The Effect of Fuzzy Logic Controller on Power System Stability; a Comparison between Fuzzy Logic Gain Scheduling PID and Conventional PID Controller

Transcription

1 The Effect of Fuzzy Logic Controller on Power System Stability; a Comparison between Fuzzy Logic Gain Scheduling PID and Conventional PID Controller M. Ahmadzadeh, and S. Mohammadzadeh Abstract---This paper presents the application of a fuzzy Logic controlled to improve stability of power system. The system in this study is a two-area electrical interconnected power system. Also, in this paper, the effect of a conventional controller PID, and a fuzzy logic gain scheduling PID (FGPID) controller on power system stability are compared. The performance of these controllers in First, settling times and overshoots of the frequency deviation are compared. All the models are simulated by Matlab Simulink software. The simulation results show that the FL controller developed in this study performs better than the PID controller with respect to the settling time and overshoot, and absolute error integral of the frequency deviation. Keywords---Load frequency control, Power system stability; Fuzzy logic controller L I. INTRODUCTION OW frequency oscillations are a common problem in large power systems. Increasing power demand leads to more complexity and less reliability of interconnected power systems. Insufficient transmission capability of the interconnection leads to bottle necks in the system and reduce the system stability. In an electric power generation, disturbance caused by load fluctuation Will result in changes of the desired operating frequency [1-2]. The requirements for control of frequency in an interconnected power system are implemented by the Automatic Load Frequency Control (ALFC). The ALFC provides automatic variation of generation set points on the speed governors to maintain system frequency within the specified limit. Intelligent controller is a Fuzzy Logic Controller for a Frequency Stabilization. Control algorithms based on fuzzy logic have been implemented in many processes. The application of Such control techniques has been motivated by the following reasons: 1) improved robustness over the Conventional linear control algorithms; 2) simplified control design for difficult system models; 3) simplified implementation [1], [4], [7].Several studies have been done in the past about the load frequency control in interconnected power systems. M.Ahmadzadeh, Department of Electrical Engineering, Mahshahr branch, Islamic Azad University, Mahshahr, Iran (corresponding author to provide phone: , mostafa.ahmadzadeh@yahoo.com) S.Mohammadzadeh, Department of Electrical Engineering, Mahshahr branch, Islamic Azad University, mahshahr, Iran. In the literature, a number of control strategies have been suggested based on the conventional linear control theory. To some investigators, variable structure system control [4, 5] maintains stability of system frequency. However, this method needs some information for system states, which are very difficult to obtain completely. Also, some described a minimum variance strategy for load frequency control of interconnected power systems [6]. According to [7], conventional PID control schemes will not reach a high performance. Since the dynamics of a power system even for a reduced mathematical model is usually non-linear, time variant and governed by strong cross-couplings of the input variables the controllers have to be designed with special care [8]. Thus, a gain scheduling controller can be used for nonlinear systems [3]. In this method, control parameters can be changed very quickly because parameter estimation is not required. It is easier to realize as compared with automatic tuning or adaptation of controller parameters. However, the transient response can be unstable because of abruptness in system parameters. Besides, it is impossible to obtain accurate linear time invariant models at variable operating points [3]. Some fuzzy gain scheduling of PI controllers have been proposed to solve such problems in power systems [3] and [8] that developed different fuzzy rules for the proportional and integral gains separately. Fuzzy logic control presents a Good tool to deal with complicated, non-linear and indefinite and time-variant systems [7]. In this paper, the rules for the gains are chosen to be identical in order to improve the system performance. The comparison of the proposed FGPID and the conventional PID suggests that the overshoots and settling time with the proposed FGPID controller are better than the PID controller. II. SYSTEM MODEL In an interconnected network, a disturbance in one line or changing in loads, leads to effects on the neighboring systems to change in frequency causing severe problem in the entire power system network. LFC is very important in power system operation and control for supplying sufficient and adequate electric power with good quality. Power system have not been designed for wide area power trading with daily varying load patterns where power flows do not follow the initial planning criteria of the existing network configuration. 83

