Process simulation controlled by demons

Process simulation controlled by demons:

An AI approach for predicting the

reliability of electrical substation

architectures prior to design

R. Sandner" & A. Montoya*

* Fraunhofer-Institute for Industrial Engineering,

D-7000 Stuttgart, Germany

* General Electric Protecciones y Control S.A.,

E-48080 Bilbao, Spain

SUMMARY

The paper describes a methodological approach for integrating mathematical

models of primary and secondary equipment of an electrical substation into a

qualitative behavioural representation by means of interacting objects. The

major innovative potential of this approach lies in the possibility to allow for

an easy estimation of system reliability and behaviour of equipment under

disturbances for even not yet existing substation architectures. This enables the

protection engineer to test several design alternatives off-line before the actual

implementation at a substation will take place. Therefore, the object-oriented

simulation tool called SUB SIM has been developed, running on a 486 PC un-

der MS-Windows.

Transactions on Information and Communications Technologies vol 1, © 1993 WIT Press, www.witpress.com, ISSN 1743-3517

96 Artificial Intelligence in Engineering

INTRODUCTION

Electrical substations are the linking support of the three phases of the electri-

cal energy cycle, which consists of generation, transmission, and distribution

of energy. Through all primary and secondary equipment located in a sub-

station, the entire power network is monitored, controlled, and protected. As a

result of this, any fault in the power network is reflected in the secondary com-

ponents of the electrical substation, causing a protection relay to transmit a

tripping order to its related high-voltage or medium-voltage equipment or

forcing the equipment to send an alarm to the control centre. Vice versa, every

problem or failure in the substation minimises the performance of the whole

network. Thus, the reliability rate of the electrical energy cycle depends, in a

large proportion, on the reliability of primary and secondary components of the

substation [1].

Most of all, the availability of the protection and control system is responsible

for the stability of the electrical power network. The reliability of protection

and control strongly depends on the degree of centralisation or decentralisation

of the different functions, i.e. on the degree of integration and the distribution

between the different levels of a substation architecture, namely between level

1 (the bay level) and level 2 (the substation level) [2].

Digital relays provide a high degree of flexibility providing all of the protec-

tion functions as well as fault location and data communications capability. [3].

A very new approach are attempts to integrate control functions like measure-

ment acquisition, sequential commands of HV/MV equipment, interlocking or

synchronising into the same set of digital relays in order to reach a highly in-

tegrated link between protection and control. However, there are nearly no ex-

periences concerning the availability and reliability of these new different ar-

chitectures of system integration.


Artificial Intelligence in Engineering 97

This paper, which is based on previous research work undertaken in a Euro-

pean joint research project of the BRTTE/EURAM programme, describes how

the reliability of new protection and control system architectures can be as-

sessed by using qualitative models of electrical substations, which represent the

mathematical characteristics of the different components by means of inter-

acting objects.

THE STRUCTURE OF SUBSTATION MODELS

Computer simulation is the problem-solving process of predicting the future

state of a real system by studying an idealised computer model of the real

system, especially whenever it would be too costly or impracticable to obtain

the predictive information with real devices [4].

Usually, the model cannot represent the real system completely, but always

represents an idealised approximation. Thus, the quality of the model-based

simulation always depends on how good the quantitative or qualitative ap-

proximation represents the most important features of the real system. Espe-

cially in the simulation of the operation of an electrical substation, the com-

plexity of the system necessitates simplification in the modelling process. Po-

tentially, there are two ways of simplifying the representation of components.

One is to limit the number of equipment to be modelled so as to concentrate on

the most important components. The advantage of this approach is that you can

deeply model the behaviour of a power transformer or a line circuit breaker,

but nevertheless you may miss the important relationships between the differ-

ent equipment, because you cannot establish a network of components. Or,

more simply, you may have an excellent representation of single elements, but

you have no description of the overall structure of a substation.



The other approach in model simplification is to simplify the description of the

component itself. That means, that you will not have an exhausting representa-

tion of a transformer or any other equipment, but you are able to model the

overall substation architecture with all the dependencies and interrelations. In

any case, you have to deal with the cost of simplification. It depends on the

application, which approach best fits the needs for predictive simulation. In our

case, we have chosen the second approach, describing as many elements as

possible in terms of those features of the real system that inevitably have to be

included in the models.

The form in which the model of an electrical substation is specified is as a

network of its components. Each of the components is specified as a "black

box" with inputs and outputs, with explicit functions and methods for calcu-

lating or determining the outputs given the inputs. Usually, the black-box

functions are mathematical equations containing parameters, such as coeffi-

cients for difference equations or the specifications of random distributions.

In the models described below, we selected a different approach to represent

the physical behaviour of the substation elements by means of integrating

mathematical equations and object-oriented representation.

