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350automatically and rapidly, while the power system configura-tion or protective relaying scheme is ch anged. The task c an beaccomplished merely by the experienced dispatchers in thecontrol center. For different power system configurations,high versatility and po rtability will thus be achieved.To demonstrate the effectiveness of the proposed HFDSstructure, the system has been tested on a typical secondarytransmission system of Tainan area in the T aipower System.Acvonyms UsedGMDH Group Method of Data HandlingSCADA Supervisory Control And Data AcquisitionANN Artificial Neura l NetworksHFDS Hierarch ical Fault Diagno sis SystemT&D Transmission And DistributionOOP Object-Oriented ProgrammingMMI Man-M achine InterfaceAPM Alarm Processing ModuleGUI Graph ical User InterfaceSMU State Monitoring UnitDPU Data Preparation Unitos Operating SequenceAIC Akaike's Information Criterionsc Sequence ComparatorBOLIM Bit-Ope ration Logical Inference Mechanism

11.FAULT IAGNOSISN A T&D SYSTEMThe duty of the protective relaying scheme is to performthe function s of m onitoring the system status and tripping theappropriate breaker(s) if a fault occurs in the T&D system.When fault occurs in the protective zone of a relay, a controlsignal is sent to trip related circuit breakers to isolate thefaulted section from the rest of the system. Here, a section of a

T&D system is meant by a power apparatus, such as a trans-mission or a distribution line, a bus bar, or a transformer,which can be separated from the rest of the system by break-er@). In order to minimize the service interruption and limitdamage to equipment, fast and correct restoration action isneeded. The system restoration first identifies the fault situa-tions. Based on the fault situations identified, proper measurescan thus be taken to restore the system.When fault occurs, the alarm signals received in an auto-mated contro l center would be the binary signals of the on/offstatus of the protective relays and breakers as well as analogsignals of bus voltages and line currents. According to thesealarm signals, the opera tor should fulfill the fault diagnosis tojudge as soon as possible where the faulted section is andwhat the fault type is.If all the protective devices operate correctly and datacommunicate without error, a fault situation would result in aparticular sym ptom pattern (states of relays and breakers). Bycorrectly recognizing the symptom pattern, the possible faultsituations can be straigh tforward identified, a task w hich is aprocess of pattern rec ognition.

I pLegend:GMDH -- Group Method of Data HandlingBOLIM -- Bit-Operation LogicalInference M echanismFig. 1 Structure of the Hierarchical Fault Diagnosis System

However, in practical application,exist in the symptom patterns due to cfalse operation of relay or breaker, the problem would be com-plicated. In these cases, a fault situation may lead to severalpossible symptom pattems, and vice versa . C onsequently evenexperienced operator may not promptly an d precisely identifjwhat the real situation and the original fault even t are.111.PROPOSEDIERARCHICALAULT IAGNOSISYSTEM

A . OO P Scheme and Modular Design of the SystemFollowing the OOP scheme [14,15], the physical objects inthe T&D system (lines, bus bars, and transformers, etc.) areregarded as the objects that belong to diverse classes in thesoftware. An object in the class has two parts: one is a set ofvariables describing its current state; the other(in OOP terminology called method) that accefies the data for the var iables. The same type of objects has thesame variables and the sa me m ethods. Besides, each object isan instance of a class, and the instance is a n object for whichmemory space has been allocated.For example, the instances concerned in t"Line" class have the instance variables of thprimary and local protective devices (relays and circu it break-ers), the remote protective devices, and the correspondingcoordinated protective delay times to eac h of these devices. Bymeans of the OOP scheme, the diagnosis system is designedand implemented using the C++ language with modules andobjects closely integrated in the system via the input/outputinterfaces.The structure of the proposed object-oriented HierarchicalFault Diagnosis System (HFDS) is shown in Fig. 1, whichconsists of Man-Machine Interface (MMI), Alarm ProcessingModule (APM), Phase I and Ph ase I1 Diagnos is Modules, andGraphical User Interface (GUI). Detailed description of theindividual modules of HFDS is addres sed as follows.