2 International Conference on Computer, Systems and Electronics Engineering (ICSCEE'2014) April 15-16, 2014 Johannesburg (South Africa) Interconnected power systems naturally consist of complex and multi-variable structures with many different control blocks. They are usually non-linear, time-variant and/or nonminimum phase systems [8]. Power systems are divided In to control areas connected by tie lines. In each control area, the generators are supposed to constitute a coherent group. It means that the movements of their rotors are closely related [4]. Experiments on the power systems show that tie-line power flow and frequency of the area are affected by the load changes at operating point. Therefore, it can be considered that each area needs its system frequency and tie-line power flow to be controlled [3]. Additionally, it is desired That transient frequency oscillation without a large increase in the magnitude and speed control must be reduced. Also, the number of LFC signals sent to power systems without compromising other objectives must be reduced [8]. Since the small load changes are affected by the active power, and the frequency, while reactive power is only affected by the magnitude of the bus voltage, a separate control loop can be used for frequency control. Generally, the load frequency control is accomplished by two different control Actions in interconnected two-area power systems: (a) the primary speed control and (b) supplementary or secondary speed control actions. The primary speed control performs the initial vulgar readjustment of the frequency. By its actions, the various generators in the control area track a load variation and share it in proportion to their capacities. The speed of the response is only limited by the natural time lags of the turbine and the system itself. Depending upon the turbine type the primary loop typically responds within 2 20 s. The supplementary speed control takes over the fine adjustment of the frequency by resetting the frequency error to zero through an integral and differential actions. The relationship between the speed and load can be adjusted by changing a load reference Set point input. In practice, the adjustment of the load reference set point is accomplished by operating the speed changer motor. The output of each unit at a given system frequency can be varied only by changing its load reference, which in effect moves the speed-droop characteristic up and down. This control is considerably slower and goes into action only when the primary speed control has done its job. Response time may be of the order of one minute. The speed-governing system is used to adjust the frequency. Governors adjust the turbine valve/gate to bring the frequency back to the nominal or scheduled value. Governor work satisfactorily when a generator is supplying an isolated load or when only one generator in a multi generator system is required to respond to the load changes. For power and load sharing among generators connected to the system, speed regulation or droop characteristics must be provided. The speed-droop or regulation characteristic may be obtained by adding a steady-state feedback loop around the integrator. Ш. Controller Design An uncontrolled two-area interconnected power system is shown in Fig. 1, where f is the system frequency (Hz), Ri regulation constant (Hz/unit), Tg speed governor time constant (s), Tt turbine time constant (s) and Tp is power system time constant (s). Fig.1. A two-area interconnected power system (DP1, 1, 2: load demand increments)[1] The objective of the proposed controller design is to improve the power system performance under the normal and different load disturbance. To maintain the system frequency in an interconnected power system, the controller is designed using a fuzzy logic gain scheduling PID to improve the power system stability. The overall system can be modeled as a multi-variable system in the form of: Where A, B and E are system state matrix, distribution matrix and disturbance matrix of appropriate dimensions respectively. Similarly x, u and d are the state, control and disturbance vector and denotes deviation from the nominal values and u1 and u2 are the control outputs in Fig. 1. The system output, which depends on area control error (ACE) shown in Fig. 2, is given as: [ ] T T 84

