intelligent-management-of-electrical-systems-in-industries.docx

Intelligent
Management of
Electrical Systems in
Industries

Abstract
The automation of public electricity distribution has developed very
rapidly in the past few years. The same basis can be used to develop new
intelligent applications for electricity distribution networks in industrial plants.
Many new applications have to be introduced because of the different
environment and needs in industrial sector. The paper includes a system
description of industrial electric system management. The paper discusses on the
requirements of new applications and methods that can be used to solve problems
in the areas of distribution management and condition monitoring of industrial
networks.

CONTENTS
1 Introduction …………………..…………………….………………... 04
2 Applications for supporting the public
Distribution network management ................................................ 05
3 Description of the system environment …………………………….….08
4 Application functions for distribution
management in industrial plants ………………………………............ 11
5 Advanced Distribution
Automation ………………...............................………………....……..14
5.1 Distribution System of Future
with ADA ………………………………………….….17
6 Distribution Management
Functions …...............……………………………….…....18
7Application Functions of Data Management
Systems ………………………………........................…......…...…..21
7.1) Load modeling ………..……….….........................................21
7.2) Reliability management………………………….........……..23
7.3) Voltage dip analyses.........……………........……...............…25
7.4) Power quality analyses……………………………................26
7.5) Condition monitoring…………………………………..........26
8 Conclusion….......................................……………..…………….…...29
9 Bibliography.……………….................……………………................30

Introduction
Industrial plants have put continuous pressure on the advanced process automation.
However, there has not been so much focus on the automation of the electricity distribution
networks. Although, the uninterrupted electricity distribution is one basic requirement for
the process. A disturbance in electricity supply causing the“downrun” of the process may
cost huge amount of money. Thus the intelligent management of electricity distribution
including, for example, preventive condition monitoring and on-line reliability analysis has
a great importance. Nowadays the above needs have aroused the increased interest in the
electricity distribution automation of industrial plants. The automation of public electricity
distribution has developed very rapidly in the past few years. Very promising results has
been gained, for example, in decreasing outage times of customers. However, the same
concept as such cannot be applied in the field of industrial electricity distribution, although
the bases of automation systems are common. The infrastructures of different industry
plants vary more from each other as compared to the public electricity distribution, which
is more homogeneous domain. The automation devices, computer systems, and databases
are not in the same level and the integration of them is more complicated.
Applications for supporting the public

Distribution network management
It was seen already in the end of 80's that the conventional automation system (i.e.
SCADA) cannot solve all the problems regarding to network operation. On the other hand,
the different computer systems (e.g. AM/FM/GIS) include vast amount of data which is
useful in network operation. The operators had considerable heuristic knowledge to be
utilized, too. Thus new tools for practical problems were called for, to which AI-based
methods (e.g. object-oriented approach, rule-based technique, uncertainty modeling and
fuzzy sets, hypertext technique, neural networks and genetic algorithms) offers new
problem solving methods. So far a computer system entity, called as a distribution
management system (DMS), has been developed. The DMS is a part of an integrated
environment composed of the SCADA, distribution automation (e.g. microprocessor-based
protection relays), the network database (i.e. AM/FM/GIS), the geographical database, the
customer database, and the automatic telephone answering machine system. The DMS
includes many intelligent applications needed in network operation. Such applications are,
for example, normal state-monitoring and optimization, real-time network calculations,
short term load forecasting, switching planning, and fault management.

The core of the whole DMS is the dynamic object-oriented network model. The
distribution network is modeled as dynamic objects which are generated based on
the network data read from the network database. The network model includes the
real-time state of the network (e.g. topology and loads). Different network
operation tasks call for different kinds of problem solving methods. Various
modules can operate interactively with each other through the network model,
which works as a blackboard (e.g. the results of load flow calculations are stored in
the network model, where they are available in all other modules for different
purposes).The present DMS is a Windows NT -program implemented by Visual
C++. The prototyping meant the iteration loop of knowledge acquisition, modeling,
implementation, and testing. Prototype versions were tested in a real environment
from the very beginning. Thus the feedback on new inference models, external
connections, and the user-interface was obtained at a very early stage. The aim of
a real application in the technical sense was thus been achieved. The DMS entity
was tested in the pilot company, Koillis-Satakunnan Sähkö Oy, having about 1000
distribution substations and 1400 km of 20 kV feeders. In the pilot company
different versions of the fault location module have been used in the past years in
over 300 real faults. Most of the faults have been located with an accuracy of some
hundred meters, while the distance of a fault from the feeding point has been from
a few to tens of kilometers. The fault location system has been one reason for the
reduced outage times of customers (i.e. about 50 % in the 8 past years) together
with other automation.

