Adaptive Neuro-Fuzzy Scheduled Load Frequency Controller for Multi Source Multi Area System Interconnected via Asynchronous Tie-line

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1 Adaptive Neuro-Fuzzy Scheduled Load Frequency Controller for Multi Source Multi Area Sytem Interconnected via Aynchronou ie-line..m. VIJAYA CHANDAALA, S. BALAMUUGAN Electrical and Electronic Department Amrita Vihwa Vidyapeetham Amrita School of Engineering, Ettimadai, Coimbatore, amil Nadu INDIA. Abtract: - he article focue toward the development of an optimal econdary controller which could adapt with the varying ytem condition. For the analyi, a two area multi ource ytem coniting of thermal, hydro and nuclear ytem in one area i interconnected with another area compriing of thermal and hydro ytem. On ubjection to unit tep load change in demand, the impact on frequency and tie-line power flow variation in multi ource multi area i oberved under MALAB / Simulink environment. he fine tuning of frequency and tie-line power flow variation i achieved with the help of econdary controller. Optimal econdary Proportional Integral (PI controller i choen baed on Zeigler Nichol (ZN, Genetic Algorithm (GA, Fuzzy Gain Scheduling (FGS and Adaptive Neuro-Fuzzy Inference Sytem (ANFIS tuning technique. he performance of the controller i evaluated baed on performance indice. ey-word: - Load Frequency Control (LFC, Zeigler Nichol (ZN Method, Genetic Algorithm (GA echnique, Adaptive Neuro-Fuzzy Gain Scheduling (ANFIS echnique, Integral Squared Error (ISE Introduction In today era of fat developing load when compared to utilitie have paved way toward reliable power upply. In interconnected ytem due to vat variation in load, the frequency variation and tie-line power flow to be in limit i a challenging tak. Load Frequency Control (LFC play a major key role in keeping the ytem operation and control intact [-3]. Many of the reearcher have contributed their work toward LFC in interconnected ytem [4-]. When ubjected to load change, a High Voltage Direct Current ranmiion (HVDC Line in anti-parallel with the High Voltage Alternating Current (HVAC line [-5] connecting two area would reult reduced area frequencie and tie-line power flow. In thi work, for the analyi a a multi ource thermal, hydro and nuclear of one area i interconnected via HVAC and HVDC anti parallel tie-line with another area of hydro and thermal ytem. When load change in an interconnected ytem compriing of multi ource, the frequency and tie-line power flow varie, a uch, primary control loop governed by the peed governor et the ytem back to it nominal value. Due to the limitation in droop change of the peed governor which relie between -3%, the ytem ettle nearer to the nominal value with a teady tate error. For ettling the ytem back to it nominal tate, econdary controller come into action. A econdary controller, PI controller ha achieved it par in control application for it flexible operation [6-7]. ZN method a a conventional benchmark i ued for tuning the gain of the PI controller [8]. o achieve optimal gain value of PI controller GA, PSO (Particle Swarm Optimization, SA (Simulated Annealing technique are ued by many reearcher under load frequency control iue. In thi work, GA technique [9-] i ued for tuning the gain value of the PI controller which provide global optimal value. But, ZN and GA tuned PI controller gain value being fixed, it fail to vary the gain value with the ytem varying condition. herefore, FGS baed on it efficient deciion making [3-5] will help to vary the gain value of the controller with repect to varying ytem condition. For effective gain cheduling to adapt with the ytem on wide varying condition ANFIS [6] i ued in thi work. ANFIS conider the Fuzzy Inference Sytem (FIS to take the deciion and further optimize to adapt with the help of neural network to judge wide range of variation in the ytem. he controller performance i evaluated baed on performance ISSN: Volume, 7

2 indice [7-8]. For practicality, a a cae tudy, multi ource multi area ytem i included with reheat team turbine, boiler dynamic with the limit bound to turbine operation namely Generation ate Contraint (GC i conidered to make the ytem highly robut in nature on a cale of wide varying characteritic of the ytem [9-3]. Intead of identical area, the ytem i operated under nonidentical area approach. ANFIS baed PI controller i ued for adapting the controller gain with repect to wide change on ytem condition to bring back the ytem area frequencie and tie-line power flow to it normal value. he dicuion of the work tart with the ection which tate about the modeling of multi ource multi area ytem with aynchronou tie-line, Section 3 deal