Over the past 15 years, the energy metering industry has been witnessing multiple waves of change, each with a scope and size not seen earlier for over a century. The transition from electromechanical to electronic meters was quickly followed by automatic meter reading (AMR) systems which in turn evolved into advanced metering infrastructure (AMI) systems associated with higher speed, two-way communication, and the ability to deliver large amounts of data to central databases for billing, troubleshooting, and analysis. Undeniably, these changes brought in business efficiencies ranging from drastic reduction in labour hours (for reading millions of meters), to improved access (from a utility key room), to worker safety (reduction in meter reader accidents – electrical shocks, dog bites), to better measurement accuracies and lower losses, to reduction in down time, and to lower environmental impact. But, have these changes ensured accountability of the entire electricity consumption?
Figure 1: Smart Meter with Health Monitoring – Closed View
Figure 2: Smart Meter with Health Monitoring – Open View
Increasing Demands from AMI systems
There is rising competitiveness in the electricity distribution industry along with increased regulations and a demand for customized services. All these are putting greater pressure on utilities to look beyond meters to cash and to manage assets cost-effectively, provide exceptional customer service and modernize their obsolete processes. This has given entry to the Internet of Things (IoT) that has promised amongst other things – translation of various sensor-based information into actions, new services, crew localization, part inventories, and control & maintenance of assets.
Despite the IoT promise, a chief engineer or a head of metering in an electrical utility is still left with unresolved inefficiencies around maintaining the health of millions of meters on a case-by-case basis, verifying accuracy using imperfect statistical methods, disrupting service for testing, replacing meters prematurely, or using age-based decision making. Most importantly, despite the advancements in IoT, the AMI network so far had been unable to do justice to its primary function, namely to make sure all electricity consumption is measured and accounted for.
How to get more value out of AMI systems
A quick market survey of all the metering systems available over the past few years, would lead us to believe that the measurement technology in electricity meters is commoditized. The specifications drafted by the different electrical utilities, too, have essentially remained the same over the past few years. The utility engineers have, therefore, been putting all their efforts into delivering value by analysing the available electricity data which they believe to be true and correct. However, when all electricity consumption is not being measured and accounted for, it makes little sense to pursue this approach.
What if we turn the tables and begin with an assumption that the electricity measurement data is suspect and incorrect. Is there a way to extract more value out of AMI systems and make the already smart meters even smarter? The answer is yes, and the way to achieve our goal is to upgrade the measurement with new diagnostics. For this, the IoT revolution gives us the clue – to upgrade sensors!
Intelligence based on Direct Measurements
Out-of-specification and faulty meters can be identified by an intelligent process working in real time called ‘in-meter health monitoring’. This technology that can non-intrusively monitor the health of deployed meters, including the accuracy of the current and voltage sensors, and better detect meter tampering can save significant cost for electrical utilities.
Once direct measurement of meter health is on your fingertips, it is easy to build intelligence around it. This will help make decisions quickly and with confidence by identifying out-of-specification meters, detecting meter malfunction, and confirming and quantifying more tamper events. Utility companies will then be able to more effectively dispatch field crews, optimize meter replacement, and reduce tamper investigation time.
The advantages of in-meter health monitoring are:
- Always On: Detect problems quickly
2. Meter Specific: Locate drift or tampering
3. Direct Measurement: High confidence results
4. Non-invasive: No service interruption
Figure 3: Self-Testing Capability of the Measurement Front End including the Sensor
Figure 4: Block Diagram of the ADE9153B Chip
Metering IC with Sensor Monitoring
The recently launched chip ADE9153B from Analog Devices is an accurate, single-phase, energy metering IC with (a) sensor monitoring and (b) autocalibration. Sensor monitoring is achieved with their patented mSure® technology that allows (i) meter health monitoring and (ii) advanced tamper detection.
As can be observed from Figure 3, the mSure technology resides in the energy measurement IC, and it provides accurate monitoring and self-testing capability for the entire system, including the sensor.
The monitoring feature allows the user to check the overall accuracy of the sensor and signal path to identify accuracy drifts that occur over time on the current and voltage channels, independently.
Similarly, mSure offers advanced tamper detection with the ability to detect unusual changes on the sensors. mSure runs in parallel to the metering measurements, allowing uninterrupted and unaffected metrology in the chip.
Autocalibration with mSure enables a meter to automatically calibrate the current and voltage channels without accurate reference meters or accurate sources when a shunt resistor is used as the current sensor. The autocalibration feature supports Class 1 and Class 2 meters.
