Steam-Hydraulic Turbines Load Frequency Controller Based on Fuzzy Logic Control

Transcription

1 esearch Journal of Applied Sciences, Engineering and echnology 4(5): , ISSN: Maxwell Scientific Organization, Submitted: February, Accepted: March 6, Published: August, Steam-Hydraulic urbines Load Frequency Controller Based on Logic Control,4 Ali M. Yousef, Jabril A. hamaj and 3,4 Ahmad Said Oshaba Electrical Engineering Department Faculty of Engineering, Assiut University, Egypt, 756, Mechanical Engineering Department Faculty of Engineering Jazan University, SA 3 Power Electronics and Energy Conversion Department, Electronics esearch Institute, Egypt 4 Electrical Engineering Department Faculty of Engineering Jazan University, SA Abstract: his study investigates an application of the fuzzy logic technique for designing the load-frequency system to damp the frequency and tie line power oscillations due to different load disturbances under the governor deadzones and GC non-linearity. ler are designed and compared with the proposed fuzzy logic ler. o validate the effectiveness of the proposed ler, two-area load frequency power system is simulated over a wide range of operating conditions and system parameter changes. Further, comparative studies between the conventional PID and proposed efficient fuzzy logic load frequency are included on the simulation results. Programs Matlab software are developed for simulation. he digital results prove the power of the present fuzzy load-frequency ler over the conventional. PID ler in terms of fast response with less overshoot and small settling time. eywords: logic ler, PID ler, steam-hydraulic turbines INODUCION Power systems have complex and multi-variable structures. Also they consist of many different s blocks. Most of them are non linear. Power systems are divided into areas connected by tie lines. From the experiments on the power systems, it can be seen that each area need to its system frequency and tie line power flow to be led. Frequency is accomplished by two different actions in interconnected two area power system: primary speed and supplementary or secondary speed actions as in Ertugrul and Ilhan (5), El-Sherbiny et al. () and El-Sherbiny et al. (). he secondary loop takes over the fine adjustment of frequecy by resetting the frequency error to zero through integral action. he relationship between the speed and load can be adjusted by changing a load reference set point input. Load-Frequency Control (LFC) is important in electric power system design and operation. Moreover, to ensure the quality of the power supply, it is necessary to design a LFC system, which deals with the of loading of the generator depending on the frequency. he conventional proportional plus integral ler is probably the most commonly used. It is well known that power systems are non-linear and complex, where the parameters are a function of the operating point and the loading in a power system is never constant. Further, the two area power system composed of steam turbines led by integral only, is sufficient for all load disturbances and it does not work well. Also, the non-linear effect due to governor deadzones and Generation ate Constraint (GC) complicates the system design. Further, if the two area power system contains hydro and steam turbines, the design of LFC systems is important. here are different strategies that have been applied, depending on linear or non-linear methods as in Saadat (98), othari et al. () and found in Anju and Sharma (). With the advent of fuzzy logic theory, various applications for it have been established as in Pan and Liaw (989), Saravuth et al. (6) and as in Yusuf and Ahmed (4). It is considered that the fuzzy logic system works well with non-linear systems. he present study utilizes the fuzzy logic technique to design the LFC system for two area power systems. he proposed technique is added to the integral LFC system as a supplementary signal to improve the power system stability and response characteristics. Also, the non-linear effect due to governor dead zones or GC are Corresponding Author: Ali M. Yousef, Electrical Engineering Department Faculty of Eng., Assiut University, Egypt, 756, Electrical Engineering Department Faculty of Engineering Jazan University, SA 375