Every element of an electrical substation, which in this case is a distribution

substation, is modelled in terms of objects which describe the structure of the

element. The structure is defined by a collection of its most important charac-

teristics called attributes. For every attribute an attribute type, describing the

allowable value type, has been assigned. Thus, an example out of the full basic

structure of the power transformer model looks like the following:



OBJECT POWER TRANSFORMER

WITH power feeding NUMERIC

WITH power delivery NUMERIC

WITH primary voltage NUMERIC

WITH nominal secondary voltage NUMERIC

INIT 15.75 kV

WITH cooling mode COMPOUND

air cooled,

ventilation

INIT air cooled

This preliminary representation does not contain any behavioural descriptions,

which of course are mathematically formalised, nor any other functional rela-

tionship. It only describes the input and output characteristics of the trans-

former in terms of attributes, while some of them may have an initial value,

which is a characteristic one for the equipment

The behaviour of the equipment is represented privately within the objects by

means of so called WHEN-CHANGED methods. It contains a sequence of

procedural statements which are executed whenever the value of an attribute

changes. These methods contain the qualitative representation of the mathe-

matical equations describing the equipments behaviour. How they will be de-

rived from the real physical system characteristics, will be described below in

case of a 20 MVA core type power transformer with the following characteris-

tics:

Rated power: 20 MVA

Rated Voltage: HV 62KV - MV 15.75 kV

Rated Current HV 186.2 A - MV 703.2 A



The real secondary voltage Us can be calculated by the following equation:

(1) Us = Up/R

Up: primary voltage (with a range from 52kV to 72kV)

R: transformation ratio Np/Ns

Np: number of primary windings

NS: number of secondary windings

This simple equation is quite easy to represent in a WHEN CHANGED

method. Thus, the basic model above will be extended as follows:

OBJECT POWER TRANSFORMER

WITH power feeding NUMERIC

WITH power delivery NUMERIC

WITH primary voltage NUMERIC

WHEN CHANGED

BEGIN

calculated secondary voltage := primary voltage

/ transformation ratio

END

WITH nominal secondary voltage NUMERIC

INIT 15.75 kV

WITH cooling mode COMPOUND

air cooled,

ventilation

INIT air cooled

WITH calculated secondary voltage NUMERIC

WITH transformation ratio NUMERIC

INIT 4.0



WITH primary winding NUMERIC

INIT400

WITH secondary winding NUMERIC

INIT100

This is a very simple example of how to integrate mathematical equations into

a qualitative description of a substation element It gets much more complex if

you do not only consider the normal behaviour of an equipment, but try to de-

termine the output in case of either an internal failure or a fault in the line. This

may be apparent in case of a typical current transformer, a device which pro-

duces a current to be applicable to standard instruments, meters or protective

relays.

Whenever a fault occurs, and the primary current Ip and secondary current Is

are in phase, the error e can be calculated as a function of the primary current

through the following equation:

(2) e = (

while Is can be expressed in terms of an impedance equation

(3) Is =

Ze: impedance of secondary circuit load

Z$: impedance of secondary winding

ES is the effective no-load voltage represented by the transformer

equation:

(4) Eg = 2,22 • Ns • B • S • 10^



with B = induct!vity in Gauss

NS = number of secondary windings

S = cross section of the magnetic circuit (in cm̂ )

B can be calculated as follows:

(5)B=(i-H

with p = magnetic flux in the core of the inner winding

and

(6)H = Ns-IO/L

with IQ = magnetising inrush current

L = length of magnetic circuit (in cm)

These mathematical relationships can be translated into an object-oriented

qualitative representation of a current transformer as follows:

OBJECT CURRENT TRANSFORMER

WITH fault value NUMERIC

WITH transformation ratio NUMERIC

INTT4.0

WITH primary current NUMERIC

WHEN CHANGED

BEGIN

fault value := (transformation ratio * (no-load

voltage / (secondary circuit load impedance +

secondary winding impedance)) - primary

current) / primary current

END



WITH no-load voltage NUMERIC

INTT140V

WITH secondary circuit load impedance NUMERIC

WITH secondary winding impedance NUMERIC

So far, only relationships which are local to the equipment have been repre-

sented in the simulation models. As described above, this is done by WHEN-

CHANGED methods. The main interest, however, is the reliable behaviour of

protection and control functions, which requires an interchange of data and or-

ders among several elements of a substation. The representation of protection

and control simulation is encapsulated in so-called "demons" which are exe-

cuted by a forward-chaining inference engine. The firing of a demon is trig-

gered whenever an attribute within its antecedent changes its value, as, for ex-

ample, when the user adjusts the parameter of an equipment, or when the value

of any parameter changes as a consequence of a WHEN-CHANGED execu-

tion.