The experiences as a whole were so encouraging that the DMS was modified as a
commercial product. The vendor was first a small Finnish software company. Since 1997
the DMS has been a worldwide software product of ABB Transmit Oybeing integrated to
the MicroSCADA platform. At present the DMS is in everyday use in several distribution
companies all over the world. Part of the research group behind the development of the
DMS works at present as the employees of ABB, which has confirmed the successful
commercially phase.

Description of the system environment
A big industrial plant differs from public distribution company by organizatory
structure and by system environment. A production is divided into many departments or
many companies. These units have the responsibility of production and maintenance. Very
often the maintenance is maintained by a service company. An energy department or
company is in charge of local energy production and of the distribution network. Above
organizations may have some control systems that serve for their needs only, but usually
information systems are closely connected together. A process automation system is the
most important system in an industrial plant, sometimes including other systems, as
illustrated in Fig. 1. For example, all energy production and distribution network control
tasks can be done in a process automation system. Normally, because of the reliability
reasons, vital parts of distribution network control is independent on the process
automation. The independency of process automation system vendor has been one reason
for separate systems, too.
Figure1: Automation and information systems of an industrial plant.

The systems in Fig. 1 utilize many databases, which contain data that can be used
in new applications. Process automation systems collect data for process monitoring and
optimization tools. The databases contain information of material flow, energy flow and
control data of production machines. Maintenance databases include technical
specifications and condition data of production machine components. Similar information
of electricity network components is supported by network database. Production
programs are stored in the databases of administrative systems.
Intelligent applications are needed to:
- Handle large amount of information available. This includes filtering of data and
producing new information by collecting data.
- Illustrate complex dependencies of electricity distribution and production processes in
abnormal situations.
- Give instructions for operators in fault situations. A risk of misoperation in unusual
fault situation is obvious and prevents or delay operators’ decision making.
- Automize analysis tasks. Continuous information analysis is not possible manually.
In order to introduce new intelligent applications for the management of electric
systems in industrial plants, a basis for implementation is needed. The following
requirements should be satisfied:
- Documentation of electricity distribution network is available for the systems. Network
databases can supply this information.

- Network, process and motor measurements are available for the system. This means, that
data acquisition from multiple sources with capability to use various data transfer methods
is needed, as illustrated in Fig. 2.

Application functions for distribution management in
industrialplants
As mentioned above the concept of public distribution automation cannot be
applied as such in the management of industrial electricity networks. For example, fast
and accurate fault location has a great importance for reducing the outage time of
customers in the public electricity distribution, while there is no special need of such a
function in industrial networks. Predictive condition monitoring, reliability calculations,
and protection relay coordination to prevent disturbances in advance are more important.
Caused by the features of industrial networks there are needs for methods to model
dynamic phenomena and harmonics, and to calculate load-flow and fault currents in ring
connected networks. An essential need is the load modeling which differs considerable
from the public distribution. The basis of the distribution management system (i.e. the use
of network model as the blackboard) is common in the both domains. The network model
includes the real-time topology and network calculation results in the prevailing
switching and load conditions. The main functions of system entity for the industrial
networks are listed in the following:
* Real-time network monitoring, state estimation and optimization:
- Topology management
- load flow and fault currents also as dynamic phenomena
- Monitoring and compensation of reactive power
- monitoring of harmonics and resonances

- Minimization of power losses
* Planning and simulation of operation actions
- switching planning
- Automatic load shedding and forming a local island
- switching the network as a part of the national grid
- fault situations
* Management of disturbances
- Event analysis
- Fault location and network restoration
- Preventive condition monitoring
- Protection relay coordination
- Reliability calculations
- reporting
Distribution Automation which includes feeder automation and distribution
management systems (DMS) is an important technique in distribution network. The
distribution management systems are composed of distribution management functions.
The DMF is an entity which incorporates different applications on a single platform over
which supervision is made. This mainly supports documentation of network data planning
operation and reliability management of distribution networks. Various application
functions for distribution management in industrial plants are mainly load modeling
,reliability management , power quality analysis, voltage dip analysis and condition
monitoring .All this are incorporated in a domain of distribution management functions.

