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        PARELLEL PROCESSING-A CHALLENGE AREA IN POWER SYSTEM
                     MONITORING AND CONTROL

                 Jianping Wang    Jacques Trcat
               Faculty Polytechnique de Mons,Belgium

ABSTRACT

With the technology development in computer science, a new
structure for monitoring and and control systems has been
proposed in this paper. Considering current softwares in despatch
center and substation host computers, some parallel processing
structures have been suggested for software development. Tests
results in parallel processing expert systems have proved that
parallel processing is a good method for software improvement
in modern monitoring and control systems. At last, some related
problems in parallel processing applications are presented for
researchers in power systems.

INTRODCTION

With the development of hardware and software technologies in the
field of computer science, a new revolution has been made for
traditional research and production in computers in recent years.
This revolution can be summarized as a challenge in parallel and
distributed processing both in software side and hardware side.

Parallel and distributed processing can be defined as the use of
many processors together in a single machine to solve a single
problem simulatneously.

Typical parallel processing computer structure can be divided
into two categories, that is, single instruction and multiple
data (SIMD) computers and multiple instruction and multiple data
(MIMD) computers. In general, SIMD computers are used for special
calculations such as matrix calculations and MIMD computers are
used for general parallel and distributed processing
applications.

As it is possible to make any connection structure such as
pipeline,ring, tree, mesh and hypercube for parallel processing
purpose with the current hardware and software technolgies, it
is necessary to reconsider the limitations met in traditional
monitoring and control systems in modern power systems.

In  the field of power systems, there were a few papers which
addressed the parallel processing applications. Reference[1]
described all parallel processing computers before the middel of
1980s. Reference[2] discussed many possible algorithm
improvements with parallel processing technologies in modern
energy management systems (EMS). There were also some other
papers which made parallel processing applications in some
special calulation algorithm such as load flow calculation,
stability calculation etc.

The purpose of this paper is intended to give a general
description about the possible improvement both in software and
hardware in modern monitoring and control systems in power
systems. This description will base on the concept of parallel
and distributed processing.

In the first part of this paper, a new structure for monitoring
and control system, which is based on the concept of parallel and
distributed processing,is presented. Then the discussion about
the function improvement both for numerical software and non-
numerical software as well as hybrid software is described both
for modern dispatch centers and substations. In the third part
of this paper, the software tools and related problems in
parallel processing applications are given in details. At last,
the paper conclusions are made. All of works here is intended to
highlight this promissing area for our researchers and engineers
in the field of power system monitoring and control.


HIERARACHICAL SYSTEM STRUCTURE

Current monitoring and control systems are mainly based on the
concept of centralized control because of hardware limitaions
and small system size. A typical current monitoring and control
system(SCADA/EMS) can be seen in figure 1.
























figure 1--Typical current monitoring and control system

In figure 1, the despatch center computer(DCC) will receive
all informations from all connected remote teminal units(RTU)
which located in substations and plants. Also, the DCC will send
some telecontrol information to some RTUs in order to make
control operations. Based on the concept of centralized
control,the DCC should do a lot of functions for power system
monitoring and control. In this way, the communication between
DCC and RTUs and the data processing tasks in DCC become two
bottle-neck problems in current development of SCADA/EMS.

In order to decrease the working load in DCC and increase the
communication efficiency as well as to increase the reliablity
of modern SCADA/EMS, a hierarachical distributed system structure
is presented in figure 2.























   Figure 2-A Hierarachical Distributed System Structure

In figure 2, the first level, is the dispatch center
computers(DCC). The second level are substation center
computers(SCC). We call every computer unit in the third
level as intelligent unit(IU) because these units can not only
deal with the protection functions but also make monitoring and
control functions.

With this structure in mind, the monitoring and control tasks
of a power system can be divided into three level problems in
figure 2. In IU level, every IU makes real time measurement both
for analogy and state data and work as a mixture system of
protection,control and monitoring for one bay in substations. The
SCC level computer can handle all fault diagnosis,fault location,
auto-reclose,fault event recording analysis,alarm processing and
switch operations even backup protection and state estimation in
the area of corresponding substations.  The DCC will make the
system wide monitoring and control according to the processed
data from SCC level.