2 es. J. Appl. Sci. Eng. echnol., 4(5): , considered as in Yousef (). he types of turbines employed in the two area power system, steam-hydro, are studied with the proposed fuzzy logic technique. MAEIALS AND MEHODS he system investigated comprises an interconnection of two areas load frequency. he model is steam-hydraulic turbines. he linearized mathematical models of the first order system are represented by state variables equations as follows: For steam area as: =!/ p )f +k p / p )P g!k p / p ) f!k p/ p)p d P / P / f / E / U r r x p X E / XE Pr[ / / ] / f E [ B f a * P ] x E tie he tie line power as: P [ f f ] tie he overall system can be modeled as a multivariable system in the form of: =!/ r )P g +)P r [/ r! r / t ] P g + r/ t)x E x Ax() t Bu() t Ld() t () P r = / t )X E!/ t )P r ) X E =! / g )f! / g )X E + / g )E x +/ g )U p Ex EBf Ptie For hydraulic area as: f / pf kp / p Pg kp/ p * a* Ptie kp/ ppd P g Pg[ 5 /. w ( 5 /. /. 5 )] XE[/. 5w 5 /. ] /. 5 f where A is system matrix, B and L are the input and disturbance distribution matrices. x(t),u(t) and d(t) are system state, signal and load changes disturbance vectors, respectively: x(t) = [)f )P g )P rl )X E )E X )f )P g )P r )X E )E X ) ] () u(t) = [u u ] (3) d(t) = [)P dl )P d ] (4) ACE i = )ptie, i + b i )f i (5) y(t) = Cx(t) (6) )p tie (t) = )p tie, (t) =! )p tie, (t) (7) p g EB A p p r r ( ) r t t r t t g g p.5 B E (.5 w p p.5 ).5 ( ) ( ).5 w.5 p p E p a p E a 376

3 es. J. Appl. Sci. Eng. echnol., 4(5): , B g, = urbine time constant (s) of area No. and area No. p = Plant model time constant (s) p = Plant gain = Speed regulation due to governor action (HZ/p.u. MW) = he integral gain E where, L p p )f, )f = he frequency deviation (HZ) of area No. and area No. )P g, )P g = he change in generator output (p.u. MW) of area No. and area No. )X E,)X E = he change in governor value position (p.u. MW) of area No. and area No. )P d,)p d = he change in turbine values (p.u. MW) of area No. and area No. )E X,)E X = he change in integral of area No. and area No. )P d,)p d = Load disturbance (p.u. MW) of area No. and area No. g, g = Governor time constant (s) of area No. and area No. p p Effect of governor dead zones non-linearity: Figure shows the power system block diagram with governor dead zones. he described of governor dead zones D(v) by the following function: m(v-d), if v $d D[v] =, {if-d # v # d {m(v+d) if v #d Effect of GC non-linearity: he block diagram of the plant model with Generation ate Constraint (GC) given by P g.7 p.u. MW/s is shown in Fig. logic : logic has an advantage over other methods due to the fact that it does not sensitive to plant parameter variations. he fuzzy logic approach consists of three stages, namely fuzzification, fuzzy rules engine and defuzzification. o design the fuzzy logic load frequency, the input signals is the frequency deviation at sampling time and its change. While, its output signal is the change of signal )U(k). When the value of Fig. : wo-area (Steam-Hydraulic urbines) load frequency with governor deadzone and GC nonlinearity 377

4 es. J. Appl. Sci. Eng. echnol., 4(5): , LN MN SN Z SP MP LP : Large negative membership function : Medium negative : Small negative : Zero : Small positive : Medium positive : Large positive Fig. : hree-stage of fuzzy logic ler: u, membership degree Fig. 3: logic power system stabilizer able : logic rules d)f )f 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 PID power system stabilizer: PID lers are dominant and popular and, have been widely used because one can obtain the desired system responses and it can a wide class of systems. his may lead to the thought that the PID lers give solutions to all requirements, but unfortunately, this is not always true as in Datta et al. (). In this work, the PID optimal tuning method used is found in ristiansson and Lennartson (). In this method, the parameters of PID ler satisfying the constraints correspond to a given domain in a plane. he optimal ler lies on the curve. he design plot enables identification of the PID ler for desired robust conditions and in particular, gives the PID ler for lowest sensitivity. By applying this method, trade-off among high frequency sensor noise, low frequency sensitivity, gain and phase margin constraints are also directly available. he transfer function of a PID ler is given by: (s) = p (+/ ls + D s) (8) where p, p / l and p D represent the proportional, integral and derivative gains of the ler respectively. Define n and I as the ler's I D D natural frequency and the damping coefficient, respectively. hen the PID transfer function can be written as: s () P n ns s s n (9) Fig. 4: Membership functions of the fuzzy regulator the signal at sampling time [k-] (U(k-)) is added to the output signal of fuzzy logic ler, the required signal U(k) is obtained. Figure shows the threestage of fuzzy logic ler. While the fuzzy logic system is described in Fig. 3. he fuzzy rules are illustrated in able. he membership function shapes of error and derivative error and the gains are chosen to be identical with triangular function for fuzzy logic. However, this horizontal axis range is taken different values because of optimizing ler. he membership function sets of FLC for two areas are shown in Fig. 4 where, he optimal PID ler parameters values were: p =, I =.6 and D = ESULS AND DISCUSSION o validate the effectiveness of the proposed fuzzy lers, the power system under study is simulated and subjected to different parameters changes. he power system frequency deviations are obtained. Further a various types of turbines (steam and hydro) are simulated. Also a comparison between the power system responses using the conventional integral and PID system and the proposed fuzzy logic ler is studied as follows and the system parameters are: Nominal parameters of the hydro-thermal system investigated: 378

