Substation event analysis using information

from intelligent electronic devices

C.L. Hor a,*, P.A. Crossley b

a Angela Marmont Renewable Energy System Technology, Centre for Renewable Energy Systems Technology (CREST),

Loughborough University, Leicestershire LE 11 3TU, UKb Electric Power and Energy Systems, Queen’s University Belfast, Belfast, UK

Received 4 October 2004; accepted 13 December 2005

Abstract

The data acquisition capability of processor-based relays and intelligent electronic devices (IEDs) can improve reliability and reduce the global

cost of the power system. Nevertheless, the quantity and complexity of the captured data is beyond the requirements of most utilities, particularly

when ones consider their immediate operational needs. Though the data acquisition process has been highly automated, the process of assimilating

and analysing data still lags behind. Raw data must be transformed into knowledge in order to help users decide how to respond to the event and

implement the necessary actions. A promising technique for substation event analysis using rough set theory is described in this paper. It interprets

the data and outputs meaningful and concise information, which improves the performance of a data analysis system and help with the knowledge

acquisition process. A 132/11 kV substation model was developed to generate various fault scenarios for our case studies to evaluate the

performance of the rough set algorithm. The results show that it works well and efficiently with the overwhelming data.

q 2006 Elsevier Ltd. All rights reserved.

Keywords: Intelligent electronic devices (IEDs); Data overwhelm; Knowledge extraction; Rough set theory; Discernibility functions; Fault events

1. Introduction

Processor based IEDs can improve the reliability of the

power network and reduce lifetime operating costs. Never-

theless, the quantities of data acquired, particularly during a

major incident, can overwhelm an operator and lead to an

incorrect response [1]. Operators have to analyse the available

data and apply the most appropriate remedial action. Emotional

and psychological stress may result in an inadequate response

that could damage equipment, risk human life or even initiate a

catastrophic emergency [2]. What we require is useful

information that summarises the problem and helps with the

solution. The data acquired in a substation come from a

multitude of sources but often carry the same information. This

creates superfluous and/or redundant information, which

makes the analytical task harder to achieve. To improve the

speed of data handling, the data must be grouped and unified.

The data overwhelm issue not only has an impact on each piece

0142-0615/$ - see front matter q 2006 Elsevier Ltd. All rights reserved.

doi:10.1016/j.ijepes.2005.12.010

* Corresponding author.

E-mail address: c.hor@lboro.ac.uk (C.L. Hor).

of plant and its substation, but also, at the system operational

level [3]. The paper focuses on ‘system’ instead of ‘device’

data analysis and formulates a technique that converts available

data to knowledge for decision-support.

Fig. 1 shows the impact of information overload that leads

to the decrease in our decision quality and accuracy. The

burden of heavy information load can confuse the individual,

affect his ability to set priorities, or makes prior information

harder to recall [4].

2. Information flow

The terms data and information are often used synonymously,

but they are not the same. Data describes what was collected

through observation and inference. Information describes what

was produced when data is analysed and organised.

Fig. 2 shows the steps required to convert raw data into

useful information before it can actually be used as knowledge.

Fig. 3 presents a future digital control system (DCS) integrated

with an information management unit (IMU) that is used to

deliver useful information to appropriate manpower groups in a

utility. Each group uses themonitored data for a different purpose

and consequently has varied information requirements.

Electrical Power and Energy Systems 28 (2006) 374–386

www.elsevier.com/locate/ijepes

Fig. 1. Decision accuracy versus information overload.

Fig. 2. Information flow diagram.

Fig. 4. A typical 132/11 kV substation model.

Fig. 3. Future DCS with information management unit.

C.L. Hor, P.A. Crossley / Electrical Power and Energy Systems 28 (2006) 374–386 375

To understand the theory of rough sets as related to the

knowledge discovery and data mining, it is useful to define

knowledge as an ability to classify objects. It provides an useful

starting point because it leads to a formal description of

knowledge defined by Prof. Zdzislaw Pawlak, the founder of

rough set theory:

‘Knowledge consists of a family of various classification

patterns of a domain of interest, which provide explicit facts

about reality—together with the reasoning capacity able to

deliver implicit facts derivable from the explicit knowl-

edge.’ [5]

Pawlak regards a body of knowledge as a knowledge base

that:

‘.represents a variety of basic classification skills (e.g.

according to colours, temperature, etc.) of an intelligent

agent or group of agents (e.g. organisms or robots) which

constitute the fundamental equipment of the agent needed to

define its relation to the environment or itself.’ [5]

3. Substation network model

It is impossible to test all scenarios because real data from

the control centre and IEDs are difficult to obtain. To partially

solve this problem, a typical 132/11 kV substation given in

Fig. 4 and its associated feeders has been simulated using

PSCAD/EMTDC [6].

