Voltage analytics
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Did you know that a drop of as little as a half volt at one meter as compared to the average of others on the transformer will expose a diversion or other problem? The challenge is in finding this drop in the utility’s transformer voltage before serious damage or injury transpires.

This article first appeared in ESI-Africa Edition 5, 2018. You can read the magazine's articles here or subscribe here to receive a print copy.

Like all utility infrastructures, the electric grid is at risk of fraud. Prior to the advent of smart meters, power diversion from the line side of the revenue meter went undetected during billing reads and even by meter readers during their monthly inspections. Diverted loads are often hidden behind walls or buried below ground where they can continue, unseen and unabated, with no secondary voltage protection. Any type of short on the utility side of the meter has the potential to arc and continue to burn, since there are no breakers for auto shut-off.

These problems can be solved by adding voltage analytics to existing smart grid management systems. By exposing voltage anomalies strongly associated with diverted loads and other types of faulty connections, this analytics approach can also reduce a utility’s liability exposure to the risk of ratepayer injury or damage to their connected electrical equipment. It also can improve grid integrity and service quality through better transformer voltage and load management.

Long-term, the commitment to ongoing surveillance of voltage dynamics will prevent the ticking time bomb of undetected meter bypasses after power thieves have vacated the premises. In these cases, the diverted load may have been removed, but the live wire connection to the ‘line side’ of the secondary service is left intact. This leaves the new owner or tenant vulnerable to the hazards of unprotected wires tied directly to the electric grid.

How voltage analytics stops theft

When the voltage drops at one meter as compared to the average of all meters on the same transformer, and there is no simultaneous increase in wattage, it is very likely that the meter is being bypassed or that there is some other problem (see Figure 1).

Figure 1: A drop of as little as a half volt at one meter as compared to the average of others on the transformer will expose a diversion or other problem.

The same voltage anomaly also can expose meters that have been mapped to the incorrect transformer, and those with faulty utility-side electrical connections. While each is important, fraud is the most expensive problem and poses the greatest safety risks.

Unlike software solutions that detect diversion based on usage patterns, voltage analytics compares the difference in the relationship between voltage and kWh in a properly functioning meter to that in a meter where there is a diversion or other problem. The technique was proven over the five-year period between 2012 and 2017 at one Californian utility, where its use revealed that 750 residential customers had diverted loads; i.e. approximately 0.7% of the ratepayer base. The average diverted load represented a billing loss of $5,000 per month, and the utility captured over $2.6 million per year in revenue loss from these diverted loads.

Figure 2: In this example, kWh information in red for the meter should be higher since the voltage is lower than others on the transformer.

Figure 2 shows a voltage-to-kWh comparison of one meter to others on the transformer, with the anomaly highlighted in yellow.  Analysts can use this information to drill deeper and determine how many amps are being diverted. It is not unusual for anomalies like these to appear only for a few hours at any given residence, before the meter returns to the earlier, average behaviour. This is the case in Figure 3, where the average voltage at the meter in question tracks along with all others on the transformer before briefly dropping below their average.

Figure 3: Sometimes, comparative voltage readings drop for only a brief period relative to the average of the other meters on the transformer.

Voltage analytics can be used to recover millions in unpaid past usage fees, ‘stop the bleeding’ of further revenue loss, and establish new, higher revenue streams. The technique leverages metering data to also deliver other valuable safety, service quality and grid integrity benefits that weren’t available even when meter readers made physical inspection visits.

Improved safety and service quality

Diverted wires with no secondary voltage protection pose the risk of fire or electrocution at the meter. Thieves work from inside the home or garage, cutting open the sheetrock and entering the back side of the meter panel to expose and then connect to the utility’s lineside service wires, using self-piercing compression lugs. These connections are most often made outside of any type of safety electrical box. Besides being dangerous, they mask the true kVA the associated transformer is serving. These large loads can cause transformers to overheat without distribution staff being aware, leading to unplanned outages during peak loading periods.