5 es. J. Appl. Sci. Eng. echnol., 4(5): , f = 6 HZ = =.4 HZ/per unit MW g =.8 s r =. s t =.3s = 5 s D = D =.833 Mw/HZ = 48.7 s =.53 s, g =.8s t = t =.3 s r = r = /3 Pd =.5 p.u. MW B = B =.45 Pd =. r = r = s, =.77 s he integral gain i = pu f F ime Fig. 5: Frequency deviation response of area- due to.5 p.u. load disturbance in area- of the two-area power system ime Fig. 6: Frequency deviation response of area- due to.5 p.u. load disturbance in area- of the two-area power system Figure 5 shows the frequency deviation response of area- due to. 5 p.u. load disturbance in area- of the two-area power system with and without integral and proposed fuzzy logic. Figure 6 shows the frequency deviation response of area- due to.5 p.u. load disturbance in area- of the two-area power system power deviation response due to.5 p.u. load disturbance in area- of the two-area power system with and without Deviation ime Fig. 7: ie-line power deviation response due to.5 p.u. load disturbance in area- of the two-area power system with and without integral and proposed fuzzy logic f Deviation ime Deviation of F g = g =. Fig. 8: ie-line power deviation response due to.5 p.u. load disturbance in area- of the two-area power system with and without integral and proposed fuzzy logic at increased % in g ime g = g =. Fig. 9: Frequency deviation response of area- due to.5 p.u. load disturbance in area- of the two-area power system of model- at increased % in g. Figure 7 shows the tie-line power deviation response due to.5 p.u. load disturbance in area- of the two-area power system with and without integral and proposed fuzzy logic. Figure 8 depicts the tie-line integral and proposed fuzzy logic at increased 379

6 es. J. Appl. Sci. Eng. echnol., 4(5): , f f Deviation of F ime Deviation of F g = g =. Fig. : Frequency deviation response of area- due to.5 p.u. load disturbance in area- of the two-area power logic at increased % in g f ime Deviation of F g =.5, r = Fig. : Frequency deviation response of area- due to.5 p.u. load disturbance in area- of the two-area power logic at change in t, r ime g =.5, r = Fig. : Frequency deviation response of area- due to.5 p.u. load disturbance in area- of the two-area power logic at change in t, r % in g. Figure 9 depicts the frequency deviation response of area- due to.5 p.u. load disturbance in area- of the two-area power system with and without integral and proposed fuzzy logic at increased % in g. Moreover, Fig. depicts the frequency F Deviation ime dev. esponse in pu. t =.5, r = Fig. 3: ie-line power deviation response due to.5 p.u. load disturbance in area- of the two-area power system at change in t, r p =, ki =, kd = -4 W/o- -6 PID ime (sec) Fig. 4: ie-line power deviation response due to.5 p.u. load disturbance in area- of the two-area power system with and without PID and proposed fuzzy logic F dev. response in pu. p =., ki =., kd = W/o- PID ime (sec) Fig. 5: Frequency deviation response of area- due to.5 p.u. load disturbance in area- of the two-area power system with and without PID and proposed fuzzy logic deviation response of area- due to.5 p.u. load disturbance in area- of the two-area power system with and without integral and proposed fuzzy logic at increased % in g. Figure displays the frequency deviation response of area- due to.5 p.u. load 38

