An Innovative Loss-of-Excitation Protection Based on ... - IEEE Xplore

IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 25, NO. 4, OCTOBER 2010

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An Innovative Loss-of-Excitation Protection Based on the Fuzzy Inference Mechanism Adriano P. de Morais, Ghendy Cardoso, Jr., and L. Mariotto

Abstract—A new loss-of-excitation protection based on fuzzy set theory has been presented. It makes use of conventional concepts of loss-of-excitation protection in synchronous generators (i.e., the behavior of internal voltage and apparent impedance trajectory). Instead of crisp values, a fuzzy inference mechanism is applied. To show the effectiveness of the proposed technique, comparisons are made with traditional protection methods by considering different generators sizes. The protection scheme proposed displays a secure and effective high-speed loss-of-excitation detection during power swings. Furthermore, the performance of the proposed method was not affected by generator parameters. Index Terms—Fuzzy set theory, generator protection, loss-of-excitation protection.

I. INTRODUCTION

ENERATOR protection requires the consideration of more different types of abnormal operating conditions. The most common abnormal conditions are short circuits, overload, overheating of windings or bearings, motoring, single-phase or unbalanced current operation, out-of-step, loss-of-excitation (LOE), etc. To produce the logic trip, traditional protection schemes are usually based on crisp values, which are combined with external auxiliary relays and wiring [1]. In this paper, the attention has been focused on the problems related to the detection of LOE conditions. In 1949, a relay was introduced for the high-speed single-phase offset LOE detection of the synchronous generator [2]. Over the years, various methods for LOE detection [3]–[5] have been developed and the majority of them are based on the apparent impedance trajectory. The conventional methods of LOE protection have been reported to maloperate on stable power swings (SPS), such as a severe fault electrically near the generator operating with a leading power factor [6]. Simply providing the time delay relay on SPS is not an ideal solution. Real cases of LOE could result in potential damage to

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Manuscript received June, 3, 2009; revised March 11, 2010. Date of publication August 23, 2010; date of current version September 22, 2010. Paper no. TPWRD-00423-2009. A. P. de Morais is with the Industrial Technical College of Santa Maria (CTISM), Santa Maria 97105-900, Brazil (e-mail: [email protected]). G. Cardoso, Jr. is with the Electromechanical Department of Technology Center at the Federal University of Santa Maria, Santa Maria 97105-900, Brazil (e-mail: [email protected]). L. Mariotto is with the Technology Center of the Federal University Federal of Santa Maria, Santa Maria 97105-900, Brazil (e-mail: [email protected]). Digital Object Identifier 10.1109/TPWRD.2010.2051462

the generator and/or system in consequence to the delay introduced on the relay logic. So there is user apprehension about the protection performance [6]–[8]. New techniques have been presented in the literature and some of them are based on artificial intelligence. A neural network for online classification and detection of fault conditions during the first swing transient stability or LOE is proposed in [9]. Another LOE protection was proposed by [10]. This is an adaptive relay based on the rate of change of reactance. The application of both methods [9] and [10] involves an extensive simulation process. To overcome these difficulties, a new LOE protection based on fuzzy inference mechanism (LOEP-FIM) has been proposed. It makes use of the traditional LOE concepts and a fuzzy set theory. The developed technique has been exhaustively tested through computational simulation. Comparative analysis, between the proposed LOEP-FIM and the widely used methods (i.e., Mason [2], Berdy [3], and positive offset [4]) was realized. The results showed that the new method of LOE protection is more reliable and faster. II. LOE CONCEPTS According to what was described in [4], the excitation generator source may be completely or partially removed through such incidents as: • accidental tripping of a field breaker; • field open circuit; • field short circuit (flashover of the slip rings); • voltage regulation system failure; • LOE system supply. When a synchronous generator loses excitation, the rotor field magnetomotive force (mmf) decays suddenly; as a result, the magnetic coupling weakens with respect to the stator mmf. During this time, the governor is still set to deliver a given amount of power to the generator, so that it will accelerate, inducting large slip frequency currents in the rotor in order to maintain the power output as an induction generator [11]. It will continue to supply some power to the system, but it will draw its excitation from the system in the form of reactive power. The most critical condition for the generator and the system is when a generator loses excitation while operating on full load. A reduction in system reactance will decline the final slip frequency and increase the power output from the synchronous generator now operating as an induction generator. The adverse effects on the unexcited asynchronous generator are high stator current, induced rotor currents, torque pulsations, and end-core heating. The stator currents can reach 2 to 4 times rated [11].

