Intelligence-Driven Power Quality Monitoring

IEEE PEDS 2005

Intelligence-Driven Power Quality Monitoring Hazlinda Hakimie, Vigna K. Ramachandaramurthy, R.N. Mukerjee Power Quality Research Group Dept. of Electrical Engineering Universiti Tenaga Nasional Selangor, Malaysia

[email protected]

Abstract- Power quality is an issue that needs continual attention and have increasingly been used as the key indicator for benchmarking of the true performance of many utilities in the world. Since quality of voltage holds significant importance in the functionality of any power network, monitoring of voltage quality has become a major area of investigation. The extreme financial burden required may not be favourable to provide measurements at each bus and line in a network in both transmission and distribution levels. However, to decide on the remedial measures, it is imperative to identify the type and the location of the fault in a power system. In this paper, an overview of the intelligence-driven power quality monitoring system is described. The intelligence-driven monitoring system is used to enhance system observability through pseudo-measurement data generation. Investigation and simulation were performed on a regional network. Subsequently, the type of fault and the probable location of fault whether it is in transmission or distribution level leading to the degradation of quality were identified.

Keyword words: monitoring system, fault location

I. INTRODUCTION Power quality performance has been increasingly perceived as the key indicator for benchmarking the true performance of many utilities in the world and thus is an issue that needs continual attention. In the past decade, the issue has gained prominence due to the increase in the number of loads sensitive to power quality and to make matters worse, these loads themselves are the causes of degradation in quality. For utilities, providing adequate power quality has been a moving goal due to the need to satisfy customers and avert the possibility of deregulation. For the users, new equipment's sensitivity to service quality means constant demand of almost perfect power quality at all times.

Since quality of voltage holds significant importance in the functionality of any power network, monitoring of voltage quality has become a major area of investigation. The extreme financial burden required may not be favourable to provide measurements at each bus and line in avork in both trasmen and inevin a network in both transmission and distribution levels. However, to decide on the remedial measures, it is imperative to locate the type, cause and the exact location of the fault in the power system. It is usual to have power quality related events, more specifically a sag or swell, surfacing at the distribution level due to a fault at the transmission level.

For power systems without SCADA systems, the status of circuit breakers are not available for detection of fault locations. Thus, there is a need for system observability

through pseudo-measurements.

In this paper, an overview of the intelligence-driven power quality monitoring initiative by Tenaga Nasional Berhad (TNB), Malaysia's leading power supplier, is presented. The methodology adopted to enhance the observability of the network through pseudo-measurement data generation is discussed. The identification of the type and probable location of the fault is also presented.

II. OVERVIEW OF MONITORING SYSTEM The objective of the power quality monitoring research project by TNB is to enhance the system observability by generating pseudo measurements, identifying type and cause of fault and the exact location of fault, thus facilitating the determination of mitigation options available. The TNB network is divided into four operating regions. In each region, a polling server called Regional PQ Hub (RPQH) is commissioned, as shown in Figure 1. The RPQH will be connected to all PQ recorders installed within the region and also to the central PQ Database server

Voltage sags of a few cycles can cause tripping of drives, lost of computer data and computer errors. Voltage sags normally do not cause equipment damage but can easily disrupt the operation of sensitive loads. Accurate

through fbre opticcommunicationlinks.

estimation of sag magnitudes and durations can help power utilities as well as users to select appropriate equipment specifications for critical processes. By identifying the areas of vulnerablity of the electrical power system, utility engineers can take measures to install power quality mitigation equipment if necessary [1].

0-7803-9296-5/05/$20.00 © 2005 IEEE

Using the data from the Regional SCADA system and PQ recorders, the Data Processing Unit (DPU) will perform intelligent data processing for the region. Each region consists of one DPU. The total of four DPUs will be

1278

PQ Recoders

the entire nber OptwC Co faultOtogether teachieve fl l hige syser wIthrved feature is expected with to O strC Ceoiln

B~~~~~Fgr. T

Ovrve

-

^

;

