INTELLIGENT SIMULATOR DESIGN FOR DISTRIBUTED

PIPELINE NETWORKS CONTROL

Yong Xu

Department of compute Science, Guilin University of Electronic Technology, China

Keywords: Scada, Modelling, Pipelines, Intelligent Control.

Abstract: Supervisory Control and Data Acquisition (SCADA) systems are widely used to meet the ever-increasing

technological demands for monitoring and control of distributed system. An intelligent simulator is

designed to enhance the conventional SCADA system. The new architecture can be exploited to develop

integrated systems for complex distributed system management, performance prediction, fault detection and

optimized operation.

1 INTRODUCTION

SCADA (Supervisory Control and Data Acquisition)

systems use computers and communication

technologies to automate the monitoring and control

of distributed systems and processes. Use of SCADA

systems improves the efficiency of the monitoring

and control process by providing timely information

so that appropriate operational decisions can be

made. Examples of traditional complex distributed

systems are pipeline systems in irrigation, water, gas

and oil industries (Mareels, 2004), (Dieu, 2001)

(Nitivattananon,

1996). Increasingly, SCADA system

can be found in manufacturing, petrochemical and

power plants (Albert, 2003) (Shen and Hsu, 1999),

factory automation, building automation, complex

pipeline systems and traffic management systems

(Gieling, 1996) (Moten, 1997). These systems

integrate geographically distributed units with

different functions. Computers and various control

and measuring modules are widely employed to

ensure these systems are efficiently managed, well

monitored and maintained under adverse conditions.

However, the initial investment for a full-featured

SCADA system can be high in terms of hardware,

software and staff training and consequently the

knowledge acquisition from the target system are

limited. The main reasons may due to:

1) the quantity and quality of the sensors are

limited;

2) the acquisition of adequate system knowledge

may be either incomplete or impractical due to

system complexity and/or due to the change in

system parameters over a period of time;

3) information from the physical system always

involves some uncertainty as noise and device

malfunction may lead to information inconsistencies

and even conflicts;

4) control and management strategy can not be

tested and evaluated before used in real system.

Figure 1: Intelligent SCADA system architecture.

182

Xu Y. (2006).

INTELLIGENT SIMULATOR DESIGN FOR DISTRIBUTED PIPELINE NETWORKS CONTROL.

In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics, pages 182-185

DOI: 10.5220/0001202701820185

 SciTePress

The proposed SCADA system architecture uses a

simulator to enhance the information acquisition

capacity of the SCADA system. The design and

operating principles of the simulator will be

discussed.

Simulation is used as an alternative and

complementary of the real system and the

dependencies of system parameters and variables.

Use of models with the SCADA system for the plant

or processes can improve the performance of the

SCADA systems while keeping the implementation

and running cost down. Therefore models are widely

used and have been proved as successful approach

(Kingsley, 2004) (Hernebring, 2002) (Demey, 2001)

(Turner, 1991).

The simulator plays two main roles. Firstly it can

predict how the real system would behave as initial

conditions, attributed values and relationships are

systematically varied. Combined with the sensory

system, the simulator provides a reliable and real-

time representation of the real world system.

Secondly, the system simulator can also be used

as a virtual system for testing and examining the

effects of different operational strategies. By

providing system information under varied operation

conditions, the system simulator is in essence a

knowledge generator where its data stream is

available for further exploitation. Typically this may

be used as a reference in decision making at an

operator level as well as in many activities at a

management level.

2 MODEL-BASED SCADA

SYSTEM ARCHITECTURE

The architecture of the SCADA system with a

simulator is illustrated in Figure 1. It integrates the

SCADA components with the simulation, decision

and optimisation components.

Intelligent real-time systems must make high-

level decisions and diagnose unexpected events

based upon the knowledge base. They acquire data

automatically, apply heuristic methods to interpret

sensor readings and feed advice out to the process or

up to the user. Such a system can evolve from

conventional SCADA systems by adding system

simulator and intelligent reasoning modules.

Methods used for control system design should use

both algorithmic-numeric methods and symbolic

methods. The former based on conventional control,

identification, estimation and communication

theories developed for continuous-state type

systems, and the latter used for knowledge base

processing, reasoning and decision making.

3 DEVELOPMENT OF SYSTEM

SIMULATOR

3.1 System Simulator Concept

A system is characterized by a set of attributes, or

quantities that assume values. Static attributes have

fixed values and are called the parameters of the

system. Dynamic attributes can assume different

values at different times at different points in space

and are known as the state variables of the system.

The system simulation approach adopted can be very

different depending on the developer preference and

on the target system. The approach described in this

paper is a general-purpose method but with

implementation emphasis to a continuous process

system such as a flow pipeline system. Typical

examples of static attributes in pipeline systems are

the geometric parameters of pipes while common

state variables are pressure and flow rate.

In terms of variable scope, there are two types of

state variables. These are interface variables, which

have interactions with the environment, and internal

variables, which have no direct interaction with the

environment. The interface variables can be further

classified into input and output variables. Internal

variables represent the internal condition of the

system. These variables are illustrated in Figure 2

and defined as equations (1) to (3):

Figure 2: Conceptual model of a system.

(

)

(

)

(

)

{

()

}

12 m

u ,u , ...,uttt t

∈uU

(1)

(

)

(

)

(

)

{

()

}

12 q

y ,y , ...,yttt t

∈

Y (2)

(

)

(

)

(

)

{

()

}

12 n

x ,x , ...,xttt t

∈xX (3)

where U, X and Y are understood to be the range

sets of the input, state and output variables.

