Performance Evaluation of an ANFIS Based Power System Stabilizer Applied in Multi-Machine Power Systems

Transcription

1 Performance Evaluaton of an ANFIS Based Power System Stablzer Appled n Mult-Machne Power Systems A. A GHARAVEISI 1,2 A.DARABI 3 M. MONADI 4 A. KHAJEH-ZADEH 5 M. RASHIDI-NEJAD 1,2,5 1. Shahd Bahonar Unversty of Kerman, Kerman, Iran 2. Internatonal Research for Scence & Hgh Technology, Mahan, Iran 3. Shahrood Unversty of Technology, Shahrood,Iran 4. Electrc Dstrbuton Company Ahvaz, Iran 5. Desgn and Engneerng Department of North Electrc Dstrbuton Company Kerman, Iran 22 Bahman Boulvard, Elec. Eng. Dept. Kerman IRAN Abstract: - Power system stablzers (PSSs) are utlzed n order to make power systems stable after a large or small dsturbance. Therefore PSSs must be capable of provdng approprate stablzaton sgnal over a broad range of operatonal condtons and dsturbances. Due to the fact that PSSs are wdely used n power ndustry, any approprate mprovements n controllng methods of PSSs are mportant as well. Recently, Fuzzy logc s used as a robust control desgn method and FPSS s proved to have a good answer for generatng approprate control sgnal. However fuzzy logc controllers (FLCs) are based on emprcal contract rules, however there s no systematc method known for desgnng of a FLC. A self learnng adaptve network based fuzzy nference system (ANFIS) type power system stablzer (ANFPSS) s presented n ths paper. In ths approach, the fuzzy rules and membershp functons of the fuzzy PSS s tuned automatcally by the learnng algorthm. The proposed technque s llustrated on a 9-bus, 3-machne power system. Results show that ANFPSS has a satsfactory performance under varous and dfferent condtons of power systems and related faults. Key-Words: - Mult-Machne Power System, Adaptve Networks Based Fuzzy Inference System (ANFIS) 1 Introducton Power systems are usually large non-lnear systems, whch are often subjected to low frequency electromechancal oscllatons. Power System Stablzers (PSSs) are often used as an effectve and economc means for dampng the generators electromechancal oscllatons and enhance the overall stablty of power systems. Power system stablzers have been appled for several decades n utltes and they can extend power transfer stablty lmts by addng modulaton sgnal through exctaton control system. They provde good dampng; thereby contrbute n stablty enhancement of the power systems. Desgnng PSS s an mportant ssue from the vewpont of power system stablty. Conventonal PSSs (referred to as CPSS) use transfer functons desgned for lnear models representng the generators at a certan operatng pont [1,2]. However, as they work around a partcular operatng pont of the system for whch these transfer functons are obtaned, they are not able to provde satsfactory results over wder ranges of operatng condtons. In other words, accordng to the fact that the gans of the mentoned controller are determned only for a partcular operatng condton, they may not yet be vald for a wde range around or for other new condtons [3]. Ths problem s overcome by usng Fuzzy logc based technque for desgnng of PSSs. Fuzzy logc systems (FLCs) allows us to desgn a controller usng lngustc rules wthout knowng the exact mathematcal model of the plant[4,5]. The applcaton of Fuzzy Power System Stablzers (FPSSs) has been motvated because of some reasons such as mproved robustness over that obtaned usng conventonal lnear control algorthm, smplfed control desgn for dffcult-tobe-modeled systems and smplfed mplementaton[3,6]. FLCs are very useful n the case a good mathematcal model for the plant s not avalable; however, experenced human operators are avalable for provdng qualtatve rules to control the system. In some paper to mprove the performance of FPSSs a hybrd FPSS s presented. In [7] a FLC s used wth two CPSS, also Hybrd PSSs usng fuzzy logc and/or neural networks or Genetc Algorthms (GA) have been reported n