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Fig. 3. Apparent impedances path after LOE (situation).

Fig. 1. Typical behavior of terminal voltage (V ), active power (P ), and reactive power (Q) of a generator after an LOE.

Fig. 4. Fuzzy reasoning system. Fig. 2. Two-machine equivalent system.

III. FUZZY INFERENCE MECHANISMS (FIMS) No general statements may be made with respect to the permissible time that a generator can operate without excitation, and the generator manufacturers should be consulted for guidance. The time that a machine can operate without its excitation system may be a few seconds or even several minutes. Therefore, LOE protection should be fast in order to prevent severe damage to the generator and system. When the generator absorbs reactive power from the system, a terminal voltage drop will occur, which can be spread to a larger area depending on the robustness of the power system [12]. Fig. 1, obtained by means of computer simulation with the DIgSILENT® Power Factory software, shows a typical behavior of terminal voltage ( ), active power ( ), and reactive ( ) of a generator, after an LOE (situation). relays The majority of LOE protection methods uses connected to the generator terminals and are based on the concepts developed in [2]. The impedance seen by the relay, connected at the machine terminals, can be analyzed by using a simplified system represented by two generating sources as shown in Fig. 2 [2], [12]. and The relay is connected at the generator , (bus C). are the internal voltage of the machine and external equivalent system, respectively. The phase angular displacement between the two systems is represented by . , with some trigonometric manipulaIf tions, the impedance seen by the relay connected at the generator is (1) Fig. 3 shows the general shapes of the paths traced by the apparent impedance as measured at the generator when the field circuit is short-circuited by considering three different initials loading [12].

The fuzzy theory is known since 1965, when it was first introduced by Zadeh [13] in order to simplify the approach to problems that are impossible (or at least very difficult) to describe in terms of deterministic variables, but could be more easily described in terms of fuzzy variables. In the latest years, the number and variety of fuzzy-logic applications have increased significantly. Some recent applications in power system protection are included in [1], [14]–[21]. A fuzzy inference mechanism (FIM) uses a collection of fuzzy membership functions (MFs) and rules. Basically, it is described in [1] and can be represented as shown in Fig. 4: • fuzzification (comparing the input values with membership functions to obtain membership values of each linguistic term); • fuzzy reasoning (firing the rules and generating their fuzzy or crisp consequent); • defuzzification (aggregating rule consequent to produce a crisp output). IV. LOE PROTECTION BASED ON FUZZY INFERENCE MECHANISMS (LOEP-FIM) The general scheme of LOEP-FIM developed is shown in Fig. 5. The power system supplies a preprocessor with voltage and current waveforms which are converted to root mean square (rms) by an appropriate algorithm. Through these signals, apparent impedance seen by the generator and the terminal voltage, both in per unit on the machine base, are calculated. The apparent impedance and the terminal voltage feed the LOEP-FIM which, in turn, sends its output to be analyzed and then, a decision is made between trip, alarm, and no trip. A. Preprocessor Two variables that are used in traditional LOE protection methods are also used in LOEP-FIM (i.e., apparent impedance

DE MORAIS et al.: AN INNOVATIVE LOSS-OF-EXCITATION PROTECTION BASED ON THE FUZZY INFERENCE MECHANISM

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Fig. 5. General scheme of LOEP-FIM.

Fig. 8. Levels of Z .

Fig. 6. Input 1: Apparent impedance vector (Z ) centered in an mho relay.

Fig. 9. MFs of the inputs. (a) Z . (b) V .