In terms of figure 2,the optimized information flow relationship
can be built along with the concept of distributed processing.
Suppose the input signals from power system to one IUi can be
divided into two groups, analogy signals(Ai={a1,a2,...an}) and
digital signals(Di={d1,d2,...dn}). The totally input signals to
the IU level in one substation (for example SCC1) can be
expressed as equation (1).


If the input signals from every IUi to SCC1 are described as sets
of Asi(analogy signals set) and Dsi (digital signal set), the
total input signals from IUs to  SCC1 can be described in
equation (2).

Similarly, we have the total input signals from all substations
in one despatch center in equation (3).

The relationship between different level signals should satisfy
following functions.

>From equation (1) to (4), it is clear that every level computers
have to process some input signals and send their processed data
which relate to the high level problems. In this way, the working
load in current DCC can be decreased much more and the DCC can
focus on high level problems other than raw data processing.

PARALLEL PROCESSING APPLICATIONS IN DCC LEVEL

It is obvious that the main difference between SCADA and EMS is
that the state estimation, load flow calculation, stability
calculation and automatic generate control(AGC) as well as
economic dispatch(ED) functions are included in EMS. From
application point of view, the functionality is our first
criterion for the EMS in DCC.

Recent development in monitoring and control systems  has shown
that the functions in EMS with open structure is growing  more
and more through the introduction of new technology both in power
systems and in computer science. Because a power systems is a
real time system, it is necessary to make quickly response for
the functions provided in EMS. This time criterion makes more
restrictions for EMS design and many EMS are working in the
overhead conditions with this criterion.

Under traditional sequencial processing technology in computer
science, it is difficult to overcome the speed problem in EMS.
The possible ways have been done through the use of modern
reduced instruction set computers(RISC) in EMS and the increases
both in communication speed and bitwords length in computer
memorys. None of these method can effectively solve the speed
problems met in current EMS.

In the time of 1990s, our researcher should answer one question,
that is, what we can do for these problems in EMS with current
technology?  The parallel and distributed processing technology
is the best solution for these problems in EMS. Although
sophisticated software and hardware in parallel processing have
not been made commercial in current industry markets, it is time
to make the possible researches to see the best ways for parallel
processing applications in the field of EMS.

The function softwares in any EMS can be divided into three
categries, that is, numerical softwares,non-numerical softwares,
hybrid softwares. All of these softwares are working with a
database and a knowledge base as well as a mixture base in turn.
Figure 3 shows the detail categries in the softwares in EMS.

















        Figure 3-Classifications of Softwares in EMS
For the parallel processing in numerical software in power
systems, reference[2] has summarized recent methods in parallel
processing.  The basic problem in these softwares is the solution
of linear algebraic equations. There are two ways to introduce
parallel processing method for this problem. One way is to solve
the current irregular sparse matrix met in sequencial processing
with parallel processing method. This method has been tested in
many papers. Another way is to introduce dense matrix such as
impendence matrix into power system calculation with parallel
processing method. The comparison between these two methods is
necessary. For the improvement in state estimation, it is easy
to use distributed processing method since the estimation matrix
can be partitioned into different blocks. There are lot of
research results in parallel processing, which deal with the same
mathematical problems in power systems in other fields.

For the non numerical softwares, which mainly mean artificial
intelligence softwares or expert systems in EMS, is very
attractive to apply parallel processing or its general form
(distributed processing) to them. Recent years research in
knowledge based systems in sequencial environment show that the
speed problem is a bottle neck problem in applications. For
example, if a fault diagnosis has 5000 rules in its knowledge
base and the actual fault situation just matches the last rule
in this expert system, the sequencial processing expert system
should wait to time when the inference engine searches the last
rule. A parallel processing method can be used to increase the
speed in this situation. A multiagent organazition expert system
is recommended to be built in which every agent works for one
part of power system fault diagnosis and make communication with
other agent.  Every agent has a local kowledge base and a
inference engine. In this way, the original large knowledge base
in sequencial situation has been separated into many small parts
in different agents in parallel processing situation so that the
response speed is increased. A structure of a multiagent  expert
system  can be seen in figure 4.  This structure can also be used
to construct other parallel processing expert systems such as
alarm processing and switch operation expert systems.

























     Figure 4-Multiagent Expert System Structure

Here, the top level agent is the host agent which not only works
as a interface between user and expert systems but also makes
last decisions from the inference results in its lower level
agents. In this way, the depth first stratgy will be
automatically implemented in multiagent expert systems. This kind
of structure has been tested for fault diagnosis in our labratory
in a SUN workstation which has 32 transputers. The parallel
system only use 23ms to produce a diagnosis result and sequencial
system will spare 78ms in the same condition.