The model includes the primary plant and the secondary

protection [7] and control systems. Bus-coupler BC is assumed

closed during normal operating conditions. To prevent both

transformers tripping when a fault occurs on the 11 kV

terminals of a transformer, the directional overcurrent and

earth fault relays (DOC/DEF), i.e. {IED5 and IED6} are set to

look into their respective transformers (IEEE nomenclature

67). These relays also include non-directional time graded

earth fault elements (50, 51N) for the protection of the 11 kV

busbar and backup of the overcurrent and earth fault relays

(OC/EF), i.e. {IED1, IED2, IED3 and IED4} on the outgoing

feeders [8]. Both 132 kV feeders are protected by overcurrent

(OC) and earth fault relays (EF) which may also include

Balanced Earth Fault (not shown in Fig. 4). The 132/11 kV

transformers are protected by restricted earth fault (REF) and

biased differential unit protection (BDF) [9]. A selection of

fault scenarios was applied to the simulator. For each scenario,

Table 1

Data availability by device types

A B C D E F G H I J K L M N O P

SER ! ! ! ! ! # # # # ! # * ! ! ! !

DFR # # # # # # # # # ! # # # # # #

SCADA # # # # # ! * * # * * ! ! ! ! !RELAYS # # # # # * * # ! # * # # # # #

#: data available, !: data not available,: partial data may not available in some form. SER, sequence events recorder; DFR, digital fault recorder; SCADA,

supervisory control and data acquisition system.

C.L. Hor, P.A. Crossley / Electrical Power and Energy Systems 28 (2006) 374–386376

the operating response of the relays, circuit breakers and

voltage and current sensors are recorded and stored in an event

database.

There are many types of IEDs, which could provide data for

solving different problems. The operational data required

analysing the performance of the relay systems and circuit

breakers during a fault on the electrical system are [3]:

A. Bus phase voltages

B. Bus residual voltage

C. Line phase voltages

D. Line phase currents

E. Line residual current

F. Pilot channel data

G. Breaker, station tripping and blocking status

H. Control contact performance

I. Alarm contacts

J. Relay target data

K. Time coded information

L. Fault duration

M. Clearing time (all phases)

N. Magnitude of the fault current

O. Type of fault

P. Phases involved in the fault

Table 1 compares different devices types for the availability

of the given set of data.

Relays provide most of the information and the main

exception is the alarm contacts. Since our interest lies on lower

level data analysis rather than higher-level alarm processing,

the absence of the alarm contacts is not important. Further-

more, relays have become the preferred recording system over

the years. The cost of adding other types of recording systems

may not be justified if the use of relays as the source of data can

meet the goals [10]. Thus, protection IED is seen as the main

data source for us. There are two types of data in an IED relay,

i.e. operational and non-operational [11]. What we are

interested is the operational data that could help us in fault

analysis.

4. Discretisation

The power system state changes over time as the event

evolves. It is important to determine the condition of the

system based on the real-time data collected from IEDs. This

normally requires manipulating and processing a large volume

of data and information before the status of the system can be

verified. Real-time data in power system always contains

mixed types of data, such as analogue values and digital data.

All these data contain useful information that could provide us

with a good picture about the supervised network in a safe and

unsafe condition. For qualitative reasoning, the voltages and

currents parameters need to be discretised into three categories:

‘normal (N)’, ‘high (H)’ and ‘low (L)’. The discretisation

determines how coarsely we want to view the raw data. It is

formulated as P : R/C assigning a class c2C to each value

r2R in the domain of the attribute being discretised [12]. The

normal operating range for voltage is typically 90–110% of the

nominal voltage. To categorise the disturbances, a threshold is

set for the voltage and current signals [13]:

† The voltage thresholds:

(i) Low (L)!90% of the nominal voltage

(ii) 90%!Normal (N)!110%

(iii) High (H)O110%.

† The current thresholds:

(i) Low (L)!50% of nominal current

(ii) 50%!Normal (N)!150%

(iii) High (H)O150%.

Sometimes the real-time data received may oscillate a lot.

If this happens close to the threshold, it will lead to a flood

of events. The problem can be reduced by defining hysteresis

values, i.e. 1% hysteresis for voltage threshold and 10% for the

current threshold.

5. Information system

Information system or knowledge representation system can

be perceived as a two-dimensional data table with sets of data

represented by rows. Each row corresponds to a case or event

and each column represents an attribute, which could be a

variable, an observation or a property. The table is filled with

attributes values. A set of attributes, i.e. voltage, current logged

in 1 ms time tag represents a set of operational situations in the

substation. Each of the attributes has a value set as normal (N),

high (H) or low (L).

Table 2 describes a simplified dataset, which is composed of

a set of discrete voltages and currents over a time period of

0.139–2.012 s. It can be considered as a pair of finite and non-

empty set (U, A) as an information table. U is the universe of

objects and A is the set of attributes {IED1, IED2, IED5,

IED7} assuming that the bus-coupler is open and only a partial

single busbar system of Fig. 4 is considered. Each attribute

Table 2

Information system

Time IED1 IED2 IED5 IED7

t (s) V1 I1 V2 I2 V5 I5 V7 I70.139 N N N N N N N N

1.003 N H N N N N N N

1.004 L H N N N N N H

1.005 L H N L N H L H

1.006 L H L L L H L H

2.007 L N L L L H L H

2.011 L N L N L H L N

2.012 L L N N N N N N

Table 3

Protection status of IED1

Time IED1

t (s) Pickup Trip AR 52A 52B

0.139 0 0 0 0 1

1.004 1 0 0 0 1

1.937 1 1 0 0 1

2.007 1 1 0 1 0

2.010 0 1 0 1 0

C.L. Hor, P.A. Crossley / Electrical Power and Energy Systems 28 (2006) 374–386 377

a2A defines an information function such that, fa:U/na,where na is the set of values of the attribute a, called the domain

of a.

