AREA1 du/dt Derivative Gain2 Scope5 1 s 2 Subtract4 Integrator AREA2 Scope7 ANFIS Controller 2 Subtract9 1 1 1 8s+0.9 0.3s+1 Subtract2 du/dt 0.6s+1 Governor2 Gain1 Turbine2 Generaor2 Subtract3 Gain5 Derivative1 Step1 16 16.9 Gain3 1 s 2 AREA3 Scope6 Integrator1 1 du/dt Fuzzy Logic Controller1 Subtract7 Derivative2 Gai n10 0.5s+1 Governor3 Turbine3 1 1 0.2s+1 Subtract10 Subtract6 10s+0.6 Subtract5 Generator3 Gain4 20 20.6 Fig6. Simulink Model for Three area Power System with ANFIS Control V. RESULTS AND DISCUSSIONS In the present work Automatic Generation Control of three area interconnected power system has been developed using Fuzzy integral controller and ANFIS control to demonstrate the performance of load frequency control using Matlab/Simulink package. Figs 7 to 14 respectively represent the plots of change in system frequency for 1% and 10% step load variations considering parameter variations upto ±50%. Two types of Simulink models are developed with Fuzzy integral and Hybrid Neuro Fuzzy controllers to obtain better dynamic behavior. The results obtained are also given in Tables 3 and 4 along with the Parameter variations which are given in Table5. 109
  • 7. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME Case I: For 1% Step load Perturbation -4 6 Change in frequency with Fuzzy integral controller x 10 Del f1 Del f2 Del f3 D iatio in freq cy (p .) ev n uen .u 4 2 0 -2 -4 -6 0 5 10 15 20 25 30 35 40 45 50 Time in Seconds Fig7. Frequency deviations ∆f1, ∆f2& ∆f3 with Fuzzy Integral Control 6 x 10 -4 Change in frequency with ANFIS controller Del f1 Del f2 Del f3 D i to i feunyp . e a nnr qec ( . ) v i u 4 2 0 -2 -4 -6 0 5 10 15 20 25 Time in Seconds 30 35 40 45 50 Fig8. Frequency deviations ∆f1, ∆f2& ∆f3 with ANFIS Control -4 Change in frequency with ANFIS control considering +50% Parameter variations x 10 6 Del f1 Del f2 Del f3 Cag inr qec( . . hne feuny u p ) 4 2 0 -2 -4 -6 0 5 10 15 20 25 30 35 40 45 50 Time in Seconds Fig9. Frequency deviations ∆f1, ∆f2& ∆f3 with ANFIS Control considering +50% Parameter variations -4 6 Change in frequency with ANFIS control considering -50% Parameter variations x 10 Del f1 Del f2 Del f3 D v tio infr q e c (p .) e ia n e u n y .u 4 2 0 -2 -4 -6 0 5 10 15 20 25 30 35 40 45 50 Time in Seconds Fig10. Frequency deviations ∆f1, ∆f2& ∆f3 with ANFIS Control considering -50% Parameter variations 110
  • 8. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME Case II: For 10% Step load Perturbation 4 x 10 -3 Change in frequency with Fuzzy Integral Control Del f1 Del f2 Del f3 C a g infr q e c (p .) hne e u n y .u 2 0 -2 -4 -6 -8 0 5 10 15 20 25 Time in Seconds 30 35 40 45 50 Fig11. Frequency deviations ∆f1, ∆f2& ∆f3 with Fuzzy Integral Control Change in frequency with ANFIS Control -3 2 x 10 Del f1 Del f2 Del f3 C a g inf e u n y( . . h n e r q e c pu) 0 -2 -4 -6 -8 -10 0 5 10 15 20 25 30 35 40 45 50 Time in Seconds Fig12. Frequency deviations ∆f1, ∆f2& ∆f3 with ANFIS Control -3 2 Change in frequency with ANFIS control considering +50% parameter variations x 10 Del f1 Del f2 Del f3 D v tio infre u n yp .) e ia n q e c ( .u 0 -2 -4 -6 -8 -10 0 5 10 15 20 25 30 35 40 45 50 Time in Seconds Fig13. Frequency deviations ∆f1, ∆f2& ∆f3 with ANFIS Control considering +50% Parameter variations Change in frequency with ANFIS control considering (-50%) Parameter variations -3 4 x 10 Del f1 Del f2 Del f3 D via n in F u n e tio rq e cy(p .) .u 2 0 -2 -4 -6 -8 -10 0 5 10 15 20 25 30 35 40 45 50 Time in Seconds Fig14. Frequency deviations ∆f1, ∆f2& ∆f3 with ANFIS Control considering -50% Parameter variations 111