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...Table 1Form of Raw Data Base Generated by the Man-Machine InterfaceSample I Fault Section I Primary Relays and Breaker 1 Local Back-up Device s Remote Back-up Device

1 1 Tainan - H.C. M. Line 1 PR1, CB660-1, CB660-2 I R21-1,R67-1 I R51-I, R51-2, R51-3, CB1550, CB1560, CB1670... ... ..... I I I.. ... ..... ...10 I H.C. (S/S) BU S I R21-1, R21-2, CB660-1, CB670-1 I R67-1, R67-2 I R51-1, R51-2, R51-3, C B1550, CB1560, CB1670... ... ... ... ...I I ILegend S/S: Secondary Su bstation; P R Pilot Relay; R: R elay; CB: Circuit Breaker; Tr: Transformer

S2: Tainan - H.C. S. LineS3: H.C. - K.Y. Line. SIO: H.C. Buss20:K Y. .Fig. 2 Example Data Structure of Data Base IB.Man-Machine Interface

Th e M M I works a s a vital com munication medium betweenthe experienced operators and the proposed fault diagnosissystem. The MMI creates the off-line training data by inter-acting with the exp erienced operators. These training da ta areused to set up the APM and the phase I & I1 diagnosis mod-ules. The MMI requires the ex perienced operators to input thedata for the instanc e variables of each object class in a n inter-active manner. Th e form of the raw d ata base generated by theM M I is shown in Table 1. For brevity, the coordination delaytimes between primary and local back-up, or between localback-up and remote back-up protections are not show n in thisTable. Based on the raw data base, the MMI automaticallycreates two data bases, Data Base I and Data Base I1 for con-struction of the APM and the two-phase diagnosis modules.Exam ple structures of these two data bases are given in Figs.2and 3, respectively.As shown in Fig. 2, each protective device (a relay or abreaker) is re lated to the basic section s (a line, a bus bar, or atransformer) in its protective zone, no matter its role ofprimary, local or remote protection of the basic section. Byvirtue of Data Base I, the APM can accordingly identifywhether or not there is overlap of protective zone (Le., basicsections) between the coming alarm of some protective deviceand the previous alarm just received, a fact which is used todetermine the waiting time of the State Monitoring Unit(SMU) in the APM (to be described later in this paper).

Displayed in Fig. 3 is the example data structure in thecreated Data Base I1 corresponding to one section in a T& Dsystem. As noted in this Figure, the s tructure is separated intothree data sets. Set I is the potential symptom vectors for di-verse fault situations, which comprise the o d o f fstates of therelays and the breakers. To comply with the purpose of thephase I diagnosis module in identlfying the fault section, for-mat of reduced relay and breaker signals in data set I is usedin the diagnosis process. For example, alarms from eachphase of the distance relays, directional overcurrent relays,differential relays, and overcurrent relays are reduced as asingle alarm sign al.Set I1 is the associated states of protective devices and thefailure device if ex isting. In addition, based on the protectivecoordination scheme, each protective device is a ssigned an or-dinal operating sequence (OS), which means the operatingpriority of the protective devices if a fault occurs. For in-stance, the devices with the OS = 1can n ot operate later thanthose with OS = 2, but actua l operating timing f or the deviceswith the same OS number will be ignore d to consider the littletime difference in practical tripping o perations.Set 111 is the co rrespond ing possible fault types of the sec-tion. In data sets I1 and 111, two pointers of add ress, pointer Iand pointer 11, are given to spec@ the address in the com-ment base. The com ment base serves to provide linguistic ex-planations of the op erating status of protective devices and thedetailed fault situation to the operators.In Data Base 11, each section in the T&D system has adata structure shown in Fig. 3.The Data Base I1 is then usedto set up the phase I (with data se t I) and phase I1 (withdatasets I1 and 111) diagnosis modules.C.Alarm Processing Module