with the tuning method adopted for econdary PI controller, Section 4 dicue with imulation reult and cae tudy and finally ection 5 conclude the work. he turbine output w.r.t peed governor output i repreented a hown in Equation ( P ( ( P ( ( ( g H Modeling of Multi Source Multi Area Sytem Interconnected via Aynchronou ie-line For analyi, two imilar area interconnected via HVAC in anti-parallel with HVDC tie-line operating at a capacity of MW with nominal load of MW operating at 5Hz i taken into conideration. An area comprie of non-reheat team turbine, hydro turbine and nuclear turbine driving a generator connected to a load i interconnected to another area of hydro turbine and non-reheat team turbine driving the generator and load. he one-line diagram of multi ource multi area interconnected via aynchronou tie-line i hown in Fig. In thermal power plant [-5], the turbine act a the prime mover for the generator. he turbine get the mechanical energy through team from boiler. he team input to the turbine i controlled uing the peed governor. When there i an increae in demand, the mimatch between the generation and demand i indirectly ened by the governor in term of change in frequency. he output of the peed governor i modeled a hown in Equation (. P ( P ( f ( ( g ref Depending on the ened frequency value, the peed governor with the help of hydraulic amplifier alter the inlet valve poition of team, which in turn control the frequency. Fig. One-line diagram of multi ource multi area interconnected via aynchronou tie-line he turbine power output drive the generator which provide the electrical power to the power ytem. he tranfer function of the power ytem compriing of generator with load diturbance i given in Equation (3 P P PD f (3 P he characteritic of hydro turbine are different from thermal turbine. In hydro plant [4-5], water i the ource for producing mechanical energy to drive the turbine. hough it take le time to tart up, greater time lag prevail in the repone during normal operation due to large water inertia. he mimatch between generation and demand i ened indirectly in term of change in frequency by the governor [5] which control the water input to the turbine. he tranfer function repreenting the performance of the peed governor i highlighted in Equation (4 PHg ( ( Pref ( f( (4 he output of the hydro turbine w.r.t. peed governor output i repreented in Equation(5 PH ( ( ( W (5 PHg ( ( (.5 W he turbine power output drive the generator which provide the electrical power to the power ytem ISSN: Volume, 7

3 whoe tranfer function i ame a Equation (3. In nuclear power plant, the reactor core would contain 75 ton of uranium to produce an output of MW. In the reactor core the U-35 iotope fiion or plit, producing a lot of heat in a continuou proce a a chain reaction. he proce depend on the preence of a moderator uch a water or graphite, and i fully controlled. he moderator low down the neutron produced by fiion of the uranium nuclei o that they go on to produce more fiion. he tranfer function of nuclear peed governor i repreented in Equation (6 P N( ( Pref 3( f( ( N (6 3 he main deign i the preurized water reactor (PW which ha water in it primary cooling/heat tranfer circuit, and generate team to drive the generator to meet the demand. he turbine model of tandem-compound type which ha one High Preure (HP and two Lower Preure (LP ection with High Preure (HP re-heater ection [6]. High preure team urpae through the reheat the HP exhaut. Before entering the LP turbine the HP turbine exhaut Moiture-Separator-eheater (MS. hi help to reduce the moiture of the team and eroion rate while paing through LP ection of the turbine. he tranfer function of LP and HP turbine repectively i repreented in Equation (7 and Equation (8 repectively. another area of Hydro and hermal Power Plant i hown in Fig. B A B P ref P ref P ref 3 P ref 4 P ref 5 Pg P H H P P Hg P HV P H W.5W P g N 3 HN N N ( ( P N P ACtie P D P Hg PHV P H A W.5W H N H H ( ( H 3 N P H P P D P DCtie P P P P A P P Fig. ranfer function model of multi ource multi area interconnected via aynchronou tie-line f f P N ( N ( H P ( ( ( ( ( N N H H 3 N P N ( HN P ( ( N N (7 (8 he generator i driven by power output of the turbine which generate electrical power to the power ytem i repreented in Equation (3. he tie-line interconnecting the two area via HVAC and HVDC tie-line i modelled and written in tranfer function a hown in Equation (9 and Equation ( repectively. PACtie ( ( f( f ( (9 DC PDCtie ( ( f( f ( ( DC he tie-line power flow include the variation on HVAC and a well a on HVDC tie-line.he mathematical modeling with the governing equation explained above for the Governor-urbine ytem of hermal, Hydro