Advanced distribution automation (ADA) modern day approach towards efficient
management of distribution networks.

ADVANCED DISTRIBUTION AUTOMATION
Traditional distribution systems were designed to perform one function—
distributing power to end users. The distribution system of the future will be more
versatile and will be multifunctional.
Strategic drivers for ADA are to
• Improve system performance
• Reduce outage times
• Allow the efficient use of distributed energy resources
• Provide the customer more choices and
• To integrate the customer systems
For ADA to work, the various intelligent devices must be interoperable both in
the electric system architecture and in the communication and control architecture.
.
Figure3: ADA architecture

ADA will enable the distribution system to be configured in new ways for such
things as looped secondaries or intentional islanding to facilitate easy recovery from
outages and to deal with other emergencies.
Fig: 4
The three major components of ADA
– Flexible electrical system architecture
– Real-time state estimation tools
– Communication and control system based on open architecture standards
The intelligent universal transformer is a prime example of a new electronic device that
will be a cornerstone of ADA. It will provide a variety of functions including
– Voltage stepping
– Voltage regulation
– Power quality enhancement
– New customer service options such as DC power output
– Power electronic replacement for conventional copper and iron transformers

The Flexible Electric Architecture and the Open Communications Architecture
synergistically empower each other to create the distribution system of the future.
Each of these is made more valuable by its interaction with the other.
ADA will provide improvements in many areas including
– Reliability
– System performance
– Condition monitoring
– Outage detection and restoration
– Maintenance practices and prioritization
– Automated switching and fault management
– Reactive power and voltage management
– Loss reduction and load management
– Customer service options

Distribution System of Future with ADA

DISTRIBUTION MANAGEMENT FUNCTIONS
Distribution management functions form an entity of applications supporting
documentation of network data, and planning, operation and reliability management of
distribution network in industrial plants. The functions can be included into different
computer systems, like AM/FM/GIS, Distribution Management System (DMS), and
SCADA or case specific customized applications. The main functions of distribution
management entity for the industrial networks are listed in the following:
• Documentation of network data
• Graphical user interfaces
• Real-time network monitoring, state estimation and optimization
- Topology management, load flow and fault current calculation, monitoring and
compensation of reactive power, monitoring of harmonics and resonance, and
minimization of power losses
• Planning and simulation of operation actions
- switching planning, fault situations, automatic load shedding and forming a local island
• Management of disturbances and reliability
- Preventive condition monitoring, reliability and availability management, protection
relay coordination, event analysis, fault location and network restoration, reporting.
Caused by the features of industrial networks the importance of the distribution
management functions are different as in public electricity networks. There are also needs
for new methods. An essential need is the load modeling which differs considerable from
the public distribution. Predictive condition monitoring, reliability management, and

protection relay coordination to prevent disturbances in advance have a great importance.
Some functions of the DMS for the management of public distribution networks can be
applied almost as such also in the management of industrial electricity networks, e.g.
topology management.

APPLICATION FUNCTIONS OF DATA MANAGEMENT
SYSTEMS
1) Load modeling
The essential basis for advanced application functions is the modeling of loads
connected to the network. Usually there are only few measurement points in the network.
However, loading of every load node of the network must be known in the network
calculations. For that purpose the loads are estimated by load models.
The essential need for the load models is that they form a basis for the load-flow
calculations. Results of load-flow calculations are utilized different kind of tasks as real-
time network monitoring and optimization, and switching planning. Information on loads
can also be utilized in preventive condition monitoring and reliability analyses. Although,
the loads (i.e. the current) of some nodes can be measured on-line, models are needful
because of the DMS can be used also in simulated state, when the information of system
does not correspond the current real-time state of the distribution network.
In the domain of public electricity distribution hourly load curves have been
determined for each customer group to be used in load-flow calculation and load
forecasting. In industrial plants the load modeling should be based mainly on the process