Receiving the time-stamped alarm signals from SCADAinterface in on-line environment, the APM is intended to ini-tiate the diagnosis process, and compile the alarm data intoSet I: Symptom Pattern Data SetII:Operating S tatus of Protective Devices Set III:Fault Type CodeFailure Device CorrectlyOperatingDevices & Their Operating SequenceCB660-lCB660-2 PRI R51-3 Pomter I CB660-lCB660-2 PomterII a g b-g abcg[ 1 1 1 I . . . I1I...IH 100000I 0 I 0 I ...I I 120000I 1 I 0 I...I 0 1

Note: 1 . OS stands for Operating Sequen ce of protective devices2. " 1" denotes "operate"or "opened";"0" enotes "not operate" or "closed"Fig. 3 Example Data Structure of Data Base II for One Basic Section

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352the format required by in the two-phase diagnosis modules.The APM consists of State Monitoring Unit (MU) and DataPrepar ation Unit (DPU) to perform these two tasks.State Monitoring Unit

The MU fulfills its initiation function based on the in-formation accessed from Data Base I and the received alarmsignals. Receiving the first alarm signal, the SMU assigns aproper waiting tim e (a ccording to the longest protective coor-dination time among all the basic sections it protects) to awatch dog and successively gathers the alarm signals. Whenthe waiting time elapses (meaning that the fault has been insteady state), the watch dog sends an initiation command tothe phase I diag nosis module to start the diagnosis.During the waiting time, if the SCADA interface receivesanother signal which belongs to completely different protec-tive zone (no overlap between these two protective zones), thewatch dog is reset and reassigned a new waiting time if thelongest delay-coo rdination time of this protective zone islonger than that of the previous one. These two time-stampedalarm signals are, therefore, regarded as coming from inde-penden t fault events in the f ault diagnosis process.Data Preparation Unit

The DPU compiles the time-stamped alarms into two setsof data for the two-p hase diagnosis module. For phase I diag -nosis module, the relay and breaker signals are reduced intothe format as in data set I of Data Base 11. For phase I1 diag-nosis module, according to the time-stamped alarm signals re-ceived, two-piece information for each protective device isprepared: the former indicates the operating state (on or off)of the protective device, the latter is its actual operating se-quence in this fault event. Time resolution of alarm signalsfrom the SCADA system is in 10ms.

Besides, a buffer is designated to temporarily sample andhold the alarm signals of another fault event which appearduring the w orking period of the phase I diagno sis module forthe previous event.D. Phase I Diagnosis Module

Initiated by the MU, the ph ase I diagn osis module beginswith estim ating the possible fault sections using the d ata pre-pared by the DPU . To circum vent the problems of the conven-tional m ulti-layer ANN in ex cessive training efforts required,the Group Method of Data Handling (GMDH) [16-181 isemployed to achiev e the same purpose on fau lt diagnosis. TheGMDH models the input-output relationship of a multi-inputhingle-output system using a multilayered perceptron-like network structure as show n in Fig. 4. Each element in thenetwork stands for a second-order (or lower-order) polynomialfunction of the related inputs. For example, the polynomialfunction of an elem ent in one of the layer is expressed as:Y=PG (X)= a, + a , X , + a z X , + a 3 X: + a , X , X, + a,X,2 (1 )where PG denotes a second- (or lower-) order polynomialgenerator of the input X I and X,, where a,, a,, . ., as2 0 .