The ADE9153B chip includes three high performance analog-to-digital converters (ADCs) (see Figure 4), providing an 86 dB signal-to-noise ratio (SNR). It offers accurate measurement of line voltage and current, calculates active, fundamental reactive, and apparent energy, as well as rms. A wide range of power quality information is included, such as dip and swell detection. Current Channel A is ideal for shunts, with a flexible gain stage providing full-scale input ranges from 62.5 mV peak down to 26.04 mV peak. Current Channel B has gain stages 1×, 2×, and 4× for use with current transformers (CTs). A high speed, 10 MHz, serial peripheral interface (SPI) port allows access to the chip’s registers.
Figure 5: Health Monitoring
Figure 6: Bath tub Curve of Failures over time
- For single phase energy meter: Real time meter health monitoring using mSure manager; Class 0.5 accuracy; Line and Neutral measurements; Imax = 60A, Inominal = 6A; 110V-240V; 50/60Hz; LCD and CF outputs
• Voltage RMS, Current RMS, Active, Fundamental Reactive and Apparent Power and Energy measurements
• Power quality measurements: Dip, Swell; Frequency, Power Factor
• PC control using an isolated RS485 port; PC software for full control of the chip
Sensor Monitoring Features
- mSure sensor monitoring
– Non-invasive, real-time, direct, precision measurement of the input signal path
– Detects changes in meter accuracy and amount of drift over the life of the meter
– Identifies sensor malfunction
– Enables advanced tamper detection methods
– Companion MCU firmware to facilitate diagnostic data reporting
– Supported for shunts on the phase line and CTs on the neutral line
• mSure autocalibration
– Automatic calibration based on a direct measurement of the full signal path
– Calibration procedure not requiring a reference meter
• mSure Manager for analyzing and reporting mSure diagnostic and health data
Figure 7. Mean and Standard Deviation of errors in a Meter Polulation
Figure 8: Need for a new approach to monitor Meter Health
Monitoring Meter Health
It is estimated that there will be 780 million smart electricity meters worldwide by the year 2020. With such a large population of meters, one of the bigger challenges is to overcome the limited visibility into the health of these meters. While smart meters and AMI remove the need for in-field meter readers, expensive crews still need to be scheduled to replace meters that are nearing the end of their useful lives.
Estimating Meter Life
Historically, a lot of attention was devoted to reducing the impact of Early Life failures. This is accomplished by improving the manufacturing process, environmental burn-in, and extensive testing. The Wear Out region of the curve, which typically has a Gaussian distribution, is avoided by using conservative (three or more standard deviations) statistical methods to minimize the possibility of an out-of-spec device remaining in service. Furthermore, sample testing is required in many global regions to spot check meter performance during deployment.
A technique usually adopted by the electric utilities to verify the meter accuracy is to perform sample testing. Regulatory compliance requires checking the accuracy of up to 0.5% of all installed meters twice during a meter’s lifetime. A utility with 10 million meters installed could easily incur a cost of ` 180 crores over the lifetime (15 years) of the population. A cost per customer of ` 900 assumes removing the meter under test, installing a new one, performing the test in a lab, and then installing the original meter for another customer. In-field accuracy verification can prove even costlier and inconvenience customers.
Current best practices require that utilities determine useful life of the meters based on statistical distribution of failures. Typically, this is done by reliability engineers using the Weibull function, otherwise known as the bath tub curve depicted in Figure 6. Reliability engineers use these techniques to ensure that a meter’s measurement accuracy remains within class before it is replaced.
Based on the performance of the samples picked up at random from the field, a decision is taken, either to replace the entire population or to continue with them for a pre-defined period. Such an approach gives rise to a possibility of leaving hundreds of bad meters in the field (see Figure 7).
If 1% of the meters are found to be defective during the sample testing, and based on it a decision is taken by the utility to replace the entire installed base, then too, the utility would be bearing avoidable expenses since 99% of the meters getting thrown out are still good and within its useful life.
Another common method for determining the time to replace meters is based on the length of service. Meters like other electronic devices follow a bath tub shaped failure curve (see Figure 6). For this method to be effective, the meter must be replaced before the first meter is expected to fail. Again, almost every meter that is thrown into the trash still functions.
However, the biggest drawback of all the above described methods is that often well over 99% of meters removed from operation still perform within specifications. There has never been a cost-effective way to verify the measurement accuracy of each device.
Being able to precisely and cost effectively check meter accuracy helps enable utility companies to make better replacement decisions. If we assume a ` 5000 cost for the new meter and its installation, and by achieving a 2-year extension to a 15-year meter lifetime, over ` 500 can be saved per meter over its lifetime.