In fact, figure 4 belongs to the category of Tree form structure
in the parallel processing field.  If the communication between
different level agents needs too much time, it is not necessary
to make fine grain parallel system and a two level multiagent
system will be a good solution.  Other possiblities exsits
according to the problem specified. For example, a star form
structure is more efficient for large grain parallel system.
The structure of star form system can be seen in figure 5.
































      Figure 5-A Star Form Parallel System Structure

In star form system, the communication work of central agent
(host) will occupy much CPU time. In order to solve this problem,
two agents for host tasks can be arranged. We may ask host agent
1 to do interface task and host agent 2 to do last decision work.
In this way, the star form parallel processing system will work
fast than tree form system.

One should know that if the problem size is too small and it
can be meet all criteria in sequenctical environment, it is not
necessary to use a parallel processing.

For the hybrid softwares such as voltage/reactive conrtrol expert
system, security asscessment expert systems, it is also to
consider a parallel processing method to increase the response
speed. In this case, a tree form hierarachical parallel
processing system structure is suitable to hybrid softwares.
Figure 6 shows the structure of this kind of hybrid system.





















         Figure 6- The Structure of A Hybrid System

In this situation, the numerical calculation and inference task
can be divided into two groups of nodes(processors) with their
host nodes B and C. A,B,C node can make communication with each
other. In this structure, the C node can make intermidiate
inference with the calculation result in different calculation
steps in B node and gives some adjust data for the calculation
in B node.  Again the node A will make last decisions and output
results to users. The more detail parallel structure in different
groups can be changed according to the problem specified. One
obvious adavantage in hybrid system is that the interface problem
between different languages, which is met in sequencial
environment, disappeares automatically in parallel processing
system.


PARALLEL PROCESSING APPLICATIONS IN SCC LEVEL

According to the concept of 'large scale systems', the
decomposition and hierarachical control are two basic
criteria for large system control. Traditional substation
montioring and control system can not make any control
decisions since only RTUs are implemented in susbstations.
Although digital relays have been introduced to substations,
it is not possible to cooperate digital relays and RTUs in
susbstations. In this case, the DCC computer will works too
much as it is a centralized controller.

Since 1980s, more and more suggestions about an integrated
protection and control systems in substations have been
given[3][4]. There are not any sophisticated commercial
products until now. This situation is mainly caused by the
fact of difference between SCADA/EMS system producers
and digital relay producers. As state-of-art, it is necessry to
mix the gap between these producers and hierarachical system
structure in figure 1 is strongly recommended as a new generation
system in mornitoring and control.

With the concept of parallel processing, the SCC computer
can make a lot of functions for substation. These functions may
include event recording analysis,fault location and diagnosis,
alarm processing, switch operation, backup protection and local
state estimation. Any of these functions can be parallelised
according to the actual speed demand. The parallel structure is
very similar to those discussed in DCC level. In this way, the
DCC can save much time in data communication and processing since
SCC computer only send  processed data to DCC. The test of a
parallel fault diagnosis expert system in substation level has
been made and the result show that the parallel system is fast
than sequencial system [5].

Since SCC computer has a powerful data processing ability,
some of functions in conventional digital relays can be moved to
SCC level computers. For example, it is easy to make fault
location in SCC level because SCC computer has much more
information than a digital relay.  Similarlly, some functions in
DCC level can also be put into SCC level computers too. For
example, the switch operation sequence instructions can be
produced in SCC level so that the DCC level computer only needs
to send a abstract instruction to SCC computer other than a
detail step by step instruction. Morever, the SCC computer can
make local state estimation and send its results to DCC.

As the functions in different levels are balanced properly, the
monitoring and control system will works in a efficient manner
so that the power sytem can be maintenaned more safely and
reliablly.


SOME PROBLEMS RELATED TO PARALLEL PROCESSING APPLICATIONS

>From the hardware side, it is clear to use workstations both
in DCC level and SCC level computers. Now, there are a lot of
commercial products in parallel processing computers as
workstations. One workstation, which has 16 to 64 inter-
processors, will be a more powerful computer systems than a
workstation which is currently used in EMS.