2 some lterature [8,9]. However, there s no systematc procedure for desgnng FLCs. The most common approach s to defne Membershp Functons (MFs) and IF-THEN rules subjectvely by studyng an operatng system or an exstng controller. So, an adaptve network based approach presented n [10] to choose the parameters of fuzzy system usng a tranng process. In ths technque an adaptve network s used to fnd the best parameter of fuzzy system. In ths paper, an adaptve neuro fuzzy nference system (ANFIS) based PSS s developed, whch uses the speed, and ts devaton as the nputs. The ANFPSS uses a zero order Sugeno-type fuzzy logc controller whose membershp functons and consequences are tuned by back-propagaton method. Fuzzy rules and MFs of the controller can be tuned automatcally by learnng algorthm. The proposed technque s llustrated on a 9-bus, 3- machne power system. MATLAB/SIMULINK and fuzzy logc toolbox have been used for system smulaton. The results demonstrate that the proposed self-learnng ANFPSS provdes a good dampng over a wde range of operaton condtons and mproves the stablty margn of the system as well. 2. Adaptve Network Based Fuzz Inference System In ths artcle, an Adaptve-Network based fuzzy structure s employed to desgn a fuzzy logc power system stablzer (FPSS). The FPSS consdered, have two nputs that are components of the speed and ts devaton. In ths method, nput parameters change to lngustc varable and sutable Membershp Functons (MFs) should be chosen for them. Moreover, the rule base contans the fuzzy fthen rules of Takag and Sugeno type, n whch the output of each rule s a lnear combnaton of nput varables added by a constant term [10]. I. Structure of ANFIS In ths part, the structure of ANFIS for tunng parameter of a fuzzy nference system wth two nputs and one output s explaned. The structure of ANFIS whch s shown n fg (1) conssts of fve layers [10]: Fg.1 The structure of ANFIS wth two nputs Layer1: Each node n ths layer s an adaptve node wth a node functon shown n the followng equaton (1), and performs a membershp functon (MF): 1 O = μ (x) (1) A 1, where O s membershp functon of μ A (x) and A s the lngustc label assocated wth ths node. In ths layer parameter of each MF are adjusted. In ths work, MFs of these nodes are bell-shaped functon. Layer 2: In ths layer the output of each node represents the frng strength of each rule. Hence, the nodes perform the fuzzy AND operaton, that ts output s multple of nputs as shown n equaton (2): W = μa (x)* μb (y), = 1, 2, 3,... (2) Layer 3: As shown n equaton (3), the nodes of ths layer determne the normalzed frng strength of each rule: ω = n ω = 1 (3) ω Layer4: Each node n ths layer s an adaptve node and n ths layer parameters of output are adjusted. Ths output usually s a lnear functon of nput. Layer 5: Ths layer has only one node and calculates the overall output as a summaton of all nput sgnals: n f = 1 f = (4) Hence, an adaptve Network has been constructed, whch s functonally equvalent to a fuzzy logc fault locator. Ths structure can update the MFs and

3 rule base parameters. 3. ANFIS Based PSS The ANFPSS s ntally traned off-lne. For ths, typcally dsturbances under varous operaton condtons were appled to smulated Mult-Machne Power System. The man effectve characterstc and condton of the power system by whch the PSS operatons could be dstorted are assumed to be one of the fault postons, level of system load, and clearng tme of protecton devces. In other words a useful PSS should be able to generate proper, so an effectve PSS must be able to generate sutable dampng sgnals under varous poston of fault and dfferent levels of load. Furthermore, f the protecton system has not a proper operaton and faled to clear fault n the shortest tme, the backup protecton would be actvated after a determned tme delay, so the PSS should be enable to send proper dampng sgnal under ths condtons. For ths, further to normal operaton of power system, we smulated other crtcal condtons and used the obtaned data n tranng process of ANFPSS, therefore the proposed PSS s able to damp oscllatons under normal condton of power system and also condtons descrbed bellow: Where A and B are fuzzy sets and K s a constant value. Fg.2 Proposed ANFPSS for Mult- Machne Power System The MFs of two nputs of controller represent the trangle membershp functons for each lngustc set and each nput. These MFs, after tranng process, are shown n fg (3). 4. Smulaton Result The 3-Machne 9-Bus power system, shown n fg (4), s used for testng the proposed technque. All smulatons were performed usng the MATLAB/SIMULINK [11]. A. Varous poston of fault on power system: the ANFPSS can damp oscllaton due to fault on dfferent bus or along each lne of power system. B. Fault clearng tme: the ANFPSS has good performance even f the man protecton system faled and fault clearng tme ncreases untl backup protecton operates wth a determned tme delay. C. Level of load: the proposed ANFPSS has a nce operaton when power system faces wth a maxmum %20 overload. Intal parameters of MFs and IF-THEN rules are selected n a random manner and after tranng process the obtaned MFs and rules are appled to power system as an ANFPSS. 3.1 ANFPSS Scheme Fgure (2). Shows the scheme of proposed ANFPSS and ts applcaton n a Mult-Machne power system. A zero order Sugeno fuzzy controller wth 18 rules s used for ANFPSS. The controller rules are of the form: IF ω s A and P s B then u=k Fg.3 MFs of two nputs of controller n proposed technque Performance evaluaton of the ANFPSS was done by applyng a large dsturbance caused by a threephase fault to ground on dfferent postons and n varous condton of load level and tme delay of protecton system.