Risk of Damage to Reputation
While slow running meters represent a loss to the utilities, meters running fast overcharge consumers and could result in an embarrassing situation for a utility. Many PR departments and meter managers have horror stories about overbilled customers who have turned to social media to express their dissatisfaction. The utility needs to subsequently check every meter in the batch and replace them at a high expense.
Clearly, we need a new approach to monitor meter health, collect data, analyse them and generate health reports. Any change in meter accuracy should get detected quickly and remedied before it becomes a larger problem. For this a meter should be in a position to perform background accuracy check multiple times a day and send an alert as soon as it notices any abnormality or change in meter accuracy.
An Edge-to-Cloud Utility Meter Analytics Solution
The mSure diagnostics technology enables direct and non-invasive monitoring of electric meter accuracy and faults in real-time. Such technology enabled meters, combined with an ‘Edge-to-Cloud’ analytics service, provide utility companies with real-time health data and actionable insights on meter accuracy over lifetime, meter malfunction, and advanced tamper detection.
Edge computing is a method of optimizing cloud computing systems by performing data processing at the edge of the network, near the source of the data. This reduces the communications bandwidth needed between sensors and the central data center by performing analytics and knowledge generation at or near the sensor – the source of the data. This approach requires leveraging sensors that may not be continuously connected to a network. Analog Devices, has created an analytics solution featuring a real-time diagnostic technology—mSure®—residing in the individual utility meter that reports meter health data to an analytics service. The resulting actionable insights provide utilities with a direct ability to protect revenue better, manage field resources more effectively, reduce equipment costs, and improve customer service.
This non-invasive diagnostics technology monitors sensors for accuracy and faults. In utility meters, it is built into the new Analog Devices’ energy measurement IC and it delivers meter health data. An mSure-enabled meter has the ability to check the accuracy of the metrology function directly and in real-time. The cloud based analytics service combines various data sources and data history to provide actionable insights via advanced meter health and revenue protection modules. Such insights to detect tamper and out-of-spec meters help protect the utilities revenue and manage their equipment cost-effectively.
Figure 9: Tamper Detection without actionable intelligence
Figure 10: Sensor based Advanced Tamper Detection
Figure 11: Comparison of Errors in Meters with Open Loop vs. Control Loop (a) Open Loop (existing meters)
Figure 11: Comparison of Errors in Meters with Open Loop vs. Control Loop (b) Servo Control Loop – Monitors and Reports Measurement Accuracy
Advanced Revenue Protection
Although often perceived as a developing nation issue, the problem of energy theft is widespread and impacting every geographical region, including India. It is estimated that electricity worth over $96 billion per year is stolen worldwide. Creative thieves use a variety of methods to siphon energy including direct line tapping, magnetic interference, and bypassing the electricity meter. Given the size of the problem, a variety of methods have been developed to detect theft attempts and inform the energy supplier so that appropriate action can be taken. So far, results have been unsatisfactory and energy theft continues to rise. How can we reverse this trend?
Existing Tamper Detection Methods
The root cause of the problem is that each tamper detection method has weaknesses. The insights and alerts generated are prone to error, leading to a lack of trust in the solution. They provide interesting views on the problem, but they don’t provide real-time actionable intelligence.
Recent innovations have taken a more holistic, grid intelligence or network based approach. Energy consumption is measured at multiple points in the energy distribution chain, results are compared, and any differences are attributed to technical or non-technical (i.e. theft) loses. Such solutions show promise, but the granularity of the results is wide-ranging. It is simply not economical to measure consumption at all the network points needed to profile a theft to a specific end node.
The most pervasive method in use today is pattern-based analytics with machine learning to identify anomalies and profile tamper candidates. Meter-based historical and neighbour data are combined with other sources and mined for patterns that deviate from an expected norm. Anomalies can be priority ranked, and in theory, offenders caught. In practice though, this method tends to deliver an amount of false positives, (i.e. results that are profiled as tampers but are actually not). For example, a homeowner goes on extended work assignment leaving the property unoccupied for a few months. Power consumption drops and a tamper candidate alert is triggered, leading the energy provider to initiate an erroneous investigation with a resulting waste of resources, frustrated homeowner, and damaged reputation. Another problem is that, by definition, the analysis relies on historical data and lags the actual theft (thief can’t be caught red handed).
Another common method of tamper detection is meter-hardware protection. Basic meters contain built-in detectors that are tripped by certain kinds of tamper attempts and then alert the energy supplier. Anecdotal feedback from utilities deploying these detectors indicates that, generally, such systems are over-sensitive and also prone to the false positive problem. In short, the alerts cannot be acted upon because in a high number of cases the alerts are triggered innocently.
All existing methods also suffer from one core flaw. While they can, to a greater or lesser extent, point to a potential tamper, they cannot reliably indicate the amount of energy stolen.