The information system format does not intuitively suggest

how time dependencies can be represented. When analysing

data from a substation, we are usually interested in how the

attributes change over time. Without the time dimension, it is

not possible to recognize how and when the change of states

take place. There are two approaches for analysing time series

events, i.e. event and state representations. The latter is

preferable, since the natural data interpretation from a

substation control system is more suitable for state represen-

tations. When the operator monitors the operation of a

substation, he/she scrutinises how certain monitoring, control

and protection attributes would change over time. Some

attributes, i.e. breaker(s), fault indicator and recloser status

can have a number of discrete values. Others, i.e. voltage,

current and power operate over a continuous range. In both

cases, the operator will have to monitor the values and also how

they change with respect to time. By just looking at variable

values, the operator is not able to recognize the complex

patterns exist in the data. Rather, he/she is watching out for

trends, i.e. ‘current is high over a period of time’, ‘breaker

opens’, ‘voltage and frequency of the system is lower than

nominal’ and etc. This suggests that the general trends rather

than small variations or complex patterns are more essential

when comes to analysing substation events. In order to fit the

time dimensions of event data into an information system, we

convert the time dependencies into general trends by allowing

the attribute values be a set of trends instead of values

measured at discrete points in time. This can be done by

recording when a breaker opens or closes or when a change in

the voltage or current state occurs.

Fig. 5. Schematic diagram of the upper and lower approximation of set X.

6. Protection status

For a circuit breaker, the value set is either open (1) or close

(0). To capture more information about the breaker status, we

utilise its auxiliary contacts (52A, 52B): ‘00 (breaker failed)’,

‘01 (closed)’, ‘10 (opened)’ and ‘11 (unknown)’. Table 3

presents the protection trip data, which merge with Table 2 to

describe the entire events, occurred. Relay IED1 has operated

whilst the other relays remain unchanged. The auto-recloser

(AR) has been disabled to simplify the example. Relay IED1

picked up the fault at 1.004 s, tripped at 1.937 s and the breaker

BRK1 opened at 2.007 s.

7. Rough set theory

Rough set theory is a new mathematical tool that can

discover the dependencies within the data. It removes data

redundancies and generates the decision rules using an

approximation concept. Unlike crisp sets, each rough set has

boundary line cases, i.e. objects that cannot be classified with

certainty either as members of the set or of its complement.

Elementary sets are the basic concepts of our knowledge

about reality. Some of the attributes may be irrelevant or

unimportant. Consequently, these entries are superfluous and

their removal would not worsen the classification. Rough sets

detect superfluous attributes and boundary line cases using

the approximation set [14], a pair of precise concepts called

the lower and upper approximation [15]. The lower

approximation (B*X) consists of all the objects, which

certainly belong to the concept and the upper approximation

(B*X) contains all objects, which possibly belong to the

concept. These two approximations define three regions [15]

as illustrated in Fig. 5.

– Positive region, POSB(X)ZB*X. Members of this region

are certainly members of the set to approximate.

– Negative region, NEGB(X)ZUKB*X, the complement of

the upper approximation. Members of this region are

certainly non-members of the set to approximate.

– Boundary region, BNB(X)ZB*XKB*X. Members of the

boundary region have a membership status that cannot be

ascertained with certainty, at least not on the basis of the

attributes that the approximations are built from. If there

are no boundary sets, i.e. BNB(X)ZØ (empty set), then set

X is crisp, otherwise it is rough.

C.L. Hor, P.A. Crossley / Electrical Power and Energy Systems 28 (2006) 374–386378

Let X4U. The B*(X) of X are defined as

B*ðXÞZ fX2U : BðXÞ4Xg (1)

The lower approximation of X, B*(X) is the subset of the set

X such that it is a member of the universe set, U. The upper

approximation of X, B*(X) is defined as

B*ðXÞZ fX2U : BðXÞhXs:g (2)

X is the member of U, where some of the upper

approximations of X are the element of X. Ø is an empty set.

B(X) denotes the set of all objects indiscernible with X, i.e. the

equivalence class determined by X.

7.1. Indiscernibility

Any subsetB ofA (B4A) determines a binary relation IND(B)

on U, called an indiscernibility or equivalent relation, defined as

INDðBÞZ fðx; yÞ2U2jca2A; aðxÞZ aðyÞg (3)

0a(x) denotes the attribute value, a for an event x. If

(x,y)2IND(B), then the events x and y are indiscernible from

each other with respect to the attributes ofB. The family of all the

equivalence classes of IND(B) is denoted by U/IND(B) and an

equivalence class of IND(B) containing the event x is denoted by

B(x) or B-elementary sets.

The objective of reducing data is to find a minimal subset of

relevant attributes that preserves the indiscernibility relation

computed on the basis of the full set of attributes. REDUCT is

defined as a reduced set of relations that ensures the same

quality approximation as the whole set of attributes. It discerns

all events discernible by the original information system, which

is crucial in the knowledge base reduction.

