A Glance at Smart Meters and their Underlying Wireless Technologies

by L-com

Introduction

The Internet of Things (IoT) phenomena has permeated nearly every aspect of life, exploiting every potential avenue to monitor, track, control devices/sensor nodes based upon real-time or near-real-time feedback. Energy monitoring is no exception to this trend where smart meters enable a centralized method to store and analyze the consumption of electrical, water, and gas energy. This data gathering opens doors for numerous smart grid applications from lowering barriers for sustainable/renewable energy to more ready implementation of decentralized electricity storage techniques (e.g., pumped hydro-storage, compressed air storage, batteries, capacitors, etc.). There are also some immediate benefits of a decrease in electricity consumption and even a decrease in electricity theft^[1]. However, smart meters rely on an underlying infrastructure involving technologies from power lines to existing wireless protocols. This article covers the technological advances found in electric meters and the various components and wireless protocols used to support the smart meter infrastructure known as the Advanced Metering Infrastructure (AMI). 

Demystifying Smart Metering Technologies

For as long as there has been a power grid supplying energy to residential homes and businesses, there have been meters monitoring the consumption of their electricity. Parameters such as voltage level, current, and power can track electrical usage, however, meters are often cross-functional by measuring natural gas and/or water uptake. 

A Look at the Traditional Smart Meter System

Traditional electromechanical meter systems are still widely used with an induction motor where the speed of a rotating disc within the meter is directly proportional to the voltage applied and current flow. In this type of meter, a register captures the number of turns taken by the disc and translates it to kilowatt-hours (kWh) through mechanical gearing to be read. Typically, these types of meters are not very accurate; the measurement is affected by a variety of factors such as mechanical imbalance, waveform distortions, or operational temperature. Most importantly, strictly electromechanical meters do not typically lend themselves to remote (wireless) readings and therefore must be read manually. For this reason, hybrid meters were leveraged where the number of disc revolutions is converted electrically and relayed to a centralized station via a wireless connection through an RF front-end (RFFE). This allows for bi-directional communications where the hybrid meter provides data to a centralized management system with a data concentrator and the respective signal processing via a communication infrastructure with the established wiring to support the wireless protocol used (Figure 1). 

Modern Smart Meter Systems

The smart meter signal chain transfers the voltage and current sensing data to the analog front-end with the respective analog-to-digital converters (ADC) to then be processed by a larger control unit that interfaces with the communications, user, and data storage for operational functionality (Figure 2). In order for these circuits to perform properly, a supply circuit and a real-time clock are needed to provide data with a specific date/time. 

Contemporary Smart Meter Sensor Technologies

More modern smart meters integrate either a shunt resistor, hall effect sensor, a current transformer, or a rogowski coil for voltage and current sensing, allowing for higher reliability and a longer operational lifetime due to the elimination of moving parts. These sensors leverage one of four fundamental principles to measure current^[2]: 

• Ohm’s law
• Faraday’s law 
• Magnetic field sensors
• Faraday Effect

A resistive shunt would act in series to a current-carrying conductor to allow some of the conductor current to pass through the resistive element where the voltage drop across the resistive element is proportional to the current flowing through it—shunting a known quantity of the current away from the conductor.

A Rogowski coil is constructed of multiple windings around a non-magnetic core material with a magnetic permeability close to that of air. Using Faraday’s law of induction, the induced voltage is proportional to the time derivative of the current in the conductor. Therefore, the real-time current can be deduced by time-integrating the signal from the Rogowski coil where an op-amp integrator circuit is used.  

Current transformers also exploit Faraday’s law of induction similar to the Rogowski coil, however, the core material is of a high magnetic permeability. The AC flowing through the primary winding concentrates the magnetic flux lines within the core, which in turn, induces a current within the secondary winding. The current within the secondary winding is directly proportional to the current within the primary winding, offering a measurement of the current flow. 

Hall effect sensors are magnetic field sensors that come in either an open-loop or closed-loop configuration. In essence, a hall effect sensor is based upon the Hall Effect—a phenomenon that occurs when a current flows through a strip of metal in the presence of a magnetic field, a voltage is generated perpendicular to both the current and the magnetic field. Open-loop current sensors are often the most simple and cost-effective technique, whereby a magnetic field sensor is placed in close vicinity to the primary, current-carrying conductor to measure current. Closed-loop sensors are typically more accurate but costly and would involve a compensation coil to cancel the magnetic field generated by the primary current. In both these cases, a hall effect sensor can be used as the magnetic field sensor. 