Figure 12: Aging – Out of Spec Meters
Figure 13: Sensor Fault – Damaged Meter
New Advanced Tamper Detection Method
A new approach is needed, that provides on-meter, continuous real-time monitoring with an associated analytics capability that can profile, quantifies, and alert energy suppliers to tamper attempts. This approach must deliver consistent and reliable results that allow action to be taken with high confidence. That is where mSure® comes in. mSure is an agent that resides in the smart meter and monitors what happens at the sensor used to detect energy consumption. Any change to the characteristics of the sensor that would be induced by an attempt to bypass or saturate the meter can be immediately detected. That enables mSure to send the energy provider a tamper alert and/or to activate a visual flag at the meter, which can act as a deterrent to potential tampering. As the impact of various direct tamper methods on the sensor can be profiled, the type of tamper can be recognized with high confidence and the number of false positives significantly reduced. In addition, by understanding and analyzing the change in characteristics, an estimate can be made of the amount of energy stolen, not just that a tamper event has occurred.
Unlike other methods for tamper detection, the mSure technology is based on an always-on direct measurement of meter sensor functionality (see Figure 10) and not statistical or subjective factors, which enables it to:
- Deliver high confidence results with fewer false positives
• Detect new types of tampering never detected before
• Specify tampering to an individual meter
• Detect intermittent tampering (for example, during times when meter crews are off duty)
• Estimate the amount of tampering, which helps prioritize tampering investigations
The financial benefits of mSure-enabled revenue protection will vary by utility, depending on the amount of tamper, the types of tamper, and the ability to pursue enforcement. For a 10 million meter utility with a 5% nontechnical revenue loss rate, tamper is a ` 900 Crores issue. Assuming a 15% improvement in recovery, an mSure-enabled meter can save a utility over is ` 1350 per meter over the meter’s 15-year lifetime.
Figure 14: Sensor Fault – Overcurrent Damage
Figure 15: Bypass Tamper
Closed Loop Error Monitoring
Figure 11 describes the disadvantage of the existing meters that have an open loop sensor measurement circuit. The total error is the product of the sensor error and the error of the measurement front end. The sensor is a major contributor to the error and open loop architecture cannot correct such an error over its lifetime.
On the other hand, a meter that incorporates sensor within its monitoring loop (closed loop) is able to detect such errors and re-calibrate or remove ones that can be corrected. mSure has the ability to inject signals into the sensor to detect and remove sensor errors.
Table 1 shows the fault detection capability of a meter that incorporates a closed sensor loop as implemented by mSure. Meter 1 flags an ‘Out of Specs’ Alert since the accuracy has dropped due to aging.
Meter 2 indicates an error due to failure of Sensor (see Figure 12). This represents a damaged meter where there is no sensor output.
Meter 3 flags an error since the accuracy is outside limits due to a Sensor fault that is caused due to overcurrent damage (see Figure 14).
Meter 4 flags an error since the accuracy has fallen outside limits, but this time due to tamper of the sensor. In this tamper mode, the meter (sensor) is bypassed externally to slow down the meter (see Figure 15)
Thus, we have been able to demonstrate how energy theft can be combatted with actionable intelligence.
As with all new technologies, phasing-in real-time accuracy monitoring technology is a prudent approach. Existing field sampling protocols can be used to confirm effectiveness of the solution and to train predictive analytics. After a couple of years of data correlation, field sampling can be reduced or eliminated, realizing additional cost savings (see Figure 16).
Figure 16: Health Monitoring and Actionable Intelligence
Contrary to the belief of many that electricity metering has become ‘commoditized’, each wave of change that has hit this industry has caused significant improvements in operational efficiency and provided good return on investment. It is refreshing to note that in the current wave, the last mission-critical parameter – ‘energy measurement accuracy’ has been effectively monitored.
With the deployment of non-invasive, real-time accuracy monitoring technologies, smart meters can deliver added value and increase a utility’s return on investment. Smart meters with in-meter sensor based diagnostics coupled with analytics services enable multiple improvements, including eliminating meter accuracy verification, extending meter lifetime, reducing risk, and adding advanced revenue protection. With a meter’s lifetime so extended that it be replaced only just before its decline in accuracy, utility companies need to seriously consider usage of such next-generation meters in their new deployments to boost their return on investment.
While a fool-proof revenue protection solution will likely embrace a combination of methods, the in-meter sensor based diagnostics coupled with analytics services provides the missing piece of the puzzle: a meter-level real-time tamper detection capability that can be acted on with confidence. In short, actionable intelligence. With this we are now getting close to achieving AMI network’s primary function, namely to make the entire electricity consumption accountable!
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