3 The prime objective is to minimize the Area Control Error (ACE) which stabilizes the system frequency for a sudden load disturbance. The objective function of the load frequency controller [8, 7, and 1] is given by, Where i Number of areas, ΔF- Change in frequency, ΔP tie Change in tie-line power and β biasing factor.[1,2] Fig.2.Two-area power system with controller [7] IV. FUZZY LOGIC IN POWER SYSTEMS. Fuzzy set theory and fuzzy logic establish the rules of a non-linear mapping [6]. The use of fuzzy sets provides a basis for a systematic way for the application of uncertain and indefinite models [5]. Fuzzy control is based on a logical system called fuzzy logic. It is much closer in spirit to human thinking and natural language than classical logical systems [7]. Nowadays, fuzzy logic is used in almost all sectors of industry and science. One of them is the load frequency control [1]. The main goal of the load frequency control in the interconnected power systems is to protect the balance between production and consumption. Because of complexity and multi-variable conditions of the power system, conventional control methods may not give satisfactory solutions. On the other hand, Robustness and reliability make fuzzy controllers useful in solving wide range of control problems [1]. The fuzzy controller for the single input output type of systems is shown in Fig. 3 [4]. Power systems are shown that the conventional controllers have large overshoots and long settling times [4,1]. Also, optimizing time for control parameters, especially PID controllers, is very long and the parameters are not calculating exactly. In addition, it has been known that conventional controllers generally do not work well for non-linear, higher order and time-delayed linear, and particularly complex and vague systems that have no precise mathematical models [1]. According to many researchers, there are some reasons for the Present popularity of fuzzy logic control. First, fuzzy logic can easily be applied to most industrial applications in industry. Fig.3. the simple fuzzy controller. [1] V. FUZZY GAIN SCHEDULED PID CONTROLLER By taking ACE as the system output, the control vectors for the conventional PID controller can be given in the following form: ( ) ( ) ( ) ( ) Fig.4. Membership functions for FGPI Controller of (a) ACE, (b) ACE, (c) KP, Ki. KD Second, it can deal with intrinsic uncertainties by changing controller parameters. Finally, it is appropriate for rapid applications. Therefore, fuzzy logic has been applied to the industrial systems as a controller. Human experts prepare linguistic descriptions as fuzzy rules, which are obtained based on step response experiments of the process, error 85

4 signal, and its time derivative [8]. Determining the controller parameters with these rules, the fuzzy gain scheduling proportional, integral and differential controller (FGPID) is formed. Fuzzy logic shows experience and preference through membership functions, which have different shapes ACE(k) ACE (k) TABLE I FUZZY LOGIC RULES FOR FGPI CONTROLLERS depending on the experience of system experts [2]. Same inference mechanism is realized by seven rules for the two FGPID controllers. The appropriate rules used in the study are given in Table 1. LN MN SN Z SP MP LP LN LP LP LP MP MP SP Z MN LP MP MP MP SP Z SN SN LP MP SP SP Z SN MN Z MP MP SP Z SN MN MN SP MP SP Z SN SN MN LN MP SP Z SN MN MN MN LN LP Z SN MN MN LN LN LN Membership functions shapes of the error and derivative error and the gains are chosen to be identical with triangular function for both fuzzy logic controllers. However, their horizontal axis ranges are taken different values because of optimizing these controllers. The membership function sets of FGPI for ACE, ACE, Kp and Ki are shown in Fig. 4,.Defuzzification has also been performed by the center of gravity method in all studies. shows, system response with the proposed controller has a quite shorter settling time and lower magnitude in overshoots. VI. THE MODEL SIMULATION Simulations were performed using the conventional PID and the proposed FGPID controllers applied to a two-area interconnected electrical power system. The same system parameters [3], given in Table 2 were used in all controllers for a comparison. Two performance criteria were selected in the simulation. The frequency deviation graphs were first plotted with Matlab Simulink software. (Fig.5 and 6). Here, settling times and overshoots of the frequency deviation of the controllers were compared against each other.