and Nuclear plant of an area interconnected via aynchronou tie-line to an 3 uning Method of Secondary Proportional Integral (PI Controller he limitation of the primary peed governor action to -3% would not help the ytem to retain to it normal value on ubjection to unit tep load change in area. herefore, econdary controller being flexible i required. In thi work, PI controller [6-7] i ued for fine tuning of frequency and tie-line power flow variation to bring the ytem to it normal value. he tranfer function of PI controller i hown in Equation (. i Pref ( ( p ( Ptie ( ( he econdary controller i connected to the multi ource multi area ytem a hown in Fig. he variou tuning method for optimal chooing of controller gain are dicued below. 3. Zeigler Nichol (ZN uning Method A a conventional bench mark, ZN method [8] i ued for chooing the better value of controller ISSN: Volume, 7

4 gain. In thi method, proportional gain p i increaed maintaining integral gain i to zero. he gain at the ytem ocillate i termed to be ultimate gain u and the difference between the peak for one cycle i termed to be ultimate time u i noted down. he PI controller gain computed uing Zeigler Nichol method are and.34 repectively in area and area. 3. Genetic Algorithm (GA uning Method ZN tuned PI controller gain value are not the optimal one. herefore, optimization technique are adopted for obtaining optimal value of PI controller for the multi ource multi area ytem interconnected via aynchronou tie-line. In thi work, GA technique [9-] i ued for tuning the PI controller. he algorithm adopted for tuning PI controller gain i a follow: Step: Etimate range for the proportional and integral gain Step: andomly initialize the value for the controller gain Step3: On ubjection to unit tep load change in area ; evaluate the objective function to minimize the integral quared error of area frequencie and tie-line power flow change on ubjection to unit tep load change in area Step4: I the convergence criteria of error minimization i met? If No, apply genetic operator by creating new range for the controller gain through reproduction, croover and mutation and go to tep3 and repeat the proce. Step5: If Ye, top and PI controller gain value obtained are the optimal value to be conidered in the plant. he optimal gain value of the PI controller uing GA technique are tabulated in able. able. GA tuned PI controller gain value Multi ource multi area hydro thermal ytem interconnected with aynchronou tie-line Area Area p. i p.795 i p i p.73 i 5.75 p.9 i Fuzzy Gain Scheduling (FGS uning Method he optimal value choen by GA method alo provide fixed gain value which don t change w.r.t varying ytem condition. herefore, FGS method [4-5] being much flexible and it deciion making baed on varying ytem condition i conidered in thi work. he functional diagram of FGS in making the deciion i hown in Fig 3. f ( P ( tie du dt i p P ( ref f ( Fig.3 Schematic diagram of Fuzzy Gain Scheduling of PI controller In thi work, FGS ha two input namely change in error ACE and rate of change in error (du/dt.ace or ACE. he FGS baed on the knowledge change the reference power etting of the governor P ( ref. he two input and output i characterized with even linguitic variable namely Negative Big (NB, Negative Medium (NM, Negative Small (NS, Zero (Z, Poitive Small (PS, Poitive Medium (PM, Poitive Big (PB. he NB and PB are of trapezoidal memberhip function to accommodate the error lying in the infinity region. All other memberhip function i of triangular type. he input ACE range i between [-.7.7] and the other input (du/dt.ace or ACE range i between [ ], output range P ( ref i between [-.5.5]. he rule are framed in uch a way that it uit for both p and i which i furnihed in able. able. Fuzzy rule for cheduling p and i ACE LN MN SN Z SP MP LP ACE 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 he rule are of IF AND HEN type. For example; If ACE i LN and ACE i LN then output i LP. 3.4 Gain Scheduling uing Adaptive Neuro- Fuzzy Inference Sytem (ANFIS he fuzzy ytem help in to change the controller gain under varying ytem condition and neural network proven it relevance in adapting toward ISSN: Volume, 7