A:first hreshold self-selection; B. second threshold self-selection,C: selection fiom all solutions; D: threshold optimizationFig. 4 Structureof Group Method of Data HandlingAs shown in Fig. 4, one threshold self-selelayer in the network is employed to filter out the PG elementswhich are harmful (least useful) to the estimation of the cor-rect final output value. Only the values of the elemeY,, Y,, ...,Y, in Fig. 4) which exceed the self-selectioold of that layer are allowed to pass to the next layer . In otherwords, only the best network combinations of the input vari-ables are pe transfer forwa rd to the succeeding lay-

ers where layer structure is constructed . Thefeature of GMDH is capable of approximating any order ofpolynomials as given below [16-IS].n n nF= a , +g aX l+ if.a,aSr;X,+C C C a,a,akX,XJk + ... ( 2 )r=l 1 = 1l = l 1 = 1 7 = 1 k lFor each possible fault section (regarded as an object inthis paper), one GMDH network depicted in Fig. 4is relied onto estimate whether or not a fault occurs in this section, ac-cording to the input binary alarm signals represented by XI,,X,, ..., X,,. The output is expressed by F with "1" and "0"standing for "faulted" or "no espectively. The coef-ficients, a,, a , , ..., a,, of each mial generator in thenetwork (as shown in (1)) are est using an optimizationalgorithm to search for the parameters with minimum AIC(Akaike's Information Criterion)-like criteria [19]. The crria prefer the model with both highe r fitting accu racy and lesscomplexity of the model structure to avoid the overfittingproblems encountered in general modeling process. Detailednetwork structure determination and parameter estimationmethod can be referred to [16 -181. loying the GrvlDH net-work and the training data compil the MMI module, thephase I diagnosis module is impleE. Phase I1Diagnosis Modulephase I diagnosis module and the alarm signalsing sequence provided by the DPU, the phasemodule accomplishes the fa ult diagnosis throughMication of the fault type and detailed explanation of the faultsituation. To acheve this purpose on-line and real-time, theproposed fast bit-operation logical inference mechanism isemployed in the phase I1 diagnosis module. The inferencemechanism of the bit-opera tion logics is des cribed below.Bit-Operation Logical Inference Mec hanism

On the basis of the possible fault sections provided by th e

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354Shan-shang IS Lung-Clu EI S

. .~Subnetwork 2

Subnetwork 3

Fig. 6 The Practical Taipower Transmission System (Tainan area)main transformers (3 transformers in the 161kV/69kV P/S,and the rest for 69kV111.4kV S/S). Associated with these 33basic sections, total 33 objects are constructed in the two-phase diagnosis system, belonging to 3 object classes: line,bus bar, and transformer classes.B. Protective Relaying Scheme of the Practical System

The protective relaying scheme of the practica l system in-cludes primary, local back-up, and remote back-up relays andcircuit breakers. Each line section in the system employs thepilot relays (85) as the primary protection, and the directionalovercurrent (67) and ground overcurrent relays (67N) as localback-up protection. Distance relays (2 1) are additionallyequipped as local back-up protection of the subtransmissionlines from the Tainan P/S to Jong-Shiaw S I S and to Hou-ChiaS I S . They are also used as the remote back-up protection ofthe dow nstream line sections.Besides, relays of 67 and 67N ac t as the primary protec-tion of the bus bar and the remote back-up protection of thedownstream line sections and the transformers in the S/S. Thedifferential relays (87) and the overcurrent relays (51) are theprimary and the local back-up protections of the main trans-former in th e SI S , respectively, The circuit breakers trippedbythe protective relays are used to separate the sections in thepower system. As noted, there is no bus protective relays inthe 69kV S / S bus bar in typical Taipower T&D system.C. Implementation of th e HFDS

Th e HFDS has been developed on a PC-586 computers inC++ OOP language environment, which integrates all themodules of HFDS into a hierarchical and tightly-coupledcooperative system, where the GMDH network implemented

