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Electricity
+
Control
AUGUST 2017
CONTROL SYSTEMS + AUTOMATION
from AMI systems, through Validation, Estimation
and Editing (VEE), and billing preparation functions.
Thereafter the processed data can be transferred
to billing systems. MDM systems such as Ener-
gyIP by Siemens have the capability to integrate
to other Enterprise applications where AMI data
can add value, this may include grid applications,
customer operations applications etc [2].
Analytics is one of the applications which MDM
systems can integrate with by feeding it with val-
idated AMI data. Equipment (Transformer) Load
Management (ELM) is one of the functions that
can be implemented within the Analytics applica-
tion, allowing utilities to detect equipment loading
anomalies which may cause hazardous failures of
distribution equipment or transformers.
E(Transformer)LM
Transformers are the core of the power distribu-
tion grid and are built to last for years. However
grid conditions can change during the period of
their lifespan, causing issues that might shorten
the lifespan of the transformers or even cause out-
ages or fires when they fail. The common problem
resulting to this is transformer overloading. This
occurs when demand for power downstream of a
transformer frequently approaches or exceeds the
transformer maximum capacity. Over time, this
damages the transformer hence increasing chanc-
es of failure [1].
Traditionally utilities only knew when their over-
all system was overloaded and not down to equip-
ment level. Now with the introduction of Smart
Meters and analytics, utilities can spot transform-
ers which are experience overloading, to what de-
gree and predict when [1] failures may occur. With
analytics utilities can spot patterns and trends in
downstream loads being served by overloaded
transformers. Based on this information utilities
are able to be pro-active and react quickly by im-
plementing solutions to minimise the overloading
and prevent failure of the transformer, which could
result in hazardous fires, causing injuries, fatalities
and property damage and a great loss in the utili-
ties investments.
Methodology
As described before, transformer overload oc-
curs when demand for power downstream of a
transformer frequently approaches or exceeds
the transformer maximum capacity. The maxi-
mum transformer capacity describes the rating
of the transformer given in kVA. Now the load on
the transformer is compared to this rating to de-
termine if the transformer is loaded. The rating of
the transformer is compared to the power (kVA)
described below:
Where,
kVA
= Apparent Power, kW
= Real Power and
kVar
= Reactive
Power
Figure 2
is a line diagram of a portion
of a distribution network; in this dia-
gram we can see all the devices in
the network from Substation to the
metering device at the customer
metering points or Service Delivery
Points (SDPs). From this diagram
we can see that metering capability
is only at the substation transformer
and at the customer points and not
on the distribution transformers. To
get the load details on the distribution transform-
er, a virtual meter technique is employed. This will
aggregate the entire load from each of the cus-
tomer meters, resulting in the load on each of the
transformers as indicated in
Figure 2
.
Figure 1: Smart Metering Architecture enabling Smart Grid.
Utilities can unlock
further potential
of deployed AMI
systems, by analysing
the collected data
to acquire an
understanding of the
performance of the
distribution network
infrastructure.
Billing
System
Customer
Information
Systems
Meter Data
Management (MDM)
System
Analytics
Head End
System
Utility Enterprise Applications
MDM Systems & other
Smart Grid Applications
AMI Systems
Industrial & Commercial
Meters
Residential
Meters
kVA = (kW)
2
+ (kVar)
2