Electricity + Control August 2017

CONTROL SYSTEMS + AUTOMATION

Analytics

Customer Information Systems

Residential Meters

Meter Data Management (MDM) System

Head End System

Billing System

Industrial & Commercial Meters

MDM Systems & other Smart Grid Applications

AMI Systems

Utility Enterprise Applications

Figure 1: Smart Metering Architecture enabling Smart Grid.

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:

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-

kVA = (kW) 2 + (kVar) 2

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.

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 .

Electricity + Control

AUGUST 2017

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