>From software side, it is more attractive to use object oriented
programming method to build new generation softwares for power
system monitoring and control. Current numerical softwares such
as load flow calculation, stablility calculation, short circuit
calculation etc, are coded by Fortran language. These softwares
can not work as fast as expected because of the limitations in
sequencial environment and programming method. On the other hand,
the expert system softwares are mainly coded by intelligent
languages such as Lisp, Prolog which are not very convenient for
calculations. Once the communication between AI softwares and
calculation softwares are required, the interface problems will
decrease  working efficiency in these softwares.  As object
oriented programming language can not only handle the calculation
problems efficiently but also make knowledge representations in
expert systems, it is time to use this new generation programming
language to build all parallel processing softwares in power
systems.

An example of object oriented programming language is C++.  The
special advantages in C++ can be summarized as its classes,
multiple inheritance, polymophsim, data abstractions. It is very
easy to build a knowledge base through class representations in
C++ as actual knowledge itself is stored in class form in fact.
Again, it is also possible to contruct calculation classes for
numerical softwares as C++ preserves all features in language C
which is very suitable for calculation and human interface.

For parallel and distributed processing applications, it is
necessary to consider following problems when we make softwares.

The first problem is the task shedul problem. It is important
to consider the best parallel way for a given problem under
a actual computer structure. The size of subset problems should
be porperly designed so that the parallel procesing will obtain
the maxmium efficiency.

The second problem is the communication problem among the
processors. It is necessary to minimize the communication time
in a parallel processing system. When the communication time
taks too much time, it is better to reconsider the parallel
problem size and a large grain parallel algorithm might be
a good solution in this case.

The third problem is the synchronization cost problem. When
a parallel processing program is running, it is expected
to synchronize all parallel processes in some critical points
in order to cooperate the intermidiate calculation results among
different processes. The synchronization needs time to wait. If
parallel processes are not balanced, the shortest process will
wait long time to longest process. It is a good method to make
a balance tree first for parallel processing algorithm design.

The performance evaluation in a parallel processing software
can be made through the speedup value and efficiency value.
The function for these value calculations can be expressed in
equation (5).

Here, Ts is the sequencial processing time for the problem
specified and Tp is the parallel processing time and P is the
processor numbers used in the parallel processing software.

In addition to the main points described above in parallel
procesing applications, there are a lot of small subjects in
parallel processing researches.  These subjects include message
passing and shared memory methods in communications, homogeneous
and heterogeneous as well as loosely coupled parallel processing
structures, data structure and parallel compilers etc. From the
application point of view, it is enough for our researchers to
know the basic methods in parallel processing.


CONCLUSIONS

With the discussion given above, it is possible to give some
conclusions for this paper.

1 Parallel processing represent a new programming concept and
it is a challenge area both in computer science and application
areas. Since the software and hardware in parallel processing is
going to be commercialized today, it is the time to reconsider
all of the possibilities in parallel processing applications in
power systems.

2 Traditional monitoring and control system in power systems can
not met the demand in functionality.  It is better to use
a hierarachical structure to form a new generation monitoring and
control system in which substation can make more functions than
before.

3 In order to increase the speed both in software and hardware
in modern monitoring and control systems, the parallel and
distributed processing can be applied for different level
computers. Several parallel processing system structure are
proposed for the speed improvement in AI software applications
both in DCC level and SCC level.

4 The basic aspects in parallel processing have been given and
even the performance/cost in parallel processing applications is
not so good compared with traditional systems, it is a time for
us to work in this research area as the technologies in parallel
processing are developing every day.


REFERENCES

[1] H. H. Happ,"Parallel Processing In Power Systems," Int.
    Journal, Electrical Power & Energy Systems, Vol 4,No1,1982.
[2] D.J.Tylavsky and A.Bose et al,"Parallel Processing in Power
    System Computations," IEEE Trans.on  Power Systems,Vol.7,
    No2, May,1992,(IEEE Committe Report).
[3] Sachdev et al,"Microprocessor Relays in Power Systems," IEEE
    tutorial course text,pub. No.88EHO269-1-PWR,Feb,1988.
[4] J.S.Deliyannides and E.A.Udren," From Concept to Reality-
    The Implementation of an Integrated Protection and Control
    System," IEE pub.No249,pp121-126,April,1985.
[5] J.P.Wang and J.Trecat,"A Fault Diagnosis Expert System Based
    On Petri Net Model in Substations," IPEC, March 18-19, 1993,
    Singapore.