  • 9. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME VI. CONCLUSIONS Table 3: Comparative study of Settling time and Peak overshoots for 1% step load variation Controllers Fuzzy Integral ANFIS ANFIS for +50% Parameter variations ANFIS for -50% Parameter variations Settling time in (Sec) ∆f ∆f Area 1 Area 2 ∆f Area 3 Peak overshoot (p.u.) X 10-4 ∆f ∆f ∆f Area 1 Area 2 Area 3 15 10 25 15 15 10 0.25 -1 4 5 0.25 -1 20 20 20 -1.5 5 -1.5 10 10 10 -1 5 -1 Table 4: Comparative study of Settling time and Peak overshoots for 10% step load variation Controllers Fuzzy Integral ANFIS ANFIS for +50% Parameter variations ANFIS for -50% Parameter variations Settling time in (Sec) ∆f ∆f Area 1 Area 2 ∆f Area 3 Peak overshoot (p.u.) X 10-3 ∆f ∆f ∆f Area 1 Area 2 Area 3 20 15 25 15 20 15 -1.5 -1.5 -8 -8 -1.5 -1.5 15 15 15 -1.8 -8 -1.8 12 12 12 -1.5 -8 -1.5 Table 5: Parameter variations Nominal value Parameter Governor Time Constant (Seconds) Turbine Time Constant (Seconds) Generator Time Constant (Seconds) Variations considered Areas 1 & 3 Area2 Areas 1 & 3 Area2 0.2 0.3 0.1 – 0.3 0.15 - 0.45 0.5 0.6 0.25 - 0.75 0.3 – 0.9 5 4 2.5 – 7.5 2-6 In this study, Hybrid Neuro Fuzzy approach is employed for an Automatic Generation Control (AGC) system. The proposed controller can handle the non linearity’s and parametric uncertainties and at the same time is faster than the Fuzzy integral controller. The effectiveness of the proposed controller in increasing the damping of local inter area modes of oscillation are demonstrated using a three area interconnected power system. Also the simulation results are compared with Fuzzy integral controller. The results show that the proposed ANFIS controller is having improved dynamic response and at the same time faster than Fuzzy integral controller. From the above tables, the responses obtained reveal that ANFIS controller has better settling performance than Fuzzy integral controller. Therefore Intelligent control approach using ANFIS is more accurate and faster than fuzzy integral control scheme even for complex and dynamic systems, with parametric variations. 112
  • 10. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] O.I.Elgerd, Electric Energy Systems Theory: An Introduction. Newyork: McGraw-Hill, 1982. Kundur. P, Power system stability and control, McGraw-Hill, Inc., 1994. Ibraheem, Prabhat Kumar, and Dwaraka P.Kothari, “Recent philosophies of automatic generation control strategies in power systems,” IEEE Transactions on Power Systems, 20, no.1, pp. 346-57, February 2005. C.S. Indulkar and B.Raj, “Application of Fuzzy controller to automatic generation control,” Elect. Machines Power Syst., vol. 23, no. 2, pp.209-220, Mar-Apr. 1995. Chang C.S., Fu W., “Area load-frequency control using fuzzy gain scheduling of PI controllers,” Electric Power system Research, vol.42, no.2,pp. 145-52,1997. J. Talaq and F. Al-Basri, ”Adaptive fuzzy gain scheduling for load – frequency control,” IEEE Trans.Power Syst., vol.14, no.1, pp.145-150, Feb.1999. D.K. Chaturvedi, P.S. Satsangi, and P.K. Karla, “Load frequency control: A generalized neural network approach,” Elect. Power Energy control: A generalized neural network approach, “Elect. Power Energy Syst., vol.21, no.6, pp.405-415, Aug.1999. Y.L. Karnavas and D.P.Papadopoulos,” AGC for autonomous Power System using combined intelligent techniques,” Elect. Power Syst.Res. vol.62, no.3,pp. 225-239, Jul.2002 S.K.Aditya and D.Das,”Design of load frequency controllers using genetic algorithm for two area interconnected hydro power