5 the ytem condition. Depending on wide variation of the ytem condition FGS fail to provide control on the ytem which could be overruled by ANFIS [6]. he efficient deciion making by the FIS correlated with the adaptive neural network would help to handle the ytem being highly robut. he functional diagram of ANFIS i hown in Fig4. f ( du dt P ( tie i p P ( ref f ( Fig.4 Schematic diagram of ANFIS baed Gain Scheduling of PI controller Baed on the prior knowledge, FGS ha cheduled the PI controller gain for the multi ource multi area ytem ubjected to load variation by reduced optimization earch pace. With thi Neural network adapt by applying back propagation to the tructured network to automate FIS parametric controller tuning. he ANFIS algorithm i explained a follow: Step : Load the training data obtained by the Fuzzy Inference Sytem (FIS compriing of two input and one output. Step: Set the input parameter of memberhip function and generate the ANFIS model. Step3: If the error minimized or converged? If Ye, get optimized FIS after training which would be adaptable gain value of the PI controller. Step4: If No, go to tep and proceed until converged. he controller performance i evaluated baed on Integral Squared Error (ISE [7-8] which i repreented in Equation. ISE ( f Ptie f. dt ( 4 eult and Dicuion A dicued in Section, Fig model of multi ource multi area ytem with aynchronou tie-line i developed uing MALAB/Simulink [3] without econdary controller. he ytem with aynchronou tie-line i ubjected to unit tep load diturbance in area and it frequency and tie-line power flow variation i oberved and compared without HVDC line. he comparion repone with and without HVDC tie-line i hown in Fig 5. del f(hz del Ptie(p.u. M W del f(h z primary control without HVDC tie-line primary control with Aynchronou tie-line time(ec Fig.5 Comparion repone of multi ource multi area interconnected via Aynchronou ie-line and without HVDC ie-line From the above figure, it clearly how that with two-area interconnected via aynchronou tie-line and without HVDC tie-line on ubjection to unit tep load diturbance in area ha lead to area frequency variation and tie-line power flow with reduced teady tate error. herefore, preferably with a aynchronou tie-line interconnecting area would help in limiting the variation in area frequencie and tie-line power flow on load variation. A dicued in ection 3, due to the limitation in peed governor characteritic, PI controller would help the ytem to retain to it nominal value by fine tuning the variation. herefore, a dicued in ection 3. with gain value a highlighted and ection 3. with GA method, the gain value a highlighted in able i included in multi ource multi area ytem interconnected via aynchronou tie-line i compared without the econdary controller i.e. primary loop and i hown in Fig 6. del f(h z 5 x x -3 del Ptie(p.u.M W del f(hz primary loop with Aynchronou tie-line ZN tuned PI controller with Aynchronou tie-line GA tuned PI controller with Aynchronou tie-line x time(ec Fig.6 Comparion repone of primary loop, ZN and GA tuned PI controller of multi ource multi area interconnected via aynchronou tie-line ISSN: Volume, 7

6 From the above comparion repone, with econdary controller GA technique ha given optimal PI controller gain value when compared to ZN tuned and without econdary controller. But, uing GA technique PI controller gain value are fixed and doen t change w.r.t ytem condition. herefore, FGS i ued for cheduling the gain value a dicued in ection 3.3 and adapting the ytem w.r.t to increae in robutne of the ytem ANFIS a dicued in ection 3.4 i incorporated in the ytem. he comparion repone of ANFIS with FGS and GA technique tuned PI controller i hown in Fig 7. del f(h z x x -3 del Ptie(p.u.M W del f(h z GA tuned PI controller with aynchronou tie-line FGS baed PI controller with aynchronou tie-line ANFIS bae PI controller with aynchronou tie-line x time (ec Fig.7 Comparion repone of GA, FGS and ANFIS tuned PI controller of multi ource multi area interconnected with aynchronou tie-line From the above figure, it i quite evident that ANFIS baed PI controller ha reduced ubequently the variation in frequency and tie-line power flow with reduced peak overhoot and fater ettling time to it nominal value when ubjected to % load change in area. he performance indice evaluated baed on ISE i hown in able which help in judging the controller performance. and prove ANFIS a uitable PI controller which help in fine tuning the variation. able. Performance and percentage improvement of variou controller in multi ource multi area hydro thermal ytem baed on ISE Secondary Controller ISE ZN tuned PI.77 GA tuned PI 9.75e-5 FGS 7.5e-5 ANFIS.e-5 % Improvement GA (PI w.r.t ZN (PI 9.79 FGS (PI w.r.t GA (PI 5.37 ANFIS (PI w.r.t FGS (PI 69.3 From the able, baed on ISE and improvement in percentage comparion among the controller, ANFIS tuned PI controller prove to be the uitable econdary controller which help in fine tuning and achieving the ytem fater in action to it nominal value. he performance of the ANFIS baed PI controller i teted under robut ytem by including the boiler dynamic, reheat team turbine [-3,3] in thermal power plant in both the area, on ubjection to increae