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Provides for time scales to be measured terms of
“time periods”. This will greater flexibility and benefit users who do not record their maintenance or reliability
over one calendar year or over a 12-month period. |
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Take account of the number of faults occurring on
each line per time period. This can make use of historical data which has been recorded for a particular time
period, grouped data to find an average over a number of time periods or projected values from activity curves to
give the predicted number of faults, which will occur on a specific line, for a time period in the future. |
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Calculate the faults per time period for loads,
transformers and shunts. Again, the data can be historic, an average or predicted and the number of faults per
time period |
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Allow the user to select whether switching
will restore loads or whether the fault must be repaired for supply to be restored. These two different methods
of restoring supply will have two different associated supply restoration times which will be stored in the data
for the protection device. |
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Calculate a number of “reliability indices”.
These indices are calculated from formulae that typically include the number of interruptions, the duration of
interruptions and the number of customers affected. There are over ten of these reliability indices. The
Reliability module will calculate the four most common of these indices, which are:
o System average interruption frequency index (SAIFI).
o System average interruption duration index (SAIDI).
o Customer average interruption duration index (CAIDI).
o Average service availability index (ASAI). |
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Historical or predicted indices can be
calculated depending on the data available. Actual records can be used to calculate indices for previous
time periods, or predicted faults on the network can be used to calculate indices for future time periods.
A description for the type of information may be entered and later displayed with the results. There are
several advantages of entering data for different time periods:
o Using historical data for different time periods can build up a picture of chronological
changes to the network to identify weak areas.
o Existing indices can be established which can act as a guide for acceptable levels in the future.
o Previous predictions can be compared with actual performance. This will give a true gauge of the accuracy
of the simulated results.
o Use of predicted data in conjunction with maintenance schedules can be used to re-plan maintenance
activities and effect cost savings.
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As mentioned above, the loads will
contain information regarding how their supply will be restored i.e. by switching or repair. It is the intention
that the circuit will be configured to calculate indices for specific situations. Further to this, indices will
be calculated by assuming that all the loads will be restored by switching (best case scenario) and all the loads
will be restored by repair (worst case scenario). |
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The results of the indices will be
available for both individual protection zones and the entire network, i.e. overall indices for all the
protection zones in the network. |
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Allow the identification of areas of the
network that require more protection and suggest areas where devices should be added. This will be in the
form of a report and the actual network will remain unchanged. The report will identify the zone with the
worst reliability indices and suggest a node in this zone where a further protection device should be placed
in order to create a new zone containing 30% of the load for the original zone. The figure of 30% will be set
as a system setting so that it may be changed. |
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Enable users to determine the impact of
embedded generation on your network |
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The ability to define the cost to the consumer
of loss of supply becomes possible through use of the Reliability module. |
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Enables you to assess the impact of
distribution loss of supply on the energy supply (retail) companies, who are subsequently unable to
deliver energy they have purchased. |
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The reliability module is API enabled. This
will enable users to “drive” the module remotely or automatically as part of a reliability sequence by means
of entering data into DINIS, running the analysis and generating the output results.
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