4 of fault clearng. The smulaton results and ANFPSS s responses to varous dsturbances have demonstrated that the proposed ANFPSS can effectvely enhance the dampng of low frequency oscllatons. Fg.4 3-Machne 9-Bus Power System Some dfferent smulatons are done and llustrated bellow: 4.1 Case 1: A 3-phase to ground (3PG) fault was appled to bus 6. In ths case the load level s n normal condton and the man protecton system can operate n the mnmum tme. The results are shown n Fg (5) to Fg (8). 4.2 Case 2: A 3PG fault was appled to bus numbered 8 and the power system has % 20 overloads. The results are shown n Fg (9) to Fg (12). 4.3 Case 3: A 3PG fault was appled to bus numbered 2 and the man protecton system faled to clear the fault, so the backup protecton operates wth a normal tme delay. Therefore n ths case fault clearng tme s longer than the two prevous cases. The results are shown n Fg (13) to Fg (16). 5. Conclusons Ths paper descrbed an ANFIS based power system used for dampng oscllaton n Mult-Machne power systems. In ths method FPSS, whch uses Takag-Sugeno type FLC, s traned n a systematc approach to set on proper parameters. The parameters of proposed PSS (such as MFs and Fuzzy IF-THEN rules) are tuned off-lne usng an adaptve network; consequently the proper dampng sgnal can be generated n wde range condton of power system n on-lne operaton. The proposed PSS s tested on a 3-Machne 9-bus system under dfferent condtons such as change on load level, change on poston of fault and the tme 6. References [1] N. Hossen Zadeh, A. Kalam, PERFORMANCE OF A SELF-TUNED FUZZY-LOGIC POWER SYSTEM STABILISER IN A MULTIMACHINE SYSTEM School of Engneerng and Scence Monash Unversty, Malaysa. [2] A.Sree, V.Ghadr, R.M.Abdelrahman, AN ADAPTIVE NEURO FUZZY POWER SYSTEM STABILIZER FOR DAMPINGINTER-AREA SCILLATIONS IN POWER SYSTEMS, [3] P.Hoang, K.Tomsovc, DESIGN AND ANALYSIS OF AN ADAPTIVE FUZZY POWER SYSTEM STABIZER, IEEE Transacton on Energy Conversons, Vol. 11, No. 2, June [4] N. Hossen Zadeh, A. Kalam, A Drect Adaptve Fuzzy Power System Stablzer, Proceedng of the 4 th Conference on Advances n power System Control, operaton and Management, APSCOM-97, Hong Kong, Nov [5] M.chetty, N.Trajkosk, A Dscrete Mode Fuzzy Power System Stablzer, Monash Unversty, Australa. [6] M.A.Hasan, O.P.Malk, Implementaton and Laboratory Test Results for a Fuzzy Logc Based Self-Tuned Power System Stablzer, IEEE/PES Summer Meetng, seattle, July [7] J.Lu, M.H.Nehrr, D.A.Perre, A Fuzzy Logcbased Adaptve Power System Stablzer for Mult-Machne Systems, IEEE, [8] M.Hashem, H.Eghbal, A neuro-fuzzy power system stablzer wth self-organzng map for Mult-machne systems, Proceedng of IEEE Power Engneerng Socety Transmsson and Dstrbuton Conference, Vol. 2, 2002, pp [9] D.Mennt, A.Burgo, A.Pnnarell, V.Prncple, N.Scordno,N.Sorrenton, Dampng Oscllaton Improvement BY Fuzzy Power System Stablzers tuned by Genetc Algorthm, 14 th PSCC, Sevlla, [10] JShng and R.Jang, ANFIS: Adaptve-network based fuzzy nference system, IEEE Trans., Vol. 23, MayIJune 1993, pp [11] [12] A.Harrr, O.P.Malk, Implementaton and real

5 tme studes wth a Self-Learnng Adaptve- Network Based Fuzzy Logc PSS, 14 th PSCC, Sevlla, Fg.10 Varaton of δ 2 (case 2) Fg.5 Varaton of P g2 (case 1) Fg.11 Varaton of ω 2 (case 2) Fg.6 Varaton of δ 2 (case 1) Fg.12 Varaton of P g3 (case 2) Fg.7 Varaton of V 6 (case 1) Fg.13 Varaton of δ 2 (case 3) Fg.8 Varaton of ω 2 (case 1) Fg.14 Varaton of ω 2 (case 3) Fg.9 Varaton of P g2 (case 2)

6 Fg.15 Varaton of δ 3 (case 3) Fg.16 Varaton of ω 3 (case 3)