system,” Elect.Power Compon. Syst., vol.31, no.1, pp.8194, Jan.2003. Ibhan Kocaarslan, Erugrul Cam, “Fuzzy logic controller in interconnected electric Power systems for load-frequency control,” Electrical Power and Energy Systems, vol.27, no.8, pp.542-549, 2005. L.H.Hassan, H.A.F. Hohamed, M.Moghavemi, S.S.Yang,” Automatic generation control of power system with fuzzy gain scheduling integral and derivative controllers,” International Journal of Power, Energy and Artificial Intelligence, vol.1, no.1, pp.29-33, August 2008. Sathans and A.Swarup “Intelligent Automatic Generation Control of Two area Interconnected Power System using Hybrid Neuro Fuzzy Controller” World academy of Science, Engineering and Technology 60 2011. Gayadhara Panda, Siddhartha Panda and C.Ardil, “Hybrid Neuro Fuzzy Approach for Automatic Generation Control of Two –Area Interconnected Power System”, International Journal of Computational Intelligence 5:1 2009. Surya Prakash, S.K.Sinha, “Load frequency control three area interconnected hydro-thermal reheat power system using artificial intelligence and PI controllers”, International Journal of Engineering, Science and technology vol.4, No.1, 2011, pp.23-37. Ch.Ravi Kumar, P.V.Ramana Rao, “Automatic Generation Control of Three area Interconnected Power System using Hybrid Neuro Fuzzy Controller”, International Journal of Electrical Engineering Research and Applications vol1 Issue4, September -2013. R. Arivoli and Dr. I. A. Chidambaram, “Multi-Objective Particle Swarm Optimization Based Load-Frequency Control of a Two-Area Power System with Smes Inter Connected using Ac-Dc Tie-Lines”, International Journal of Electrical Engineering & Technology (IJEET), Volume 3, Issue 1, 2012, pp. 1 - 20, ISSN Print : 0976-6545, ISSN Online: 0976-6553. J.Srinu Naick and Dr. K. Chandra Sekar, “Application of Genetic Algorithm and Neuro Fuzzy Control Techniques for Automatic Generation Control of Interconnected Power Systems and to Study the Development of a Hybrid Neuro Fuzzy Control Approach”, International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 4, 2013, pp. 62 - 66, ISSN Print : 0976-6545, ISSN Online: 0976-6553. 113
  • 11. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME BIOGRAPHY Ch.Ravi Kumar was born in India in 1981; He received the B.Tech degree in Electrical and Electronics Engineering from A.S.R.College of Engineering and Technology, Tanuku in 2003 and M.Tech degree from JNTU Anantapur, A.P.-India in 2005. Currently he is pursuing Ph.D in Electrical Engineering and working as Asst.Professor in University college of Engineering and Technology, Acharya Nagarjuna University, Andhra Pradesh India. His areas of Interest are Power system operation and control, Application of Intelligent control techniques to Power systems. P.V.Ramana Rao was born in India in 1946; He received the B.Tech degree in Electrical and Electronics Engineering from IIT Madras, India in 1967 and M.Tech degree from IIT Kharagpur, India in 1969. He received Ph.D from R.E.C Warangal in 1980. Total teaching experience 41 years at NIT Warangal out of which 12 years as Professor of Electrical Department. Currently Professor of Electrical Department in University college of Engineering and Technology, Acharya Nagarjuna University, Andhra Pradesh, India. His fields of interests are Power system operation and control, Power System Stability, HVDC and FACTS, PowerSystem Protection, Application of DSP techniques and Applicat ion of Intelligent control techniques to Power systems. 114

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