Smart utility meters enhance utility operations
- Understand advanced metering infrastructure in electric metering.
- Evaluate early-detection options via data collection.
- Learn to work with local utilities to provide customers with improved electrical service.
The onset of the advanced metering infrastructure (AMI) era of electricity metering has brought about numerous changes to utility-industry operations. A primary driver for the deployment of AMI meters and systems is their ability to bring more granular data into integrated back office utility systems. This data-regarding outage events, consumption, voltage, and more-can then be processed by systems to accelerate power-restoration times, improve end-user energy monitoring, and strengthen infrastructure analytics.
Many preliminary rollout and integration efforts have focused on replacing existing metering and system-to-system functionality with the newer AMI technology. Utilities have been able to successfully implement AMI systems to replace their aging meter-reading systems with equivalent AMI functionality. However, the majority of utilities have taken a wait-and-see approach to the additional reporting and analytical capabilities that AMI and advanced systems integrations have to offer. While some of these additional capabilities are still conceptual and in development, there are other lower-hanging capabilities, many of which are functionally developed and readily available from AMI vendors. These low-hanging capabilities are ripe for the picking and ready to be put into operational use today.
One often overlooked capability is phase error event reporting, whereby the AMI meter reports specific event messages to the AMI head-end system when phase overvoltage, undervoltage, and/or outage conditions are detected by the AMI meter. While outage- and restoration-event messages are typical integration points for AMI deployments to improve reliability and operational efficiency, voltage sag- and swell-event messages are not typically of interest to utilities without an accompanying advanced distribution- automation program. However, both of these types of event messages are of particular relevance and interest with regard to transformer-rated meters.
Transformer-rated meters do not actively measure power flowing in series through a meter into the target load. Rather, they passively measure power through a target load using potential transformers and current transformers. Thus, issues within the metering-transformer circuitry can lead to inaccuracies in measurement without an actual issue present in the power-delivery circuitry. When transformer-rated power measurement is combined with the fact that the largest utility customers are metered using transformer-rated meters, it becomes evident that accurate measurement of power delivery to transformer-rated electric services is critical. Accuracy is of utmost importance for protecting utility revenue streams and avoiding preventable revenue loss.
Some examples of issues that can cause inaccuracies within the metering circuitry include faulty connections, connection failures due to extreme climates, corrosion, water leaks, damaged potential transformers or current transformers, and blown fuses. These issues can be transient in nature, as when water in an improperly sealed fuse freezes overnight but thaws out during the daytime. When these issues occur on electromechanical meters and even automatic meter reading (AMR) devices, they would have to be "caught in the act" during field investigations to be noticed or corrected. With electromechanical meters, these issues are not detected outside of the impact they have on the accumulated usage information. With AMR, at best, these issues could trigger an event that would be logged in the meter. However, these event messages would not necessarily be noticed until quite some time after they had manifested themselves. As a result, lost revenue is likely to occur.
Detection and analytics
The key to minimizing and preventing lost revenue is through early detection. Without daily event reporting and historical data trending offered by AMI meters, these issues could sit unnoticed for months or years until the next time the circuitry is serviced or investigated.
Prior to the adoption of AMI meters, most of these issues were not easily identifiable. Because the majority of these issues do not prevent the meter from accumulating usage information, and although the usage information is not accurate, meter readings still increase and do not necessarily present cause for investigation.
Most AMI meters and systems already are capable of bringing the data into utility systems, and many AMI meters and systems already include this data in default configurations. The key is to put effective reporting and analytics processes in place to turn the event data into useful information and work orders. Depending on the scale of the customer base and transformer-rated meter population, this analysis can begin manually and eventually become automated once useful data sets, processes, and integrations are developed.
The first step in performing this analysis is to configure the AMI meters and AMI head-end so that the meters can report phase errors and interval voltage data back to the head-end for logging and processing. Most AMI systems are capable of obtaining this data, and many are configured to do so by default. However, if the AMI meters and system are not already configured to obtain this data, then they can typically be configured by updating settings within the AMI head-end, and possibly by pushing an updated metrology configuration over the air (via the AMI network) to each target AMI meter.
Once the meters are configured to send the AMI events and interval voltage data to the AMI head-end, the next step is to choose a place to store the events messages for further analysis and processing. Many AMI head-ends will offer a screen within their user interface to specifically address phase errors. However, the event messages could also be sent outside of the AMI head-end via system-to-system integration to another utility system, such as a meter data-management system or asset-management system. The important detail here is that the information is stored in one location; less important are the specifics of the location where the information is stored.
Once the phase error events are reported into the system of record, it is time to begin looking at the associated voltage data. First, select a meter that has reported a phase error on a specific phase at a specific time, and then begin to parse through the interval voltage data at and around the time the phase error occurred. The voltage data then can be used to determine the nature of the phase error. For example, a persistent zero voltage on a phase is representative of a blown fuse in the metering circuitry on that phase. A voltage that is consistently greater than 10% below the nominal phase voltage, or a voltage that fluctuates frequently, is indicative of a poor connection or a water leak.
It is important to consider other common and explainable causes for the phase errors, such as outages caused by storms or equipment failures in the area of the meter and service point where the phase error occurred. These conditions are typically easy to detect, as they should cause a large group of outages and phase errors on service points in the area of the storm or equipment failure.
After some time and experience with this analysis, it will become easier to detect and determine the nature of the issue. Once the nature of the issue has been identified, it then must be determined whether a service order is required to investigate and repair the service. It is very important to keep a detailed service history of the issue and any corrective actions taken. These notes can be referenced in the future should the issue resurface or another issue presents itself at the same service point. For persistent phase errors, the service-order history information can be used to consider the past issues and their corrective actions and determine the ultimate root cause of the issue.
Due to the varying and unpredictable behavior of the measurement circuitry in the presence of these various issues, it is difficult to quantify the estimated savings of undertaking this analysis prior to actually performing the analysis. However, it is very simple to determine lost revenue and quantify the estimated savings of finding and correcting these issues once they are discovered. As an example, consider a meter detecting a voltage drop on the metering circuitry, a drop that is common for deteriorating connections. If a large 3-phase power customer-say, $1 million in annual electric sales revenue for a utility-has a 30% voltage drop on a single phase, then the utility could lose $100,000/year due to an issue as simple as a faulty connection in the metering circuitry. Worse yet, a blown fuse on a single phase in the metering circuitry could cost the utility $333,000/year.
Phase error analysis presents a useful and attainable mechanism for utilities to greatly improve revenue collection from larger customers. While phase error analysis does not absolutely prevent lost revenue, it does offer utilities the ability to detect and correct such revenue issues much more rapidly than with past metering technologies. This analysis can be especially useful in areas with extreme climates and seasonal climate swings, as these changes can negatively affect even the highest-quality electrical components, connections, and wiring. Additionally, phase error analysis has shown efficacy for utilities with service territories acquired through mergers and acquisitions. That’s because different utilities often use different wiring standards and practices, and detailed service histories for individual metering points are not necessarily preserved, especially during mergers and acquisitions.
No utility is immune to issues causing phase errors, but every utility with AMI is capable of performing this analysis to improve its operational efficiency and to preserve, protect, and enhance its revenue streams.
-Jesse Teas is a staff electrical engineer in the transmission and distribution group at Burns & McDonnell, where he works on telecommunications and network engineering projects including enterprisewide deployments of AMI and meter data-management systems.