Fig. 7. LOEP-FIM design.

and terminal voltage). The main reason for this is that these two features can detect with reasonable reliability the LOE event. However, these features, when submitted to a relay of binary logic (traditional protection), do not have very precise sets. Thus, introducing the same variables in a relay logic where the dichotomy between belonging and not belong does not exist, such as fuzzy logic, seems to be interesting. Hence, in this block, these two variables (both in per unit) on the generator base are preprocessed to be analyzed on the LOEP-FIM block. The first input, apparent impedance in per unit ( ), is a vector relay with dithat should have its origin in the center of the and , respectively, as shown ameter and offset equal to in Fig. 6. A directional unit supervises the impedance vector, so the LOE-FIM block is only activated when the power factor is below a predefined value. B. LOEP-FIM Methodology The basic idea of the LOEP-FIM block is shown in Fig. 7. It uses the concepts of conventional LOE protection in a fuzzy set theory. Each input has 3 MFs, where: MFs for the linguistic terms low, medium, and high associated with the th input of . The MFs of the first input have fuzzy parameters to identify three levels of , as shown in Fig. 8.

TABLE I PARAMETERS OF MFS INPUTS

• Low aims to identify small values of the apparent impedance vector. It has a maximum relevance degree . with impedance values of less than • Medium is set to recognize impedance vector values between 50% over and 50% below the circumference ra). This MF has a maximum relevance degree dius ( in . • High is used to identify the normal operation and initial condition of LOE. In the first case, this MF is set 50% over ), and in the latter, it is set the circumference radius ( 50% below . Table I and Fig. 9(a) show the fuzzy parameters and MFs of the input, respectively. The MFs of the second input, terminal voltage of generator in per unit ( ), were set by considering three conditions (i.e., LOE, SPS, and normal operation (NO). These MFs aims to identify three levels of (i.e., low, medium, and high). • Low: SPS events can cause a false trip (e.g., a solid threephase fault near the generator terminals). This event leads

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Fig. 10. MFs (of the) output.

TABLE II PARAMETERS OF MFS OUTPUTS

Fig. 11. Algorithm to analyze the LOE-FIM output and decision-making.

C. Analysis of LOEP-FIM Output and Decision-Making to a severe voltage drop, generally below 0.5 p.u.. Hence, this MF begins to increase its degree of relevance up to zero per unit voltage. • Medium: During an LOE situation, the voltage decays slowly from its nominal value. So this MF has fuzzy parameters that increase the relevance degree as the terminal voltage collapses. This MF has a maximum degree of relevance for voltages between 0.8 and 0.5 p.u. However, the degree of MF begins to decrease if the voltage drops beyond 0.5 p.u. This last situation is more characteristic for an SPS. • NO: The MF high has fuzzy variables based on values common to a normal operation condition, that is, 0.9–1.05 p.u. Table I and Fig. 9(b) show the fuzzy parameters and MFs, respectively, of the input , respectively. The MFs of the output has three linguistic terms: • No trip: to identify normal conditions; • Alarm: to identify the conditions of SPS and initial LOE; • Trip: to identify the LOE (situations). Table II and Fig. 10 show the fuzzy parameters and MFs (of the) output. Based on the behavior of the two inputs during the events of SPS, LOE, and normal, the following rules were developed: 1) If Low and Medium, then Trip (1); 2) If Low and High, then Alarm (1); 3) If Medium and Medium, then Trip (1); 4) If Medium and High, then Alarm (0.5); 5) If High and Medium, then Alarm (1); 6) If High and High, then No Trip (1); 7) If Low or Low, then Alarm (0.5). In some cases of LOE with light initial generator loading, the impedance vector can be a medium value and the voltage at the generator terminal does not collapse. Due to it, rule 4 has weight 0.5. The only rule that links the inputs by an or connection is Rule 7. Because of this, it has weight 0.5. The other rules have weight 1. Some extra LOEP-FIM parameters are shown in the Appendix.

The value , defuzzified output, is then examined by the decision-making algorithm shown in Fig. 11. The quantities marked with the subscript set are assigned as a fixed value by the relay operator. Block 1 is a verification that is carried out by comparing the with the values continuously updated. If the fixed value updated value in a block exceeds the fixed value, an alarm signal is a value between the MF output C and D as is reported. shown in Fig. 10. Block 2 checks whether the defuzzified output exceeds the ). is a value between the MF output G fixed value ( and F as shown in Fig. 9. The rms algorithm used in the preprocessor block was the discrete Fourrier transform (DFT) of full cycle. The error due to the dc component phasors estimated by DFT is significant and should not be tolerated in most applications involving relays [23]. Because of this, in the proposed LOEP-FIM, it is necessary to remove the dc component effect by means of a timer. 50 ms, a time delay supeSince the test system used has rior than this value is sufficient for the algorithm that does not make decisions during the period in which the dc component is present. The timer in block 5 will be started and the algorithm will begin to alternate between blocks 6 and 7. When the criterion in block 7 is fulfilled, the algorithm will send a trip signal for the circuit breaker (CB). To ensure security, the LOEP-FIM was used is 200 ms in order to delayed accordingly [3]. The time make a more consistent comparison with the others two methods (i.e, Berdy and Positive Offset) which also used the same time delay. V. LOE PROTECTION METHODS According to that described in [22], there are two principal concerns on LOE protection: • to ensure that the relay will trip (dependability) the generator quickly enough to avoid machine damage or adverse system effects during loss-of-field condition;


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Fig. 14. Power system test.

Fig. 12. Operational characteristic proposed by: (a) Mason and (b) Berdy.

Fig. 15. Initial loading of generator 2.

Offset

Fig. 13. Positive offset scheme.

• to ensure that the relay will not trip (security) the generator unnecessarily for SPS or transient disturbances which will not cause damage to the unit. The most common applications of LOE protection methods are: A. Mason In 1949, a single phase offset relay was introduced by Mason [2] for the detection of LOE in synchronous generators. This distance relay approach was developed to provide improved selectivity between LOE and other abnormal operating characteristics conditions. The relay essentially had offset and offset equal to , as shown in Fig. 12(a). of diameter B. Berdy Some years later, in 1975, Berdy [3] proposed the addition of unit to this protection scheme. The first unit should another be set with a diameter equal to 1.0 p.u. on the machine base without an external time delay. The second timed unit should be . This unit should be used to ride set with a diameter equal to through the transient conditions that might cause undesirable operation. Fig. 12(b) illustrates Berdy’s method [3]. These days, Mason’s scheme is recommended for generators 1.2 p.u. and Berdy’s for 1.2 p.u. with C. Positive Offset This technique includes impedance, directional, undervoltage elements, and timers [4], as shown in Fig. 13. Two relays may also be used: the first ( ), positive offset element, is set with a 10% margin beyond the steady-state stability limit using the following equations: Diameter

(2)

(3)

where is the reactance of the system beyond the terminals of the machine. Since the impedance unit has a positive offset, it is supervised by a directional element to prevent relay pickup for system close-in faults. When a low-voltage condition also exists, indicating a complete LOE condition where the connected system is not capable of supplying adequate reactive power, the undervoltage unit operates, tripping the generator with a time delay of 0.25 s to 1.0 s. in Fig. 13, is set with an offset The second unit, shown as equal to and with the long reach intercept equal to 1.1 . In this case, the relay with the setting should trip times with a time delay of 0.2 s to 0.3 s to ride through stable swings and system transients. VI. COMPARATIVE ANALYSIS The LOE-FIM technique has been tested by means of extensive simulations carried out on the system test shown in Fig. 14. In order to validate the method, a comparative analysis was carried out between the LOEP-FIM and the traditional LOE protection method presented in Section V. This comparative analysis was performed considering two conditions: 1) LOE simulations in the generator; 2) SPS simulations in the system in order to assess possible unwanted trip. Three different machines parameters were used, aiming to identify the performance of the methods. Both simulations, LOE and SPS, were performed for 20 different operational points of each machine. Ten leading and ten lagging power factor generator loadings were chosen. Hence, a total of 40 simulations for each generator was accomplished—20 LOE and 20 SPS. Fig. 15 shows the machine capability curve and the operating points used for generator 2. The initial loadings in per unit of each generator are shown in the Appendix. Since the generators have different capability curves, some of these points are not the same for the three generators. The power system signals as well as the generator output voltages and currents were obtained by using DIgSILENT

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Fig. 17. Trip signal of the LOE event.

Fig. 16. Trip signal of the LOE event.

TABLE III PERCENTAGE OF CASES WHERE THE TRIP SIGNAL IS SENT

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TABLE IV PERCENTAGE OF THE CASE IN WHICH THE TRIP SIGNAL IS SENT

G.

Fig. 18. Load change event with: (a) overexcitation initial loading (S = 0:25+ j: 0.025 p.u. and (b) underexcitation initial loading (S = 0:51 0 j 0.16 p.u.). TABLE V PERCENTAGE OF CASE IN WHICH THE TRIP SIGNAL IS SENT:

Power Factory software. Additional functions, such as preprocessing, FIM, decision-making, and LOE methods were achieved by MatLab. The LOE simulations were realized through a total LOE in the generator and the SPS simulations through a three-phase transient fault at the high-voltage (HV) side of the transformer ( ). The fault duration was kept close to the critical clearing time, which is 150 ms for the studied system. The power system parameters can be seen in the Appendix. VII. SIMULATION RESULTS A. Simulations Using Generator 1 ( p.u.)

80 MVA;

0.9

The methods had an excellent performance in both events (LOE and SPS) as shown in Table III. Regarding the time of operation, the LOEP-FIM identified the LOE faster than the traditional methods in 80% of the cases. Thus, in these cases, the LOEP-FIM allowed the protection to act in shorter time, avoiding generator and/or system damages. Fig. 16 shows ) p.u. where the an LOE situation (loading Mason’s method sends a trip signal in 3.9 s, positive offset in 4.7 s, and the LOEP-FIM sends an alarm signal in 1.2 s, and a trip signal in 3.4 s. B. Simulations Using Generator 2 ( p.u.

390 MVA;

1.2

All of the methods had great results on LOE events detection. However, only the LOEP-FIM actuates properly in 100% of LOE cases as shown in Table IV. Besides, it was faster than traditional methods in 100% of the LOE cases.

G.

It is normal that at light load, the LOE protection sends a trip signal after a certain time. However, even at light load, an LOS 0.4 may occur after an LOE. Fig. 17 shows a case ( p.u.) where only the LOEP-FIM sent a trip signal before LOS. During SPS events, the LOEP-FIM technique had a better performance than the traditional methods. In 20% and 15% of SPS events, the Berdy and positive offset method, respectively, sent a false trip signal. All of these cases had the generator operating at a leading power factor. In the same cases, LOEP-FIM just sends an alarm signal. In order to ensure the effectiveness of the proposed technique, load change events were tested, considering that it crosses the trip area of conventional methods temporarily but without LOE, as shown in Fig. 18(a) and (b). In these tests, a new load was added and kept in the system during 1 s. In all tested cases, the LOEP-FIM technique has not trippe and is shown to be reliable during load changes close to underexcitation limit. C. Simulations Using Generator 3 ( p.u.

500 MVA;

1.6

All of the methods could identify LOE events in almost all loadings, but the LOEP-FIM detected the LOE in a shorter time in most cases (90%). The trip time difference between the methods was not so large (approximately 1 s) as when using the and . Table V presents the results of simulations using . During SPS events, the LOEP-FIM also had better performance. Berdy and positive offset methods sent a false trip signal in 15% of the simulations while the LOEP-FIM only sent an


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TABLE VI GENERATORS PARAMETERS

Fig. 19. Trip signal of the SPS event.

alarm signal in these cases. Fig. 19 shows the trip signal of the methods during an SPS case, with machine loading of ( 0.5 p.u.) . In this situation, only LOEP-FIM acted correctly (i.e., it did not send a trip signal). ), the To evaluate the influence of the timing set ( LOEP-FIM was tested with different (0, 50, 100, and 150 ms). When no timing was applied, as expected, in some short-circuit cases, the LOEP-FIM sent a trip signal due to the dc component effect. For LOE situations, all cases were correctly identified in a shorter time. When it was used, timing above (50 ms, 100 ms and 150 ms), the trip signal percentage in LOE and SPS cases was kept, but the trip time changed with the timing used of course.

TABLE VII INITIAL LOADINGS OF EACH GENERATOR

VIII. CONCLUSION This paper presents a new LOE protection method. It makes use of conventional concepts of LOE protection in synchronous generators. Instead of crisp values, a Fuzzy inference mechanism was developed. The technique based on FIM involves some linguistic rules and has simple settings. The LOEP-FIM was exhaustively tested and compared with the most traditional methods of LOE protection using three different machine sizes. The sensitivity of the technique may be altered by means of . This term should be set close to parameter “ ” the term (MF output) for high dependability. Otherwise, for secure operation, it should be set close to parameter “ ” (MF output) as shown in Fig. 10. Similar reasoning can be done with the pa, which is part of the alarm signal logic. For exrameter (0.3-j0.2) p.u., setample, an LOE event (machine 2 with ting 0.6 and 0.3, the alarm and trip times are 0.4 and 1.12 s and 9.12 s, respectively. If it was set 0.7, the alarm and trip times are 5.53 s and 13.02 s, respectively. The LOEP-FIM developed displays high efficiency and speed for LOE detection. Compared with the traditional methods, the LOEP-FIM has better performance under events, such as LOE and SPS. It was capable of identifying 100% of the LOE cases faster than the traditional methods in the majority of the cases. The traditional methods send a false trip in some cases of SPS events. On the other hand, the LOEP-FIM technique sends an alarm signal in all cases of SPS. This information may be relevant to the operator, because it indicates an abnormal external condition. The great advantage of the proposed method is the fact that its performance is not affected by the machine parameters. In practice, this means that it is suitable to be used in generators apart from their size.

APPENDIX • generators parameters (see Table VI); • loadings (see Table VII); 510 MVA; 13.8/500 kV; • transformer: 19.0%; 500 kV; 100 km; • transmission line: 0.266 /km; 1.357 /km; 5.097 S/km; 3.3097 S/km; on machine base; • system impedance: • excitation model: System type 2 (Anderson & Fouad, 2003); • Fuzzy Inference Mechanism – MatLab caption (see Table VIII).

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TABLE VIII MATLAB CAPTIONS OF LOEP-FIM

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Adriano P. de Morais received the B.Sc. degree in electrical engineering from the Federal University of Santa Maria in 2006, where he is currently pursuing the M.Sc. degree. Currently, he is a Professor of the Industrial Technical College of Santa Maria. His research interests are power system analysis and protection and artificial intelligence applied to power systems.

Ghendy Cardoso, Jr. received the B.Sc. degree in electrical engineering from the Federal University of Santa Maria in 1995, the M.Sc. degree in power systems from the Federal University of Pará, and the Ph.D. degree from the Federal University of Santa Catarina. He was a Lecturer with the Federal University of Pará from 1997 to 2006. From 1999 to 2003, he was a doctoral student at the Federal University of Santa Catarina. Currently, he is an Adjunct Professor at the Federal University of Santa Maria. His areas of interest are fault studies, protection systems, and applications of artificial-intelligence techniques to power systems.

L. Mariotto received the B.Sc., M.Sc., .and D.Sc degrees in electric power systems from the Federal University of Santa Maria, Brazil, in 1977, 1981, and 2008, respectively. He is a titular professor at the Technology Center of the Federal University Federal of Santa Maria. His research interests are power systems stability, power system protection, and renewable energy resources.