Table 2 The Number of Training Samples and Training TuneSubnetwork No Of Training Training Time (sec)Samples GMDH AN NSubnetwork 1 17 1 11Subnetwork 2 45 45 538Subnetwork 3 76 106 1227Table 3 Composition of Test Cases*single fault case double fault case triple fault case

singlephasetoground 80 95.0% 80 93.8% 40 92.5%A B A B A Bategory

phasetophasefault 45 91.1% 40 92.5% 25 92 0%doublephase toground 45 93.3% 40 95.0% 25 96.0%three-phaseshortcircuit 25 92.0% 20 90.0% 15 93.3%three-phasetoground 25 96.0% 20 95.0% 15 93.3%Note: A - No. of testing cases. B - Correct rate of fault tw e identification* The correct rate of fault sec tion identification for the testing casesreaches ashigh as 100%.by using the package of AIM networks [18] wa sthe phase I diagnosis module. A PC -tem was also used to test the SCA Dworks as shown in Fig. 6. To each subneData Bases I and I1were generate d by threquired to express the system configuration bplicit rules as done in the conventional expeConfiguration of these three subnetworks is sTotal number of training sam ples in Dtime required to establish the diagnosis system are given inTable 2. For reference of comp arison, in Table 2 the time ne-eded by the ANN is listed as well. In contrast, the timquired by the AN N is almost 10 times longer than thatdemanded by the GMDH network [18] adopted in the pro-posed diagno sis system.

The practical system was first separated into three subnet-

In the data-driv en system co

D. General TestsTo test the effectiveness oftesting data is created. The testcluding 22 0 single-fault, 100 d

can identify the fault typeformation on fault situation . In som e of the testdue to lack of more informatlot relays offered, the HFDStion estimation but fails to id entfy the fault type.In the above test cases, the p rocessing time, fro m tstamped alarm signals arriving at final steady state toplaying of the fault situations in the screen of th etook an average of only 21.63 ms. Rap id and accu ratesis results make the proposed HFDS feasible in on-line andreal-time application for the practical TaipowerIt is emphasized that in the proposed HFDSmission error or loss occurs, the fault section ma y still be

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355Table 4 The Time-stamped Alarm Signals of the Case StudyDate Time Statement Operating Sequence950305 15:21:07.03 67a, 67Ntrip (Nan-Men S/S) 195030 5 15:21:07.05 67a, 67N trip (Guang-Jou S/S)95030 5 15:21:07.08 87b (Nan-Men S/S Tr. 1)950305 15:21:07.13 CB9 operate (Nan-Men S/S)950305 15:21:07 .15 CBlO operate (Guang-Jou S/S)950305 15:21:07.30 5lb trip (Nan-Men S/S Tr . 1)95030 5 1 5:21:07.48 85 trip (Jong-Shiaw WS)950305 15:21:07.49 85 trip (Nan-Men S/S)

95030 5 15:21:07.5 3 67b, 67N trip (Jong-Shiaw S/S)95030 5 15:21:07.5 8 CB 7 operate (Jong-Shiaw S/S)

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correctly identified by the GMDH of phase I diagnosis mod-ule, but the fa ult type may n ot be identified due to incompleteinformation on th e BOL IM of the phase I1 diagnosis module.In suc h cases, the final diagnosis results of the HFDS can pro-vide at least the estimated fault section to operators. However,in the conventional expert system, such fault situation withdata-transm issionmro r or loss will result in no conclusion ora large number of possible solutions owing to no exact matchof rules in inferen ce process.E. Case Study

The case study to be further demonstrated is a simulatedexample of triple faults occurring at the same time. The faultsituation of the case study is shown in Fig. 7(a). The time-stamped alarm signals received in the contro l center from theSCADA interface are described in Table 4. Nan-Men S / S toGuang-Jou S / S line phase-a-to-ground fault took place first.The Nan-Men S / S relays, 67a and 67N, and Guang-Jou S / Srelays, 67a and 67N, operated, and circuit breakers, CB9 andCB10, were tripped by the relay s ignals. In the mean time, theprotective relay of Nan-M en S/S Tr. 1, 87b, operated and thenlocal back-up relay 5 lb also tripped in sequence. Finally,Tainan P/SAaI La*Jong-Shiaw S/S

0 : losed Breaker I