,;

|

f N

~~~~~~........

Q

Mntorn Sytem

_.....

Fngure I Overview of TNB Pa Monitoring System monitored by a Central Data Processing Unit (CDPU). The CDPU acts as a centre for data indexing and monitoring of the entire network. The intelligent data processine ine in the DPU is expected to perform identification of the typ and cause of fault together with the fault location with reduced data acquisition requirements. Thus, pseudo-measurement data generatiOn IS expected. Pseudo-measurements are readingsia generated to characterise a system where its generation feature is expected to achieve higher system surveialance with reduced meter placements and data communication needs thus achieving operational savings. The analysis leading to identification of area of vulnerability will help achieve early warning and facilftate operator intervention during distribution system operation.

Development of man-machine interface system is also imperative to help facilitate operator interaction, colection of operator responses and display of relevant computer or logical outputs for operator guidance. m11. PSEUDO-MEASUREMENT GENERATION FOR SYSTEM OBSERVABILITY The concept of bus impedance matrix denoted by Zbof iS of fundamental use in pseudo-measurement data generation.

The components in Zbw reflect important characteristics of the entire network. Since sag event may be of unbalanced nature, bus impedance matrix for positive, negative and zero sequenes neeto

becreate and tey aredenote by Z+bus

Zb-u and Zb°s In building these matrices, the topology of

the network as well as transformer winding connections were considered. For a phase-to-ground fault on phase a with fault impedance on Zf at bus k, the voltage sequence components of phase a is given in [21 as

Vj =V. -(z'k+l( Z*0 +zkk +z + 3zf ))Vkta Z +Z+3zf))Vk Vja = Zjk +/(Z+ O + 0 VJa = (zk +/(z kk +z+3zf))Vka j

(1)

VT,and V are pre-fault voltages on phase a at buses j and k respectively. Using equation (1) and an operator denoted as A where A is equal to -0.5+jr.866 and A2 is equal to -0.5-jO.866, the voltages of each phase at the jth bus are obtained using properties of symmetrical components such that p vV a e b f u f

Vna

eja objta

Vjb VP° +A2V.+ =

j

V PC=V°

a

W b + AV,

+ AV.+a + A

j

c

u

p

(2)

V,a

Thus, it is possible to generate pseudo-measurements for any jb bus in the network for a phase-to-ground fault.

Similarly, sequence components of phase voltages for other unbalanced faults are also available. Using (2), the phase voltages at every bus for unbalanced faults in a network can be obtained. With balanced faults, phase

1279

The ratios of phase voltages at a particular bus will change according to fault type and fault location. Also, phase voltage at one bus will be different from another bus during fault.

voltages can be obtained easily by analysing on per phase basis. System observability is enhanced via data generation of pseudo-measurement. The system in Figure 2 was subjected to pseudo-measurement data generation and a contour of the affected area was identified for bus voltages which are less than or equal to voltage of 65% for a single-line-to-ground fault on bus 8. The affected area was investigated for three different grid supply fault levels of 100MVA, 1000MVA and 7500MVA based on unbalanced fault analysis using bus impedance matrix.

Due to the high cost incurred with placement of large number of monitoring devices, a new method is required to enable system observability to maintain good power quality with selective placement of monitoring devices. The table of phase voltages ratios can be used to match actual data obtained from monitoring devices placed at specific buses. Although exact voltage values are almost impossible to be used as reference, the ratio of phase voltages observed would be a good indicator to the type of fault that has happened. The ratios need to be expanded to include a tolerance margin of the exact ratios since exact values of voltages cannot be used as reference when constructing the database.

Through pseudo-measurement data generation in this case, it is observed that as the grid supply fault level increases, the affected area shrinks due to the increase in the strength of the grid. Hence, utilities can take appropriate actions to inform customers to protect their sensitive equipments should they fall in the affected area. This demonstrates that system observability can be achieved through pseudo-measurements. IV. IDENTIFYING TYPE OF FAULT AND LOCATION OF FAULT Using phase voltages from pseudo-measurement data generation, table of ratios of phase voltages can be obtained. SO