7 es. J. Appl. Sci. Eng. echnol., 4(5): , F.5 F dev. esponse in pu...5 p =., ki =., kd = W/o- -.5 PID ime (sec) Fig. 6: Frequency deviation response of area- due to.5 p.u. load disturbance in area- of the two-area power system with and without PID and proposed fuzzy logic able : ime settling calculated in each - PID- (Sec) (Sec) (Sec) F F ie-line power disturbance in area- of the two-area power system with and without integral and proposed fuzzy logic at change in turbine time constant (t) and r. Figure displays the frequency deviation response of area- due to.5 p.u. load disturbance in area- of the two-area power logic at change in turbine time constant t and r. Figure 3 shows the tie-line power deviation response of area- due to.5 p.u. load disturbance in area- of the two-area power system with and without integral and proposed fuzzy logic at change in turbine time constant t and r. Figure 4 shows the tie-line power deviation response due to.5 p.u. load disturbance in area- of the two-area power system with and without PID and proposed fuzzy logic. Figure 5 depicts the frequency deviation response of area- due to.5 p.u. load disturbance in area- of the two-area power system with and without PID and proposed fuzzy logic. Figure 6 shows the frequency deviation response of area- due to.5 p.u. load disturbance in area- of the two-area power system with and without PID and proposed fuzzy logic. Also, able displays the settling time calculation at different lers. CONCLUSION AND ECOMMENDAIONS he presents study design a load-frequency system using fuzzy logic ler for enhancing power system dynamic performances after applying several disturbances upon it. he proposed ler is robust and gives good transient as well as steady-state performance. o validate the effectiveness of the proposed ler a comparison among the conventional integral and PID ler and the proposed ler is obtained. he effect of governor non-linear and GC is included in the simulation. he proposed ler proves that it is robust to variations in dead zones non-linearity. he digital simulation results Proved and show the effectiveness of the power of the proposed FLC over the conventional integral and PID ler through a wide range of load disturbances. he superiority of the proposed fuzzy ler is embedded in the sense of fast response with less overshoot and/or undershoot and less settling time. EFEENCES Anju, G. and P.. Sharma,. Design and simulation of fuzzy logic ler for dstatcom in power system. Int. J. Eng. Sci. echnol., 3(): Datta, A., H. Ming-zu and S.P. Bhttacharyy,. Structure and Synthesis of PID Controllers. Advanced in Industrial Control, Springer-Verlag London Limited. El-Sherbiny M.., G. El-Saady and A.M. Yousef,.A-two-layered fuzzy logic ler for power system automatic generation. Proceeding of 4 th Jordan International Electrical Conference (4 th JIEEEC-), 6-8 April, pp: 97-. El-Sherbiny M.., A.M. Yousef and G. El-Saady,. Efficient fuzzy logic load-frequency. Energ. Conver. Manage. J., 43(4): Ertugrul, C. and I. ocaarslan, 5. A fuzzy gain scheduling PI ler application for an interconnected electrical power system. EPS J., 73: othari M.L., J. Nanda and B.L. kaul,. Automatic generation of Hydro-thermal system. J. EL, 6. ristiansson, B. and B. Lennartson,. obust and optimal tuning of PID lers. IEE Proc. Contr. heory Appl., 49: 7-5. Pan, C.. and C.M. Liaw, 989. An adaptive ler for power system load-frequency. IEEE. Power Syst., 4(). Saadat, H., 98. Power System Analysis. ata Mc-Graw Hill Publishing Company Limited. New Delhi, India. Saravuth, P., I. Ngamroo and W. ong prawechnon, 6. Design of optimal fuzzy logic based PI ler using multiple search algorithm for load frequency. Int. J. Contr. Automation Syst., 4(): Yousef, A.M.,. Model prdictive approach based load frequency ler. WSEAS rans. Syst. Contr., 7(6). Yusuf, O. and S. Ahmed, 4. Dynamic fuzzy network based load frequency ler design in electrical power system. G.U. J. Sci., ISSN: