Transformer ConnectivityElectric meters are connected to secondary distribution transformers to reduce distribution high voltage to safe household levels. There are many transformers of different capacities along distribution lines, both underground and overhead. The choice of which transformer a meter is connected to is dictated by physical proximity as well as by optimal loading of transformer capacity. This connection between an individual meter and a distribution transformer is known as transformer connectivity and is part of the Itron Grid Connectivity offering.

WHY IS ACCURATE KNOWLEDGEOF TRANSFORMER CONNECTIVITYSO IMPORTANT?

Power outages, whether caused by extreme weather or by local accidents, require a timely remedy by utility crews.However, repairing distribution infrastructure following an outage canalso result in the reconnection of some meters to a transformer other than theone originally connected. There are situations, especially during power emergencies, where meter connectionsare rerouted without the utility’s records ever being updated accordingly.

Over time, utility records and physical reality may diverge suffi ciently such that it becomes diffi cult to manage the distribution network. Transformers may

Finally, outage detection and reporting systems rely on accurate knowledge of transformer connectivity. In today’s communication networks, only a subset of the Power-Off-Notifi cations (PONs) sent by all smart meters affected by an outage are “heard” by the head end. To accurately and quickly determine the true extent and identifi cation of all customers affected by an outage requires precise connectivity information for each and every transformer via the Transformer Connectivity service.

blow up without warning because of overloading due to incorrect data on record. Meanwhile, other transformers may be left inadvertently oversized with fewer meters than the original design specifi ed. This causes unnecessary waste of equipment capacity and power.

Theft detection strategies, based on comparing voltages of all meters connected to the same transformer, fail because knowledge of connectivity is faulty. Similarly, detecting high impedance connections – a threat to customer safety – is impaired by the lack of reliable dataon connectivity.

Itron Grid Connectivity

PRODUCT

Transformer ConnectivityValidation Algorithm

Traditionally, verifying transformer connectivity has required either visual tracing of overhead lines or sendingand receiving electrical signals overthe wire. All traditional methods require considerable human resources.

Itron has developed an algorithm (patent pending) for the purpose of validating and correcting the utility’s meter-to-transformer connectivity data. This algorithm uses the normally occurring voltage fl uctuations at each meter as a signal to identify “friend vs. stranger,” in a manner of speaking. The voltage monitored by every meter changes as a consequence of one ofthree types of events:

» Turning on or off a load at the premise that the meter monitors, e.g. turning on an electric oven, which causes a slight decrease in premise voltage

» Turning on or off a load at a neighboring premise – if an electric oven is turned on there, the slight decrease in voltageis felt by all meters connected to the same transformer

» Switching a device on the primary side of the transformer, e.g. a capacitor bank switching in or out, or a voltage regulator adjusting primary voltage

The meter-to-transformer connectivity method relies on correlating fi ve-minute time series of voltage changes between any two meters within a user-settable limit, typically 1,000 to 3,000 feet. The correlation is done on a daily basis for a minimum of seven days, and the results evaluated to either confi rm or reject the identity of each meter’s parent transformer. Where such identity is rejected, an alternate identity is indicated that is the more likely parent transformer. Itron’s algorithm exploits the fact that a voltage change caused by switching a load at a premise is “felt” by most meters that are connected to the same transformer. Therefore, by correlating each meter’s voltage changes

Voltage Changes of Two Meters on Same Transformer Show Strong Correlation – Affi nity is 0.95

Transformer Connectivity is Essential for:

» Theft Detection

» High Impedance/Safety Issues

» Outage Detection

» Transformer Load Management

Voltage Changes of Two Meters on Different Transformers Hardly Correlate – Affi nity is 0.15

to those of all other meters that are within a reasonable radius (e.g. 1,000 feet), one can distinguish meters on the same transformer that show the same, simultaneous voltage change, from meters on other transformers that do not show any simultaneous change. Because of the timing imprecision inherent to interval data, the identifi cation of actual connectivity from the Transformer Connectivity service requires collecting and correlating a suffi ciently large number of voltage changes so as to be able to use statistics-based approaches to minimize error. This method uses a quantitative measure, “Affi nity”, which characterizes the degree to which two meters experience the same local voltage changes. The degree to which Affi nity between any two meters varies helps in determining meter-to-transformer connectivity.

The orange curve in this fi gure shows the distribution of Affi nities between meters connected to different transformers. The blue curve in this fi gure shows the distribution of Affi nities between meters

Transformer Connectivity Allows Utilities to:

» Provide end-to-end monitoring and communication to enable tools to analyze each line segment from meter to feeder for revenue assurance and theft detection

» Utilize fi eld asset management and transformer monitoring to gain valuable insight into distribution systems

» Detect power theft and high-impedance connections bycomparing meter voltage tothat of neighboring meters

» Find misplaced or misfi led meters

on the same transformer. The number of combinations is shown on the left or right vertical axis respectively, and the Affi nity for each combination is shown on the x-axis.

Note the nearly complete separation of Affi nity ranges between “same-transformer” and “different transformer” meter pairs. For each individual meter, the analytic process calculates affi nities with every other meter that utility records indicate is on the same transformer. The highest such affi nity we call “Home Affi nity.”

For the same meter, affi nities are then calculated with every meter not on the same transformer, within a confi gurable distance, e.g. 1,000 to 3,000 feet. The highest such Affi nity we call “Away Affi nity.”

If Home Affi nity is higher than Away Affi nity, the utility-supplied connectivity data for the subject meter is validated as correct. If the Away Affi nity is greater than theHome Affi nity, the utility’s connectivitydata is likely wrong for this meter. Furthermore, the transformer to which the

Distribution of Affi nities between Meters Connected to Same vs. Different Transformers

Home vs. Away Affi nity of 597 Service Points

meter with the highest Affi nity is connected indicates the most likely correct transformer for the subject meter.

This fi gure shows a sample of 597 meters whose Home Affi nity is shown on the Y-axis and the Away affi nity on the X-Axis. All meters in the top left half of the fi gure have correct connectivity as stated, while the 10% of meters in the bottom right half are clearly misconnected. For any one meter in this group, its Away Affi nity on the x-axis points to the correct home for the subject meter.

A more advanced and more accurate version of the same algorithm will be implemented as a distributed application in Itron’s OpenWay RivaTM meters. The principal advantage is the one-second time resolution at the meter itself and the ability of Riva meters to communicate with their neighbors and calculate bilateral Affi nity locally. Thus, instead of uploading and analyzing large amounts of data in the back offi ce, connectivity assessment can be done in real-time at the edge of the distribution system, and the back offi ce notifi ed of any connectivity changes within minutes of their occurrence.

SUMMARY

Accurate meter-to-transformer connectivity is essential to enable a truly smart grid. Performing analytics on smart meter data without accurate and timely knowledge of Meter-to-transformer connectivity is like fi nding your way with an out-of-date map.

The new, patent-pending algorithm by Itron adds the “GPS” to smart grid analytics by accurately and timely tracking of meter location in relation to the transformers by which they are served.

While Itron strives to make the content of its marketing materials as timely and accurate as possible, Itron makes no claims, promises, or guarantees about the accuracy, completeness, or adequacy of, and expressly disclaims liability for errors and omissions in, such materials. No warranty of any kind, implied, expressed, or statutory, including but not limited to the warranties of non-infringement ofthird party rights, title, merchantability, and fi tness for a particular purpose, is given with respect to the content of these marketing materials. ITRON is a registered trademark of Itron, Inc. © Copyright 2016 Itron. All rights reserved. 101529MP-01 1/17

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