~O Legend:

This intelligent data processing engine uses ratio of the actual voltage values and match the ratio to the reference table which contains ratio information associated with type of faults. Hence, the type of fault can be identified. The one-line diagram as shown in Figure 3 was used to obtain the table of phase voltage ratios. For the purpose of this study, the system was assumed to be static and that the fault impedance was assumed to be zero. It was also assumed that the monitoring devices were placed on Bus 2 and Bus 44.

7500MVA

nus5"1 nus I - - ~-IO MVA

IOOOMVA

Bus4_.

Bus2

Bw

Bus 5 -"

Bus 3

Bw 6 1 1-w

-

_

-- -

r wJ

Bus34

Bus 10 It4Bs2

>

s 34

w30 Bus 14

Figure 3. Tpical

ne-Line "Dagra6

Figure 2. Affected area plot for bus voltages less than or equaltoBthewthreshd41

.t \

\

.. *-~~~~~~~~.

.....Fgr

Figure 2. Affected area plot for bus voltages less than or equal to the thrshold voltage of 65% for phase-to-ground fault

1280

.TpclO eU eDari

22

A database of the ratio of remaining voltages on phases

Table 1: Table of Ratios for Different Fault Type

a, b and c seen at buses 2 and 44 was tabulated for 3-phase

Ratio at Monitor 44 for SLG fault

Ratio at Monitor 2 for SLG fault

balanced (30) fault, single-line-to-ground (SLG) fault, lineto-line (LL) fault and line-to-line-to-ground (LLG) fault. These ratios are shown in Table 1 for phase voltages on monitors at buses 2 and 44 for different types of fault at different buses. This table was used as reference for this study. The ratios for three-phase balanced fault are not included in the table since the phase voltages ratio for this type of fault is very near 1. Also included in Table 1 are tolerance margin of the exact ratios.

Faulted

Faus 1 14 26

From the monitors on buses 2 and 44, the intelligent data processor engine is able to identify the type of fault as well as the probable locations of the fault. Figure 4 shows the output of the engine. It lists the probable fault location with type of fault. In this instance, for simulation purposes, voltages at the measuring points were manually fed. However, in practice, the voltages at the monitoring points will be directly fed into the engine.

41

Va/Vb

VbNc

Vc/Va 3.3481

0.2951

1.1021

4.0921

0.2415 0.9018 0.2683 1.0019 3.7201

0.9390 0.8015 0.9687 1.0433 0.8905 1.0763 1.1476 0.9796 1.1840 0.9367

0.8102

1.0408

0.9002

0.9606 1.0673

1.1448 0.9903 1.1741 0.9382 0.8039 0.9665 1.0425

0.8933

1.0739

1.1467

0.9826

1.1813

Ratio at Monitor 2 for LL fault

0.8712 0.9680 1.0648

0.8034 0.8926 0.9819

1.0415

0.8906

0.8200

0.9983

0.9895

0.9111

1.1573 1.2730 1.1093

1.0885 1.0022 1.2202 0.8715 0.8049 1.0393 0.9683 0.8943 1.1548 1.0651

0.9838

1.2702

Ratio at Monitor 44 for LL fault

VbNc

VcNa

1.8014

0.8936

0.4529

1.3734

0.9626

2.0016

0.9928

0.5032

1.5260

1.0695

0.5514 0.6127

1.2513

1.6354

0.6504

1.7980

1.1118

1.3752

0.5185

1.3064

1.0047

1.1360

1.5280

0.5761

1.2496

1.6808

0.6337

1.0208 1.1342

1.3312 1.4791

0.5365 0.5961

identified.

26

41

2.2018 1.0921 0.5535 1.0238 1.3381 0.5322 1.1375 1.4867 0.5913

1.2476

1.6270 0.6557

Ratio at Monitor 2 for LLG fault Faulted

Bus 1

14

Possible SLG fault at 6 Possible SLG fault at 10 Possible SLG fault at 14 Possible SLG fault at 18 Possible SLG fault at 22 Possible SLG fault at 26 Possible SLG fault at 30 Possible SLG fault at 41

41

0.6241 0.9742 1.6448 0.6865 1.0716 1.8093

Faulted Bus Va/Vb

1.0224

List down the phase voltages seen on monitor 44 Input from PQ monitors for Va --> 0.925 Input from PQ monitors for Vb --> 0.935 Input from PQ monitors for Vc --> 1.03

26

Vc/Va 1.4804

Vc/Va

14

List down the phase voltages seen on monitor 2

14

Vb/Vc

Vb/Vc

1

from PQ IInput 0.9751i from PQ monitors monitors for for Va Input from Vb --> 0.94 PO monitors for Vb Input ->0.941l Input from PQ monitors for Vc -->1.05

1

Vaeb

0.5617 0.8768

Faulted Bus VaNb

