2. 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.
• 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.
3. 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.
4. 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.
5. Application functions for distribution management in industrial
plants
* 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
6. 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
• For ADA to work, the various intelligent devices must be
interoperable both in the electric system architecture and in
the communication and control architecture.
8. DISTRIBUTION MANAGEMENT FUNCTIONS
• 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
9. APPLICATION FUNCTIONS OF DATA MANAGEMENT SYSTEMS
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.
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.
10. 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.