Fig. 6. Procedure for generating synthetic information.

7.2. Procedure for rough sets analysis

Fig. 6 shows the procedure of the rough set algorithm that is

used to generate synthetic information from substation data.

The entire process can be broken down into five main stages:

(i) Eliminate identical attributes. The algorithm first

identifies and removes the identical attributes, which

carry the same information. This is to ensure that the

redundant attributes are not present.

(ii) Eliminate identical examples. All the identical rows

(events) are filtered out. Sometimes the same type of

messages may repeat at the different and non-

consecutive times. They carry the same information

except that one may represent the intermediate state

of fault initiation and the other may represent the

state of recovery. Though both states may not play

an essential part in fault diagnosis, it is still

necessary to discern them even they are considered

identical.

(iii) Eliminate dispensable attributes. This step is to reduce

the superfluousdata bychecking if the data is dispensable.

The process of reduction is to ensure that no attribute can

be eliminated further without losing information. Dis-

pensable events can be distinguished from each other by

the discernibility matrix or other heuristic methods. The

minimal set of attributes are called reducts.

(iv) Compose a table with reduct. The table is composed with

the reduct, which contains a summary of events.

(v) Retain the change of state. To make the table more

condense, only the change of state information is

retained.

Table 5

Reducts

Time IED1 IED2

t (s) V1 I1 V2 I1

0.139 N N N N

1.003 N H N N

C.L. Hor, P.A. Crossley / Electrical Power and Energy Systems 28 (2006) 374–386 379

7.3. Discernibility matrix

A discernibility matrix is a symmetric n!n matrix where n

denotes the number of elementary sets. For a set of attributes,

B4A in AZ ðU;AÞ with UZ{u1,u2,u3,.un}. A discernibility

matrix of B, denoted as M(B) can be formulated as

1.004 L H N N

1.005 L H N L

1.006 L H L L

2.007 L N L L

2.011 L N L N

2.012 L L N N

MðBÞZ fmBðui; ujÞgn!n

mBðui; ujÞZ fa2B : aðuiÞsaðujÞg(4)

Table 6

Change of states

Time IED1 IED2

t (s) V1 I1 V2 I2

0.139 N N N N

where i,jZ{1,.,n} and nZjU/IND(B)j

ui or uj belongs to the B-positive region ofA.mB(ui,uj) is the

set of all condition attributes from B that classify objects ui and

uj into different classes. Empty set, Ø denotes that this case

does not need to be considered. All disjuncts of minimal

disjunctive form of this function define the reducts of B.

1.003 † H † †

1.004 L † † †

1.005 † † † L

1.006 † † L †

2.007 † N † †

2.011 † † † N

2.012 † L N †

7.4. Discernibility function

To compute reducts and core, the discernibility matrix is

used with the discernibility function which is a Boolean

function of m Boolean variables u*1 ;.; u*m (corresponding to

the attributes u1,.,um) where:

m�Bðui; ujÞZ fu�ju2mBðui; ujÞg (5)

For a set of attributes B, the discernibility function can be

defined as:

f ðBÞZ oi;j2f1;.;ng

fnm*Bðui; ujÞg (6)

where mBsØ; nZjU/IND(B)j and nm�Bðui; ujÞ is the dis-

junction taken over the set of Boolean variables m�Bðui; ujÞ

corresponding to the discernibility matrix element mB(ui,uj)

[16]. To explain how the discernibility matrix computes

reducts, a simplified example based on Table 2 is formed.

Table 4 shows how the reducts are obtained by forming the

discernibility matrix with its discernibility functions

f(0.139).f(2.012) as listed:

Table 4

Discernibility matrix

Time 0.139 1.003 1.004 1.005 1.006 2.007 2.011 2.012

0.139 Ø

1.003 1 Ø

1.004 1,7 1,7 Ø

1.005 1,2,5,7 1,2,5,7 2,5,7 Ø

1.006 1,2,5,7 1,2,5,7 2,5,7 2,5 Ø

2.007 1,2,5,7 1,2,5,7 1,2,5,7 1,2,5 1 Ø

2.011 1,2,5,7 1,2,5,7 1,2,5,7 1,2,5,7 1,2,7 2,7 Ø

2.012 1 1 1,7 1,2,5,7 1,2,5,7 1,2,5,7 1,2,5,7 Ø

f ðBÞZ f ð0:139Þo f ð1:003Þo f ð1:004Þo f ð1:005Þo f ð1:006Þ

o f ð2:007Þo f ð2:011Þo f ð2:012Þ

Z ð1Þo ð1Þo ðð1n7Þo ð2n5n7ÞÞo ð2n5Þo ð1Þ

o ð2n7Þo ð1n2n5n7Þ

Z 1o ð2n5Þo ð2n7ÞZ 1o ð2n ð5o7ÞÞ

From the solution presented, the relays {IED1, IED2} or

{IED1, IED5, IED7} are identified as the main source of

information for the fault F2 on the feeder L1. We selected the

solution with the least number of IEDs; {IED1, IED2}, which

appears in Table 5 as the minimal set of relations or reducts.

The reducts may be excessive for inclusion in the report. We

thus condense it further by retaining only the change of states.

Combining Tables 3 and 6, a summary of the events can be

reported as:

Summary of the events

Feeder L1: current (H)Z1.003 s, voltage (L)Z1.004 s

Feeder L2: current (H)Z1.005 s, voltage (L)Z1.006 s

IED1: pickupZ1.004 s, trippedZ1.937 s

BRK1: openedZ2.007 s

Faulted section: feeder L1 (isolated)

System restored after 2.012 s

Table 7

Reducts

Index IED1 IED2 IED5 IED7

No. I1 V2 I2 V5 I5 V7 I7

1 N N N N N N N

2 N L N L N N N

3 H L N L N N N

4 H L N L H N H

5 H L L L H N H

6 L L N L N N N

7 L N N L N N N

8 L N N N N N N

9 H L N L H N N

Index Time, t (s)

1 0.139

2 1.002

3 1.003, 1.004, 1.128, 1.985, 2.108, 3.367, 3.492, 5.172, 5.048

1.005, 1.007, 1.008, 1.009, 1.125, 1.127, 1.987, 1.988,

4 1.989, 1.990, 1.992, 2.105, 2.107, 3.368, 3.369, 3.372,

3.373, 3.488, 5.049, 5.051, 5.053, 5.054, 5.168

1.012, 1.014, 1.022, 1.117, 1.118, 1.122, 1.123, 1.124,

5 1.994, 1.995, 2.003, 2.097, 2.098, 2.102, 2.103, 2.104,

3.377, 3.374, 3.385, 3.481, 3.482, 3.484, 3.487, 5.058,

5.066, 5.161, 5.162, 5.164, 5.167

6 1.130, 1.132, 2.110, 2.112, 3.365, 3.494, 5.046,

5.047, 5.174

7 1.137, 1.984, 2.117, 3.366, 3.502, 5.181

8 1.156, 2.136, 3.520, 5.200

9 3.491, 5.171

Table 8

Protection status

Time IED1 BRK1 Failure

t (s) Pickup Trip AR 52A 52B

0.139 0 0 0 0 1 0

1.003 1 0 0 0 1 0

1.022 1 1 0 0 1 0

1.102 1 1 0 1 0 0

1.130 0 0 0 1 0 0

1.902 0 0 1 1 0 0

1.982 0 0 0 0 1 0

1.985 1 0 0 0 1 0

2.003 1 1 0 0 1 0

2.083 1 1 0 1 0 0

2.110 0 0 0 1 0 0

3.283 0 0 1 1 0 0

3.353 0 0 0 1 0 0

3.363 0 0 0 0 1 0

3.367 1 0 0 0 1 0

3.384 1 1 0 0 1 0

3.464 1 1 0 1 0 0

3.494 0 0 0 1 0 0

4.964 0 0 1 1 0 0

5.034 0 0 0 1 0 0

5.044 0 0 0 0 1 0

5.048 1 0 0 0 1 0

5.065 1 1 0 0 1 0

5.145 1 1 0 1 0 0

5.174 0 0 0 1 0 0

Table 9

Change of states

Index IED1 IED2 IED5 IED7

No. I1 V2 I2 V5 I5 V7 I7

1 N N N N N N N

2 † L † L † † †

3 H † † † † † †

4 † † † † H † H

5 † † L † † † †

6 L † N † N † N

7 † N † † † † †

8 † † † N † † †

9 † † N † † † N

C.L. Hor, P.A. Crossley / Electrical Power and Energy Systems 28 (2006) 374–386380

8. Case studies

The followingcase studies discuss how rough set theory canbe

used to interpret substation datausingunsupervisedclassification.

The approach requires no predefined classes or priori knowledge

to discover new relations between the data. It is able to perform

data clustering automatically. The events collected from the

132/11 kV substation are time-tagged in every 1 ms and thus,

only the results are presented. In these case studies, the currents

and voltages are regarded as separate entities.

9. Permanent fault on the load feeder L1

This section considers a three phase fault (F2) at 1.0 s on the

feeder L1. The reducts computed is given in Table 7. For

checking purpose, let us take a look at the time index. The

Fig. 8. Currents of IED2 during fault period.

Fig. 7. Currents of IED1 during fault period.

Fig. 9. Voltages of IED2, IED5 and IED7 during fault period.

C.L. Hor, P.A. Crossley / Electrical Power and Energy Systems 28 (2006) 374–386 381

indexes contain the number of events that repeated themselves

at multiple times. If all the events are sorted chronologically in

the index, the event at time 5.200 s in the index 8 is the last event

we received. For this event, the current of IED1 is low while the

Table 10

Station report

Station report

Date of event 13/08/04

Time of event 001

Event description

Feeder L1: current (H) at 1.003 s.

Feeder L2: current (L) at 1.012 s, voltage (L) at 1.002 s.

Feeder L5: current (H) at 1.005 s, voltage (L) at 1.002 s.

Feeder L7: current (H) at 1.005 s.

IED1: tripped multiple times.

Breaker: BRK1 opened multiple times.

Loss of supply: feeder L1.

Protection system operation analysis

Relay IED1 Types

First pickup (s) 1.003, 1.985, 3.367, 5.048

Primary (s) 1.022, 2.003, 3.384, 5.065

Backup N/A

Status Healthy Breaker state

Recloser Yes Number

Reclose time 1.902s, 3.283s, 4.964s, N/A

Status Healthy No. breakers

Breaker (s) 1.102, 2.083, 3.464, 5.145

Breaker time 0.08 s Breaker status

Estimated fault data

Fault inception 1.0 s

Faulted section L1

Maximum fault 32.21 kA

Breaker contact wear 406.68!106 As

Line currents, voltages (faulted section)

RMS value Pre-fault Max. fault

In 0.000 0.000

Ia 0.513 31.89

Ib 0.513 31.98

Ic 0.513 32.21

Va 6.170 0.000

Vb 6.140 0.000

Vc 6.300 0.000

Vab 10.86 0.000

Vbc 10.58 0.000

Vca 10.80 0.000

rest, i.e. IED2, IED5 and IED7 are normal. Referring to the

voltageV5 at time 1.156, 2.136, 3.520, and 5.200 s (see Index 8),

it indicates a recovery after the opening of BRK1. However, it

dropped to low after BRK1 was reclosed on the faulty feeder.

Based on the logic data in Table 8, we can conclude that the

fault was permanent. Table 7 was thus condensed to Table 9 as

a summary of the events represented as follows:

Summary of the events

Feeder L1: current (H)Z1.003 s

Feeder L2: current (H)Z1.012 s, voltage (L)Z1.002 s

Feeder L5: current (H)Z1.005 s, voltage (L)Z1.002 s

Feeder L7: current (H)Z1.005 s

IED1: pickupZ1.003 s, trippedZ1.022 s,

BRK1: openedZ1.102 s, Faulted section: feeder L1

System restored after 5.200 s

Event number 11:20:12 am

Sample rate 1 ms

79, 50/51, 50/51N

Healthy

3

1 (BRK1)

52A(1), 52B(0)

Fault types A–B–C, permanent

Avg. duration z0.098s

Post-fault Unit

0.000 (kA)

0.000 (kA)

0.000 (kA)

0.000 (kA)

0.000 (kV)

0.000 (kV)

0.000 (kV)

0.000 (kV)

0.000 (kV)

0.000 (kV)

Table 12

Reducts

Index IED1 IED2 IED5 IED7

No. V1 I1 V2 I2 V5 I5 I7

1 N N N N N N N

2 L H L N L N N

3 L H L N L H H

4 L H L L L H H

5 L H N N L H N

6 L H N N L N N

7 L L N N L N N

8 L L N N N N N

9 L H L N N N N

10 L H L N L H N

11 L N N N N N N

Index Time, t (s)

1 0.139, 5.045

2 1.004, 1.975, 3.345

3 1.007, 1.109, 1.977, 2.079, 3.348, 3.449

4 1.012,1.984, 3.354

5 1.112, 2.082, 3.452

6 1.113, 2.083, 3.453

7 1.114, 1.115, 2.084, 3.454

9 1.121, 2.091, 3.461

10 1.974, 3.344

11 3.347

12 5.027

Table 13

Change of state

Index IED1 IED2 IED5 IED7

No. V1 I1 V2 I2 V5 I5 I7

1 N N N N N N N

2 L H L † L † †

3 † † † † † H H

4 † † † L † † †

5 † † N N † † N

Table 11

Protection status

Time IED1 BRK1 Failure

t/s Pickup Trip AR 52A 52B

0.139 0 0 0 0 1 0

1.004 1 0 0 0 1 0

1.011 1 1 0 0 1 0

1.091 1 1 0 1 0 0

1.115 0 0 0 1 0 0

1.891 0 0 1 1 0 0

1.961 0 0 0 1 0 0

1.971 0 0 0 0 1 0

1.974 1 0 0 0 1 0

1.981 1 1 0 0 1 0

2.061 1 1 0 1 0 0

2.085 0 0 0 1 0 0

3.261 0 0 1 1 0 0

3.331 0 0 0 1 0 0

3.341 0 0 0 0 1 0

3.344 1 0 0 0 1 0

3.351 1 1 0 0 1 0

3.431 1 1 0 1 0 0

3.456 0 0 0 1 0 0

4.931 0 0 1 1 0 0

5.001 0 0 0 1 0 0

5.011 0 0 0 0 1 0

C.L. Hor, P.A. Crossley / Electrical Power and Energy Systems 28 (2006) 374–386382

The time of fault inception can be estimated using the

abnormal condition status flag at about 1.002 s when voltage

sags were first detected by the feeder L2 and L5. The duration

of the fault is determined by measuring the time period from

relay pick-up to the breaker opening. The nominal breaker

clearing time in the simulation is set 0.08 s. For any breaker

opening time longer than 0.15 s, it is considered not healthy or

faulty. The condition of the auto-recloser can be assessed

through each reclosing period. For instance, the reclosing

signal were initiated at 1.902, 3.283 and 4.964 s and the dead

times calculated were 0.8, 1.2 and 1.5 s which matched our

initial settings.1 Therefore, the auto-recloser operated cor-

rectly. The relationship between the sequence components can

be used to classify the type of faults occurred. The breaker

BRK1’s last attempt to open was at 5.145 s before it was

actually lock-out. The system was then restored after 5.200 s.

All phases of currents on the feeder L1were high at the time of

fault (see Fig. 7) while L2 experienced current low (see Fig. 8).

The fault was a three-phase type. Since L2 has a similar pattern to

L3 and L4, this explains that the currents in all load feeders were

low except L1. The fault must thus be located at the feeder L1.

Fig. 9 illustrated how the voltage varied against the time

sequence in the dataset. V7 merely dropped during the fault

period compared to the V2 and V5. The fault was therefore at the

downstream. Table 10 shows a detailed station report that can

be generated by processing the information from Tables 8 and

1 The dead times of the recloser were set artificially short to minimise the

runtime of the simulation. Shorter dead times mean that the relay is not able to

reset fully. The actual reset time of relay models can be as long as 30 s or more.

To compensate this, the reset time of the relays were modified to reset 10 times

faster than the original reset characteristics [7].

9 and some additional data, which can be downloaded from

IED1 or other IEDs from the faulted section.

9.1. Transient fault on the load feeder L1

The study considers almost the similar case as previous case

except that the fault was transient and the circuit breaker

remained closed at the end of the fault period.

Table 11 presents the protection status of IED1 subject to

the transient fault on L1. This sequential event consists the

change of states (or trends) of the protection systems. For

example, IED1 has picked up at 1.004, 1.974 and 3.344 s and it

tripped at 1.011, 1.981 and 3.351 s, respectively. The breaker

BRK1 opened at 1.091, 2.061 and 3.431 s and the opening time

is 0.08 s, which is within the normal operating range. The

6 † † † † † N †

7 † L † † † N †

8 † † † † N † †

9 † H L † † † †

10 † † † † L H †

11 † N N † N N †

Fig. 10. Currents of IED1 against time.

Fig. 11. Currents of IED2 against time.

Fig. 12. Voltages of IED1, IED2, IED5 and IED7 against time.

C.L. Hor, P.A. Crossley / Electrical Power and Energy Systems 28 (2006) 374–386 383

reclosing signals were applied at 1.891, 3.261 and 4.931 s and

the dead times were 0.8, 1.2 and 1.5 s. The recloser operated

correctly as indicated (‘0—no fault’) by the failure indicator.

The reducts computed via discernibility matrix are given in

Table 12.

tZ0.139 s is the time at which the simulation attained a

steady state condition, e.g. voltageZnormal, currentZnormal.

The indexes given in Table 12 showed the number of events

that repeated themselves at multiple times. 5.045 s (index 1)

was the last event we received indicating that all the currents

and voltage had recovered to normal. Based on the logic data in

Table 11, the fault was transient and had been cleared. The final

solution in Table 13 was derived by condensing the reducts in

Table 12.

Combining Tables 11 and 13, a summary of events can be

reported as:

Summary of the events

Feeder L1: current (H)Z1.004 s, voltage (L)Z1.004 s

Feeder L2: current (L)Z1.012 s, voltage (L)Z1.004 s

Feeder L5: current (H)Z1.007 s, voltage (L)Z1.004 s

Feeder L7: current (H)Z1.007 s

IED1: pickupZ1.004 s, trippedZ1.011 s. Reclosed: 3

BRK1: openedZ1.091 s

Faulted section: feeder L1 (isolated)

System restored after 5.027 s

Fig. 10 showed that only the A-phase current on L1 is high

during the fault period while L2 suffered a severe drop in

A-phase current (see Fig. 11). The fault is a single phase A to

earth type.

A voltage sag on the feeder L1 in Fig. 12 confirmed that the

fault has occurred on L1. By integrating the summary of events

and the additional source from IED1, we can generate a more

detailed station report given in Table 14.

9.2. Directional fault on the feeder L5

This case study considered the effect of a solid

directional A–B fault, F1. IED6 tripped and the breaker

BRK6 and BRK8 opened to clear the fault. IED5 did not

operate as it saw the fault in the reverse direction. Table 15

shows that IED6 tripped at 1.039 s and BRK6 and BRK8

opened at 1.119 s.

Table 16 shows the reduct table for the directional fault

scenario. Table 17 presents the final solution by condensing

the reducts in Table 16. In the reduct table, we can see that

the algorithm has identified five IEDs as the main data

sources. Among these IEDs, some are obviously redundant.

If we compare the reducts in a pre- and post-protection

system operation [17], we can see that {IED7, IED8} and

{IED5, IED6} are basically the same. The final reducts

should thus contain only three IEDs. We picked the

solution: {IED1, IED6, IED8} due to the responses of

IED6, BRK6 and BRK8.

Fig. 13 shows the voltage sags on L1, L5, L6 and L7

subject to the directional fault F1. All the voltages

recovered from the sags except V6, which gradually

decreased to zero. In Fig. 14, the current variations of the

directional IED5 and IED6 shows that the magnitude of

phase As and phase Bs currents have increased

significantly compare to the phase Cs. The fault is a

phase A–B type since no neutral currents were detected in

T1 and T2.