The state variables are any set of variables with

following properties:

1. There exists an output function λ:x→y defined

for all t

in the time base that maps current states into

current outputs;

2. There exists a state transition function

δ:(X,U)→X defined for all [t

] in the time base

that maps current states and inputs into future states.

INTELLIGENT SIMULATOR DESIGN FOR DISTRIBUTED PIPELINE NETWORKS CONTROL

183

The structure of a system is the relationships among

the input, state and output variables of a system that

give rise to its behaviour. A system, therefore, can be

completely defined by the algebraic structure

<U,X,Y,δ,λ,t>.

A mathematical model is used attempting to

quantify the attributes and to relate the components

mathematically. If a system S has the structure

<U,X,Y,δ,λ,t>, then a model of S is just some other

system S' with structure <U',X',Y',δ',λ',t'>. The

system S' is used as a substitute to study how the real

system would behave as initial conditions, attribute

values and the relationships are varied

systematically.

3.2 Object-Oriented Modelling

Object-oriented paradigm is used for dynamic

system analysis and control design for different

applications.

The basic unit in the model class hierarchy is

termed a Node and can be represented using the

notation shown in Figure 3.

Figure 3: Node notation

A Node is a model of a real-world entity

associated with a unique identifier. A Node consists

of two groups of elements: a defined set of attributes

(states) and a group of methods, commonly

implemented procedures or functions, allowing the

Node to perform various tasks.

All the node descriptive state variables are

grouped as internal variables and interface variables,

the former used for interaction with external objects

and the latter, the internal ones are private, hidden

within the node.

Nodes can be connected by coupling functions of

interface variables to form a system described by

another node, whose internal variables are only those

interface variables of the sub-nodes. In this way a

hierarchical structure model representing a complex

system can be established step by step and part by

part. The different subsystems can be analysed

separately to meet varying requirements in terms of

speed or precision.

A complex SCADA system may involve a great

number of variables in order to describe the system

for a specific requirement. Using the network of

nodes model, all the descriptive state variables are

grouped as internal variables inside the nodes with

interface variables being used for interaction

between the objects.

4 CASE STUDY: SIMULATION OF

PIPELINE SYSTEM

Pipeline system is a collection of components

including pumps, pipes, valves and filters, etc. and

can be perfectly represented using Node collection

model in a class hierarchy. In pipeline systems there

are only two basic flows, namely fluid flow and

information flow, where the fluid carries energy. The

information flow can be narrowly defined as the data

flow from sensors to the central processor unit and

that from the central processor to the actuators via

the communication network.

All the descriptive state variables are grouped as

internal variables inside the objects with interface

variables being used for interaction of the objects.

These subsystems are interconnected together by the

coupling functions of the interface variables.

As an example, in simulating components with

lumped parameters, such as most valves, connectors

and filters, the static characteristic can be used for

calculation. For pipes with considerable lengths the

distributed parameter, dynamic model has to be used

to meet the accuracy requirements. In applications

where state transience is involved, the transient flow

conditions have to be considered. In a real-time

control application, the component behaviour can be

predicted by a simple input-output map, which may

be a result of several weeks’ previous finite-element

analysis or neural-network model training.

When conditions such as initial condition and

boundary conditions being solved, all the system

state variables can be determined.

The validity and accuracy of the simulator can be

verified by system monitoring data from the sensory

system. Some successful models have been

developed for dynamic flow systems involve water

flow and air flow. The further details of these works

are reported by the author before (Xu, 1997) (Miller,

2000). Some applications in distributed process

control were also reported (Xu, 2004) (Pham, 2002).

Interface

--variables

--functions

Internal attributes

--variables

--functions

Node ID

ICINCO 2006 - SIGNAL PROCESSING, SYSTEMS MODELING AND CONTROL

184

5 INTEGRATION OF

DISTRIBUTE OBJECTS

Aided with simulator and intelligent functions, the

systems are no longer being viewed as simply

operational and engineering tools, but quasi-

autonomous decision-makers. In this role they

continue to serve as the centre for operational

responsibility, but also provide data to systems and

users outside of the control centre environment who

depend upon timely information on which to base

day-to-day business decisions.

A full solution of intelligent SCADA will also

contain the following components/modules:

z distributed I/O with real-time data exchange

(networked data acquisition and control);

z batch control and executions;

z remote network management;

z multimedia user interface (large screen

terminals etc.)

To fully exploit the potentials the intelligent

SCADA system can offer, the system needs also

considerations on:

z assure security, data protection and access

management;

z redundant system components for reliability;

z the proper infrastructure framework for

information exchange (e.g. Internet protocol

applications).

6 CONCLUSIONS

The basic idea of a simulator enhanced intelligent

SCADA system architecture is introduced. The

concept of a simulator of a long-distance pipeline

system and its implementation approach are also

briefly mentioned.

The main features of the proposed system

simulator are as follows:

1. Hierarchical structured: The object orientation

of the model system and software architecture allows

the complex system be built and upgraded gradually.

2. Evolutionary: The system may evolve by

adding more specialised and modules. New objects

can be introduced. With several identical simulators

(or several copies of the simulator) available at

different phases of development, the performance of

the system can be improved continuously without

breaking the working life cycle.

3. Intelligent: The uncertainty in physical systems

can be dealt with using modern statistical methods,

fuzzy models and neural network techniques. The

system can learn from experience and update its

memory. The AI level of the decision making

process can be developed to make the whole system

highly intelligent.

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