From Figure 4, the type of fault for this case was identified as a single-line-to-ground fault using the voltage information fed in from the monitoring devices. Tying the output of the engine with real time circuit breakers status information from SCADA, the exact fault location can be Due to the nature of power systems, it is necessary to construct several tables of ratios for different events under the contingency analysis such as generator scheduling and various maintenance works.

Faulted Bus

26

14 26

41

VaNb

VbJVc

VcNa

0.8885

0.4575

1.9927 2.1920

0.9872

1

1.0378

1.2816

0.5083 0.5592

1.1531 1.2684

1.4240 1.5664

0.6090

14

1.0343

1.3233

0.5326

1.1492 1.2642

1.4703 1.6173 1.2770

0.5918 0.6510

26

0.5519

1.1492 1.2642 -

1.4189 1.5608

0.6132 0.6746

41

1.0859

1.6786 1.1765 0.6740 1.4711 0.9097 0.5448 1.6345 1.0107 0.6053 0.6658 0.5554

1.4516

1.1164

1.5967

1.2280

0.6171 0.6788

1.4728 1.6364

0.9082 1.0091

0.5450 0.6056

1.8000

1.1100

0.6662

Ratio at Monitor 44 for LLG fault Faulted

1.7935

1.0343

41

1

Bus

0.5481

0.6699

VaNb VbNc Vc/Va I 1.1970 I 0.9440 I 0.6452 l l l 1.3300

1.0488

0.7168

1.4630

1.1537

0.7885

1.4878

0.8734

1.6531 1.8184

0.9705 1.0675

0.6857

1.3160

0.9725

0.5696

1.4623

1.6085 1.4890

1.0806 11886 0.8728

0.6962

1.6544

0.9698

0.5610

0.6233

0.6329

0.5609

0.6233 1.8199 l 1.0668 | 0.6856

Representation of a network based on the seen voltages displayed by power quality monitors placed at specific buses around the system is a cost-effective method that can be employed by utilities to enable and facilitate system observability and system analysis.

Figure 4. List of probable fault locations V. CONCLUSION The overview of the intelligence-driven monitoring system has been described. Pseudo-measurement data generation is used to create a table of phase voltages ratios that represents the network for different types of fault. The methodology of creating this table is briefly outlined. Also included here is an output of the engine which is able to identify the probable locations of fault and the type of fault.

The effectiveness of the proposed method is highly dependent on the accuracy of the network impedances. Also, the speed of processing and yielding outputs of possible fault locations and type of faults for very largescale systems would be limited to the speed of data

1281

processing unit (DPU). However, the progressive and rapid development of microprocessors in computer systems could possibly eradicate this issue. For large network models used for contingency analysis by system planners and operators, computation on values of current and voltages need not be exact since system planners and operators are more concerned in identifying current and voltage values that can cause tripping of circuit breakers. Thus, approximations can be made. Taking this into consideration, the method proposed here is feasible for application on various power systems.

ACKNOWLEDGEMENT The authors would like to thank Tenaga Nasional Berhad Distribution (TNBD) and TNB Research (TNBR) for their support in this project.

REFERENCES [1]

[2] [3]

[4]

[5] [6]

[7]

[8]

C. Radhakrishna, M. Eshwardas, G. Chebiyam, "Impact of Voltage Sags in Practical Power System Networks", IEEE/PES Transmission and Distribution Conference and Exposition, 2001, Vol. 1,28 Oct.-2 Nov. 2001, pp. 567-572 John J. Grainger, William D. Stevenson, Jr., "Power System Analysis", McGraw-Hill International Editions, 1994 Feng Yan, Zhiye Chen, Zhirui Liang, Yinghui Kong, Peng Li, "Fault Location Using Wavelet Packets", Proceedings of International Conference on Power System Technology PowerCon 2002, Vol. 4, 13-17 Oct. 2002, pp. 2575 - 2579 M. H. J. Bollen, "Understanding Power Quality Problems Voltage Sags and Interruptions", New York: IEEE Press, 2000 Paul M. Anderson, "Analysis of Faulted Power Systems", New York: IEEE Press, 1995 D. J. Gaushell, H. T. Darlington, "Supervisory Control and Data Acquisition", Proceedings of the IEEE, Volume 75, Issue 12, Dec. 1987, pp. 1645 - 1658 V.K. R.N. Mukerjee, Wan W.N. Mahmood, Ramachandaramurthy, " Fault Point Identification in a Power Network Using Single-Point Measurement", TENCON 2004, IEEE Region 10 Conference, Vol. C, 21-24 Nov., 2004, pp. 381 - 384 D. Thukaram, H.P.Khincha, L. Jenkins, K.Visakha, "A ThreePhase Fault Detection Algorithms for Radial Distribution Networks", TENCON 2002, IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering, Vol. 2, 28-31 Oct. 2002, pp. 1242 - 1248

1282