( given in Table 3) TABLE II TWO-AREA POWER SYSTEM PARAMETERS T g=0.08 B 1=0.425 R 1=2.4 B 2=0.425 R 2=2.4 T 12=0.086 T P=20 K P=120 Tt=0.3 a 12 =-1 TABLE III SYSTEM PERFORMANCES FOR ALL CONTROLLERS ON SETTLING TIMES AND OVERSHOOTS FOR FREQUENCY DEVIATION OF AREA 1 (FOR 5% BAND OF THE STEP CHANGE) Maximum CONTROLLERS SETTLING TIME (S) overshoots(hz) FGPID conventional PID The comparison results are provided in Table 3. In the analysis of the simulation results, the frequency Comparison of the proposed controller with conventional PID controller Fig5. Deviation of frequency of area 1 with conventional PID controller (DPd, i = 0.01 p.u.). Fig 6. Deviation of frequency of area 1 with GPID controller (DPd, i = 0.01 p.u.). Simulations were performed for different instantaneous loads changes and Success was achieved in all cases. As shown in Table 3, the settling time of the proposed FGPID controller is substantially shorter conventional PID controller and the proposed controller has the minimum integral absolute error too. Therefore, the proposed controller is better than other controller. VII. CONCLUSIONS In this paper, a FGPID (fuzzy gain scheduling of PID) controller, In order to automatically load frequency controller, For an interconnected power system was Examined.in fuzzy 86

5 simulation The number of rules for the inference mechanisms was taken seven, so that the controller performances were improved by increasing the rule numbers to 49. The results show that the proposed algorithm is effective in controlling and improves system performance. The proposed controller is much easier and requires no information about the system parameters. Based on experimental results, It's performance is better than the other controller in settling time and Integral absolute error and in over shoot is close to the optimum. As a result, the proposed fuzzy gain scheduling PID controller is recommended to generate good quality and reliable electric energy. ACKNOWLEDGEMENTS This work was supported by Islamic Azad University, mahshahr Branch, Iran REFERENCES [1] Ertu grul C am, Ilhan Kocaarslan,, A fuzzy gain scheduling PI controller application for an interconnected electrical power system Electric Power Systems Research 73 (2005) [2] M. Klein, L. X. Le, G. J. Rogers, S. Farrokhpay, and N. J. Balu, H8 damping controller design in large power systems, IEEE Transactions on Power Systems, Vol. 10 Issue: 1, Feb. 2005, pp: [3] M. A. Abido and Y. L. Abdel-Magid, Power System Stability Enhancement via FACTS-Based stabilizers, Final Report of a Project Funded by FKUPM, May 2007 [4] M. A. Abido and Y. L. Abdel-Magid, Analysis and Design of Power System Stabilizers and FACTS Based Stabilizers Using PSO Algorithms, Proceedings of Power System Computation Conference PSCC-2002, Session 14 Paper 3, Spain, June 24-28, 2006, [5] Y.Y. Hsu and C.Y. Hsu, Design of a Proportional-Integral Power System Stabilizer, IEEE Trans. PWRS, Vol. 1, No. 2, pp , [6] T. Lie, G. Shrestha, and A. Ghosh, "Design and Application Of Fuzzy Logic Control Scheme For Transient Stability Enhancement In Power System", Electric Power System Research. 2005, pp, [7] H. F. Wang and F. J. Swift, "Capability of the Static VAr Compensator in Damping Power System Oscillations," IEE Proc. Genet. Transm. Distrib., Vol. 143, No. 4, 2006, pp [8] M. A. Abido and Y. L. Abdel-Magid, Analysis and Design of Power System Stabilizers Using pso Algorithms, Proceedings of Power System Computation Conference PSCC-2002, Session 14 Paper 3, Spain, June 24-28, 2004 Mostafa ahmadzadeh was born in Shiraz, Iran, in He received the B.S. degree from yazd university,yazd, iran in 2006 and M.Sc. degree from chamran university, ahvaz, iran in power engineering in He is currently with department of Electrical Engineering, Mahshahr branch, Islamic azad university, mahshahr, Iran as lecturer. his research interests include stability of power systems, design of FACTS devices and power quality. Saeed Mohammadzadeh was born in 1984 in Lahijan, Iran. He received B.Sc. degree in power electrical engineering from Guilan University, Iran, in 2006, and M,,Sc. degree in power electrical engineering from Shahid Chamran University, Iran, in He is currently with department of Electrical Engineering, Mahshahr branch, Islamic azad university, mahshahr, Iran as lecturer. His current research interest includes power quality Detection and Control. 87