itself and its behavior. Load models can be determined by making enough measurements
in different known process conditions. However, the industrialplants vary from each other
quite much, which means that load models determined in one plant may not be able
to used as such in other one. One aim of the research work is to develop tools and methods
by which the determination of the plant specific load models can be achieved during the
installation of the automation system when enough measurements have been done and
certain process specific parameters are known. Neural networks can be used to learn the
correlations between the measurements and the process in order to produce the load model
Significant features of the load models are swiftness, simplicity, a capability to
utilize measured information, a capability to utilize inaccurate information and a capability
to adapt alternating and different conditions. The state monitoring of the DMS acts in real
times which appoint demands to the swiftness of the load models. Further the industrial
processes will be developed and so the load models must be able to adapt in varied
situation.
Demands, mentioned above, could be achieved using advanced methods and
technologies. This means using neural networks technology, fuzzy logic and self-
adaptively technologies in further development of load models of the industrial distribution
networks.

Fig 5: Network Load Model Determining
Load forecasting in the industrial environment cannot be based on any regularity
of behavior. Reliable forecasting assumes use of methods which can utilize production
plans in some time distance which also can have a large difference with each other and
include inaccurate information. The load forecasting of the network feeding some process
bases on the known behavior of the process, earlier measured values and the planned
production.
Calculation methods for meshed networks
The DMS for public distribution management included load flow and fault current
calculation procedures, which worked only in radial networks. The need for calculating
meshed networks in industrial distribution networks is anyway obvious (e.g. there are
several fault current sources).
Load flow calculation for meshed network leads to a group of non-linear

equations. Classic Newton-Raphson iteration is considered be the most competent method
for solving load flow equations, and was selected as the solver. Fault current calculation is
performed only in the symmetrical three-phase case. In fact, the calculation can be done
simply by inverting a matrix. To calculate inverse of matrix with conventional methods is
now too laborious and therefore discarded. Instead an algorithm called Z-bus algorithm is
used for calculating inverse effectively.
The load flow and fault current algorithms are implemented as a part of the DMS
so that they can utilize the common network model and topology analysis. The primary
information for the load-flow calculation is the loads of the secondary substations and
motors connected to the medium voltage network. The loading information is read from
the Access –database including the load models for different situations. The results of load
flow and fault current calculations can be studied through the user-interface of the DMS
by selecting the desired node.
2) Reliability management
The functions related to reliability have considerable economic significance in
industry. The losses of production caused by the disturbances and the inputs into the
investments of the systems including maintenance and operational arrangements join here.
The reliability can be studied with both qualitative and quantitative methods. With
a qualitative analysis the possible states of the system and reasons which lead to these are
determined with non-numerical methods. The failure modes, effects and

criticality analyses are adapted generally on the qualitative methods. Using failure modes,
effects and criticality analysis it is aimed to identify those faults of the devices or of the
subsystems which affect the capabilities of the system significantly. The system is
systematically analyzed and the effects of the component faults of the system are evaluated.
In a quantitative analysis indicators describing the capabilities of the system are calculated.
For example, availability, fault frequencies, durations of disturbances and indicators which
describe the economic appreciation of interruptions can be evaluated. The functions
supporting power distribution reliability management can be included in several different
systems which are, among others, AM/FM/GIS, the Distribution Management System
(DMS), SCADA system, maintenance systems, and documentation systems depending on
the total concept.
The load flow calculations and short circuit calculations are applications which
have central meaning in reliability analyses. The calculations make it possible to simulate
faults, to plan relaying arrangements and network operations. Switching plans operational
instructions can furthermore be stored in databases. An essential function supporting
reliability management and analyses is also the management of various instructions and
documents. There are many kind of documents which can be used to support the reliability
management. The graphical user-interface makes available the developing of the different
sophisticated user friendly functions, for example, determination of the feeding routes of
the components or loads to be examined
The estimation of the reliability technical state and capabilities of the distribution
system together with real-time condition supervision and maintenance programmes are in

a central position in the anticipating and prevention of disturbances and in the minimization
of their effects.
The analysis of reliability technical state and capability of power distribution
network is closely related to the protection coordination, too. Using fault current and load-
flow calculations personnel can evaluate how the distribution and the primary processes
will behave in fault situations of the distribution network.
3) Voltage dip analyses
A voltage dip is a sudden reduction of the supply voltage to a value between 90
%and 1 % of the declared voltage, followed by a voltage recovery after a short period of
time. Possible causes of these dips are typically faults in installations or in feeding public
networks and switching of large loads (e.g. motors). In rural areas voltage dips are
generally caused by short circuit faults in the public MV overhead network. The interest
in voltage dips is mainly due to the problems they cause on several types of equipment e.g.
tripping of adjustable-speed drives (both ac and dc drives), process-control equipment,
computers and contactors in front of some devices. The employment of IUT with the
support of ADA is a step towards reduction in these voltage dips.
4) Power quality analyses
The term Power Quality (PQ) is used with slightly different meanings. More
extensive meaning can be associated with any problems in voltage, current or
frequency deviations which result in failure, malfunction, disturbances or combination
of voltage quality and current quality. However, the voltage quality is addressed in