The above calculated fault value e can be used to detect a short-circuit in the

line by the differential protection function and to send a tripping order to the

related circuit breaker. The detection of the fault is based on the calculation of

the minimum current required to trip.

(7)

with Isc = maximum short-circuit current

K = adjusting factor

On the other hand, the minimum current must have a threshold value in order

to activate a tripping order. For reliability predictive reasons, the threshold is

kept adjustable to the protection engineer.



(8) I/n/n — ' Ijc

with 0 < _ _ 1

We represent this tripping threshold for the differential protective relay with

the following two demons:

DEMON short circuit detection

IF fault value OF CURRENT TRANSFORMER > 0

THEN minimum current := short-circuit current * adjusting

factor * 2 *

fault value OF CURRENT TRANSFORMER

DEMON tripping order

IF minimum current _ short-circuit current * threshold value

AND operating time _ differential protection time

characteristic

THEN tripping order := TRUE

AND position OF LINE CIRCUIT BREAKER := open

The operating time of the protection function has been introduced to adjust the

protection function parameters depending on the different architectures or

levell-leve!2 distribution of the functions. This allows the protection engineer

to "play" several alternatives by changing functions distribution, data commu-

nication channels, time characteristics, threshold values, etc. He even can

change the protection or control algorithm by rewriting the demons or adding

new ones.

In case of the differential line protection function, the time characteristic func-

tions may be adjusted by the function:



(9) T = k/lb-l

where:

b = 0.02 and k = 0.13 for ANSI characteristic

b= 1.00 and k= 16.00 for Very Inverse characteristic

b = 2.00 and k = 96.00 for Extremely Inverse characteristic

IMPLEMENTATION AND RELIABILITY CALCULATION

The substation simulation software SUBSIM is implemented using

LevelSObject - PRL (Production Rule Language), which combines the object-

oriented approach to represent the physical models with a procedural language

to describe mathematical equations. It runs on a 486 PC under Microsoft Win-

dows providing the user with a fully dynamic graphical interface. Thus, the

protection engineer is able to manipulate the substation equipment parameters

and the protection and control functions by simply selecting the appropriate

icon and changing the values in several display boxes in the related pop-up

window.

The simulation is - where appropriate - running under real-time conditions.

Therefore, ten system timers can be handled in parallel in order to represent the

different operating characteristics and reaction times of the system. There also

exist two databases, one for external faults (e.g. short-circuits, ground faults,

overloads) and the other for internal equipment failures (containing the equip-

ment name, the appropriate failure mode like winding degradation, and the

failure probability, which was assessed through a previously performed

FMECA on an existing distribution substation in Portugal). The retrieval of

these databases is controlled by a random function for each, in order to get a

realistic simulation of substation operation (in case of the failure database, the

random access is adjusted by the failure probabilities).



For the prediction of reliability all relevant output data of the different equip-

ment are written into a separate third database. Data are then processed by a

commercial statistical software package called CSS using the survival analysis

module of this package. For every component the failure times are used to di-

rectly estimate the reliability function:

(10)

In this equation, r(t) is the estimated reliability function, n is the total number

of cases, and the term p denotes the geometric sum across all cases less than or

equal to L dj is a constant that is either 1 if the j'th case is complete, and 0 if it

is not. This estimation of the reliability function is also called the "product

limit estimator" and was first proposed by [5].

REFERENCES

1. SANDNER, R. - Artificial Intelligence in Protection and Control of Elec-

trical Substations, Proc. of 1991 DECUS Europe Symposium "New Styles of

Computing", The Hague, Sept. 9th-13th, p.1-5 (1991)

2. HEISING, C.R., PATTERSON, R.C. and WEINTRAUB, E.Y. - Digital

Relay Software Quality, Proc. of 43rd Texas A&M Relay Conference, College

Station, Texas, April 23-25, p.1-11 (1990)

3. MONTANA, P. - "Protecciones en las Instalaciones Eldctricas. Evolucidn

y perspectivas". Boixaneu Editones, Barcelona-Mexico, 1991

4. WIDMAN, L.E., LOPARO, K.A. and NIELSEN, N.R. - "Artificial Intel-

ligence, Simulation, and Modelling". John Wiley & Sons, New York, 1989

5. KAPLAN, E.L. and MEIER, P. - Nonparametric estimation from incom-

plete observations, Journal for the American Statistical Association, 52, 457-

481 (1959)



ACKNOWLEDGEMENTS

The work described is part of the ongoing project AMPERIO, funded by the

Commission of the European Community within the BRTTE/EURAM pro-

gramme under contract number BREU-0459.


Date post:	08-Feb-2022
Category:	Documents
Upload:	others
View:	8 times
Download:	0 times

Process simulation controlled by demons

Documents