Large diverted loads and faulty secondary connections can also damage ratepayers’ connected electrical equipment. It is not uncommon for these larger diverted loads, such as those from marijuana growers in California, to cause extreme sags in voltage of up to six volts or more. These unmetered loads can run for 20 hours a day, or more, and the associated voltage sags will damage the utility side electrical connections to homes fed from the same service box. They can cause flickering lights and eventually lead to overheating and failure of inductive loads.

The biggest risk, though, is death or injury, and the most dangerous period is after the thieves vacate the premises. Diverted ‘hot’ wires are often left as they were, behind patched sheetrock, when the home is sold or rented to a new tenant. They are a threat to anyone who uncovers them. Because voltage analytics requires a diverted load to expose the bypass, it is critical that all diversions are found promptly, before service is stopped and the load is removed.

Optimising grid integrity and efficiency

Voltage analytics helps optimise the integrity and efficiency of existing grid infrastructure in two key ways:

1) improving transformer voltage; and

2) load management, and correcting mismapping of meters to transformers When a transformer has a large unmetered load, its duty cycle can run upwards of 95%.

When it has multiple unmetered loads, its duty cycle can run high enough to exceed its kVA rating. By helping to identify unmetered loads and alert utilities when transformers are at risk of exceeding their kVA limit, voltage analytics can help ensure more accurate transformer load management. Utilities can more effectively evaluate transformer duty cycles and loading, and pre-empt transformer overuse, overheating and unplanned outages.

Fixing meter-to-transformer mapping errors is also important. Identifying and correcting mapping errors is critical for transformer load management and will also un-mask diversions and other faulty connections, as shown in Figure 4. One utility used voltage analytics to find thousands of meter-to-transformer mapping errors.

Figure 4: The voltage and kWh for this transformer show what appears to be a mapping issue based on the distinct differences between Group 1 and Group 2.

Automating analytics

While a few utilities have been implementing voltage analytics for the past several years, they have been limited to using the approach manually. This has been very slow and time-consuming – mostly on a transformer-by-transformer basis rather than in any sort of holistic way across the entire infrastructure.

A better approach is to automate the process by building filtering and correlation algorithms that can be run across the extremely large data sets of a typical AMI system. These algorithms are based on roughly a dozen voltage interrelationships among and between meters and transformers that are the biggest indicators of safety or theft issues.

Once the analytics foundation has been built, data is gathered and analysed to identify which meters need additional, physical investigation. Analysts review the site investigation feedback and adapt algorithms as required. The same process is applied, section by section, across the entire grid with the goal of ‘touching’ each meter on a monthly, quarterly or semiannual basis.

Prerequisites for this process include the capability of the AMI system to read interval voltage and kWh. Keeping algorithms current requires close collaboration and a feedback loop with technicians who visit suspected theft sites and report findings. The correlations, exceptions and iterative queries all change continuously as meter/transformer interrelationships and diversion techniques evolve.

The first step is to rid the system of data irregularities that hide fraud. This includes reviewing how meters are mapped to transformers – correcting mapping errors will unmask energy diversion. Next, find and correct faulty connections, which can also mask energy diversions. Once the data has been cleaned, voltage analytics can find theft and other problems faster, based on much smaller anomalies.

As findings are incorporated into updated queries that steadily improve the speed and performance of voltage analytics the value and ROI of the process grows. Meanwhile, having voltage analytics in a utility’s smart grid technology toolkit will also deliver important secondary grid safety and integrity benefits, which are paid for through the revenue recovery made possible because of the technique’s high rate of success in identifying diversions. ESI

This article first appeared in ESI-Africa Edition 5, 2018. You can read the magazine's articles here or subscribe here to receive a print copy.

The article originally featured in our sister publication POWERGRID International, Issue 5 2018 and is republished with permission.

About the author

Gary Fromm is a senior consultant for smart meter analytics at APEX Data Consulting in the US. Fromm implemented the Modesto Irrigation District (MID) smart metering system and spearheaded the use of voltage analytics to find load diversions and connection problems. He was Western State Utility Theft Association (WSUTA) 2014 Revenue Protection Professional of the Year.