most cases. Voltage quality is concerned with deviations of the voltage from the ideal
and main characteristics can be described as with regard to frequency, magnitude,
waveform, symmetry of the three phase voltages and interruptions. In industrial plants on
the other hand increasing amount of disturbing devices (e.g. adjustable drives and power
electronics) and on the other hand increasing amount of sensitive devices (computers,
process automation ,electronic devices and adjustable drives) have caused growing concern
about power quality. Thus there is also a growing need to manage and monitor power
quality.
Volts
5 ) Condition monitoring
There exist many systems for condition monitoring of industrial processes,
especially for rotating machines. Monitoring usually covers electric motors that are
connected to the monitored processes. There are on-line systems designed mainly for
condition monitoring of electric motors, too. These systems usually include measuring
device connected with processing device, which can be connected permanently to data bus
supplying information for analyzing computer or data can be collected from device
occasionally. A selection between continuous data transfer and manually performed data
collection is made mainly by the costs of instrumentation and labour. Electric motors are
often considered to be very reliable, which means that investment not economically
justified.
On-line condition monitoring of components of electricity distribution network is
not commonly used. Protection relays include some functions for condition monitoring
such as self diagnostics of relay and counter of operations.

The applications described which are required to collect data from various sources,
for example from process automation, electricity grid and energy management system.
These systems contain data or are able to collect data to be used for condition monitoring
purposes. Process automation and energy management can provide energy, power, current
and temperature measurements of motors as well as measurement of output quantity of
drive, such as mass flow of pump. Electricity grid protection and measuring devices supply
quantitative and sometimes also qualitative information of voltage and current. Some
useful information of condition of components can be created just by collecting and
analyzing information available.
Database information is used in condition monitoring and condition planning of
network components as follows:
* Component data from the network database:
- Date of installation, model, and nominal life time
- Plan for service and replacement investments
* Operation counters and operation time of switches and disconnectors:
- Mechanical condition can be estimated
- Test instruction for unused disconnectors to prevent sticking
* Integrated lifetime (estimate of aging)
* Reliability analysis:
- Topology information and estimated reliability of components in a given load situation
* Analysis (reconstruction) of actual faults:

- Simulated network state using topology, load and voltage information of previous
situation.

Conclusion
Requirements of intelligent software applications for supporting the operation of
industrial distribution networks are different compared to the public distribution. The
domain is more segmented and heterogeneous, and the infrastructure of automation and
computer systems for electricity networks are not so sophisticated and advanced as other
process automation.
On the other hand the chance to apply intelligent software methods is promising
from the point of view of end-user attitudes, because the same kind of methods have been
successfully applied in process automation, e.g. in fuzzy control and system modeling
using neural networks. This paper discusses the requirements of intelligent methods in the
new domain, introduces the system environment and presents initial results gained in the
research work. Intelligent management will provide improvements inmany areas including
Reliability, System performance, loss reduction and load management.
The emergence of intelligent management is a promising step towards efficient
maintenance and complete automation.
BIBILIOGRAPHY

1) Jero.A,” Load modeling for distribution management function of industrial medium
Voltage distribution networks “, IEEE Transactions on Industry applications, Vol.32
No 4, January 2001.
2) Frank R. Goodman, Jr., Ph.D.” Advanced Distribution Automation”, www.epri.com.
3) Markku Kauppinen, Tampere University of Technology, Finland “Management of
electrical systems in industrial plants”, www.energyline.com .
4) Lijun Qin,”A new principle fro system protection in distribution networks”, IEEE
transactions on power delivery, Vol 10, No 4, June 2001.
5) Monclar F.R,” Intelligent support system for distribution network management “,
International conference on Intelligent system application to power systems “, Sweden,
June 2000.

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