Combining with the information from Table 15, a summary

of the events can be interpreted as:

Table 14

Station report

Station report

Date of event 01/10/04 Event number 23:12:06 pm

Time of event 002 Sample rate 1 ms

Event description

Feeder L1: current (H)Z1.004 s, voltage (L)Z1.004 s.

Feeder L2: current (L)Z1.012 s, voltage (L)Z1.004 s.

Feeder L5: current (H)Z1.007 s, voltage (L)Z1.004 s.

Feeder L7: current (H)Z1.007 s.

IED1: tripped at 1.011 s; resets at 1.115 s.

Breaker: BRK1 opened at 1.091 s.

Loss of supply: none. System restored after 5.045 s.

Protection system operation analysis

Relay IED1 Types 79, 50/51, 50/51N

First pickup (s) 1.004, 1.974, 3.344

Primary (s) 1.011, 1.981, 3.351

Backup N/A

Status Healthy Breaker state Healthy

Recloser Yes Number 3

Reclose time 1.891s, 3.261s, 4.931s

Status Healthy No. breakers 1 (BRK1)

Breaker (s) 1.091, 2.061, 3.431

Breaker time 0.08s Breaker status 52A(0), 52B(1)

Estimated fault data

Fault inception 1.0 s Fault types A–G, Transient

Faulted section L1 Avg. duration z0.087s

Maximum fault 32.99 kA

Breaker contact wear 284.1!106 As

Line currents, voltages (faulted section)

RMS value Pre-fault Max. fault Post-fault Unit

In 0.000 32.51 0.000 (kA)

Ia 0.513 32.99 0.513 (kA)

Ib 0.513 0.511 0.513 (kA)

Ic 0.513 0.490 0.513 (kA)

Va 6.170 4.770 6.120 (kV)

Vb 6.140 6.170 6.200 (kV)

Vc 6.300 6.020 6.290 (kV)

Vab 10.86 9.110 10.59 (kV)

Vbc 10.58 10.610 10.89 (kV)

Vca 10.80 9.080 10.76 (kV)

Table 15

Protection status

Time IED6 IED8

t (s) 67 50/51 52A 52B 50/51 52A 52B

0.139 0 0 0 1 0 0 1

1.039 1 0 0 1 0 0 1

1.119 1 0 1 0 0 1 0

1.133 0 0 1 0 0 1 0

1.139 0 0 1 0 0 1 0

C.L. Hor, P.A. Crossley / Electrical Power and Energy Systems 28 (2006) 374–386384

Summary of the events

Feeder L1: voltage sagZ1.002 s, current lowZ1.015 s

Feeder L6: voltage sag, current highz1.005 s

Feeder L8: current highZ1.005 s

Relay IED6: trippedZ1.039 s

Breaker status: BRK6, BRK8 (1.119 s—opened)

Faulted section: 11 kV feeder 6

Fault type: directional A–B, permanent

System restored after 1.172 s

Fig. 13. Voltages of IED1, IED5, IED6 and IED7 against time.

Fig. 14. Currents of IED5 and IED6 against time.

Table 17

Change of states

Time IED1 IED5 IED6 IED7 IED8

t/s V1 I1 V5 I5 V6 I6 V7 I7 V8 I8

0.139 N N N N N N N N N N

1.002 L † † † † † † † † †

1.003 † † L † L † † † † †

1.005 † † † H † † † H † H

1.007 † † † † † H † † † †

1.015 † L † † † † † † † †

1.134 † N † † † † † † † †

1.139 † † † † † L † † L N

1.140 † † † N † † † N † L

1.153 N † † † † † † † † †

1.172 † † N † † † † † † †

Table 16

Reducts

Time IED1 IED5 IED6 IED7 IED8

t (s) V1 I1 V5 I5 V6 I6 V7 I7 V8 I80.139 N N N N N N N N N N

1.002 L N N N N N N N N N

1.003 L N L N L N N N N N

1.005 L N L H L N N H N H

1.007 L N L H L H N H N H

1.015 L L L H L H N H N H

1.134 L N L H L H N H N H

1.139 L N L H L L N H L N

1.140 L N L N L L N N L L

1.153 N N L N L L N N L L

1.172 N N N N L L N N L L

C.L. Hor, P.A. Crossley / Electrical Power and Energy Systems 28 (2006) 374–386 385

10. Conclusion

The challenge for the modern power system operation

and control is the ability to interpret the data correctly

from IEDs and ensure the right decision is reached.

Without good information, action is either impossible or

foolhardy.

This paper presented a fairly simple technique that

automatically processes large volumes of raw data, identifies

the most significant and meaningful data patterns and

presents this information in an appropriate and condensed

form. This helps the operators grasp a good picture of the

events during the emergency. Several different fault scenarios

were analysed using the event datasets from the simulated

network. The case studies were conducted to evaluate the

algorithm and improve our understanding about substation

data. Like most data analysis algorithm, the defective/

incorrect data might affect the quality of extracted knowledge

particularly for those datasets that contain a high degree of

noises. Advanced filtering or other pre-processing techniques

are thus required.

Substation event processing and analysis using rough set

approach is a new research area that would certainly benefit

power utilities. It offers us the insightful analysis about the

power system data by examining how the pattern changes.

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