and decreae of % load change during ec, 8ec and 6 ec in area and area operating at decreae and increae of % load change during 4ec and ec repectively. Intead of identical power ytem, area operating at a capacity of 8 MW and area operating at a capacity of 5MW. he ytem i imulated uing MALAB/Simulink, and the repone of robut multi ource multi area ytem interconnected via aynchronou tie-line with ANFIS baed PI controller i hown in Fig 8. del f(h z del Ptie(p.u.M W del f(h z x ANFIS tuned PI controller with aynchronou tie-line under non-lineartie time(ec Fig.8 epone of ANFIS tuned PI controller of multi ource multi area with aynchronou tie-line under non-linearitie From the above figure it clearly viualize that ANFIS baed PI controller i the bet optimal adaptive controller for a robut multi ource multi area ytem interconnected via aynchronou tieline which improved the area frequencie and tieline power flow variation. 5 Concluion In thi tudy, multi ource multi area ytem interconnected via aynchronou tie-line i conidered. he ytem wa ubjected to unit tep load variation in area alone. he limitation on peed governor characteritic would help the ytem to retain to it nominal value but with area frequencie and tie-line power flow error. herefore, PI controller wa tuned uing ZN, GA, FGS and ISSN: Volume, 7

7 ANFIS technique were ued. he performance of the controller wa evaluated baed on ISE and judged that ANFIS baed PI controller i the mot optimal and bet econdary controller which would adapt with the wide varying ytem condition and helped in handling robut ytem. Appendix hermal Power Plant: =Speed regulation of thermal governor = Hz/p.u. MW; H = urbo governor time contant =.8 ec; =Non-reheat turbine time contant =.3 ec; P = P = Power ytem gain contant of area and area = ; P = P = Power ytem time contant of area and area = ec; P D = P D = Change in load demand power in area and area =.p.u; = = Frequency bia contant of areaand area =.45 p.u.mw/hz; Pref Pref 5= Change in the reference power etting of the thermal peed governor in area and area repectively in p.u.; f f= Change in frequency of area and area repectively in Hz; P g = Change in the governor output power in p.u.; P H = Change in hydraulic amplifier power output in p.u.; P = Change in the non-reheat team turbine output power in p.u.; Hydro Power Plant: Pref Pref 4= Change in the reference power etting of the hydro peed governor in area and area repectively in p.u.; =Speed regulation of hydro governor = Hz/p.u. MW; P Hg = Change in the hydro governor ytem in p.u.; P HV = Change in the mechanical-hydraulic hydro ytem in p.u.; P H = Change in the hydro turbine in p.u.; = Hydro governor gain = ; = Hydro governor time contant = 48.7 ec;, = Hydro power plant time contant = 5. ec,.53 ec; = Water time contant =. ec; W Nuclear Power Plant: P N = Change in the nuclear peed governor output power in p.u.; P N = Change in the nuclear turbine output power in p.u.; HN=HP nuclear turbine gain =.-.; N =HP nuclear turbine time contant =.5 ec; N =LP nuclear two tage turbine gain =.3; H = LP nuclear two tage turbine time contant = 7ec; H H3= LP nuclear two tage turbine time contant = 6ec and ec repectively; N = LP nuclear two tage turbine time contant = 9ec; HVDC tie-line: P DCtie = Change in the HVDC tie-line power flow in p.u.; DC = HVDC tie-line gain =.; DC = HVDC tie-line time contant =.ec; HVAC tie-line: P ACtie = Change in the HVAC tie-line power flow in p.u.; Ptie Ptie = Change in the tie-line power flow from area to area and area to area repectively = total power flow in HVAC and HVDC tie-line in p.u.; A =Synchronizing power co-efficient = -; = Synchronizing coefficient =% of area capacity =.Co δ =.77; Secondary PI Controller: p = Proportional gain; i = Integral gain; = Laplace operator; ACE= Area Control Error; ACE = ate of change of Area Control Error; eference: [] C. Foha, O.I. Elgerd, he megawattfrequency control problem: a new approach via optimal control theory, IEEE ranaction on Power Apparatu and Sytem, Vol.PAS- 89, No.4, 97; pp [] O.I. Elgerd, Electric Energy Sytem heory an Introduction, ata McGraw Hill Edition, 983. [3] P. undur, Power Sytem Stability and Control, McGraw Hill Inc., Newyork, 994. [4] IEEE PES Committee eport, Dynamic Model for Steam and Hydro urbine in Power Sytem Studie, IEEE ranaction on ISSN: Volume, 7

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9 Optimal obut Load-Frequency Control for Power Sytem, IE Generation, ranmiion and Ditribution, Vol. 6,, pp [3] ay ain-sou, Load-frequency control of interconnected power ytem with governor backlah nonlinearitie, Electrical Power and Energy Sytem, Vol. 33,, pp [3] MALAB Uer Manual, Mathwork Inc. U.S.A.,. ISSN: Volume, 7