Backhaul Technologies for the Smart Grid

As soon as electrical meters could be read wirelessly, standardization approaches for the infrastructure to collect, monitor, and track this data were implemented as a smart grid application. The easiest version was known as Automatic Meter Reading (AMR), where data was routinely collected over various locations within a vicinity within monthly, daily, and even hourly intervals for one-way communication to the utility company. This evolved to Automated Meter Management (AMM), where two-way communication is enabled to provide data to consumer devices. The most recent iteration of these standards is known as the Advanced Meters Infrastructure (AMI), with all the previous listed qualities of AMR and AMM with the addition of performing technical measurements as well as customer services. 

The general architecture involves collection and dissemination of data from/to smart meters and relevant devices (e.g., in-home display, household appliances, etc.) every 15 minutes in quasi real-time through a number of platform/wireless protocols including the power transmission/distribution network (i.e.,  power-line communication (PLC), optical fiber) and wireless protocols such as GPRS, cellular networks, WiFi/WiMAX, Bluetooth^®, SATCOM, ZigBee, IEEE 802.16e, Low Power Wide Area Networks (LPWAN), 6LoWPAN and Z-wave. As shown in Figure 3, electrical/gas/water meters are read periodically at dispersed geographic locations from either residences or commercial/industrial facilities and relayed to the Data Concentrators (DCs) wirelessly or via the PLC. This data, in turn, is transmitted to a remote management center, or meter data management (MDM) system, that does the more complex signal processing and data analysis, ultimately lending itself to data mining and predictive analytics. The DCs are intermediate units between the meters and the centralized communication hub and can exchange information with all relevant electrical meters via mesh networking or even point-to-multipoint communications. Power lines that run through homes and offices and out to power plants are a potential platform for communication. While PLC is a cornerstone potential technology for smart grid backhaul, this article will focus more on the wireless communications aspect of smart meter data transfer. 

Commonly Leveraged Wireless Protocols

Smart metering communication standards can vary, connecting the various elements of a smart metering system (e.g., gateways, devices, etc.) through open or proprietary protocols in  Home Area Networks (HANs), Local Metrological Networks (LMNs), and Wide Area Networks (WANs). Table 1 lists some of the wireless protocols utilized for smart metering and their respective parameters. The European standard M-bus (EN 13757) is specifically designed for the remote reading of smart meters, with both uni-directional and bi-directional communications defined within the specification. There are three frequencies that are utilized in this standard, where the 169 MHz frequency is used for the “Narrowband” mode for longer range, 433 MHz is used in “F” mode for frequency bi-directional communications, and the 868 MHz frequency is used for all other modes including the “S” (stationary), “T” (frequent transmit), “C” (compact), and “R” (frequent receive) modes. 

Nearly a decade ago, WiMAX appeared as a popular choice for wireless connectivity for smart meters to potentially replace the increasingly congested, less secure, and limited range WiFi option as well as the, at the time nascent, LTE technology. WiMAX offers a more than adequate amount of throughput to support traffic demands of AMI and various smart grid applications with the benefit of a large range and wide coverage area. This made this protocol particularly applicable for wireless backhaul over vast distances, limiting the use of wired technologies and therefore installation costs. Short-range protocols such as Zigbee, Z-wave, 6LoWPAN, or Bluetooth are generally able to support a connectivity model in which a smart meter communications to an aggregation point/SMGW of some kind that has alternative wireless/wired options back to the MDM system, for instance, in HANs and Field Area Networks (FANs). 

More recently, Low Power Wide Area Networks (LPWANs) that utilize the sub-GHz bands, narrow bandwidths, and proprietary modulation schemes to achieve high transmission distances have emerged as a potential technological solution to meet the long-ranged wireless connectivity requirements for smart metering applications. For instance, the LoRa Alliance recently released a Device Language Message Specification (DLMS) communication profile for LPWAN technologies to specifically support smart metering use cases. The LPWAN protocols are generally geared toward the transmission of low payload sizes sporadically (asynchronous communications) to save on battery life by ensuring the device is in an energy-saving sleep mode. However, scheduled (synchronous) communications at timed intervals can be accomplished in these protocols depending upon the class/mode of the device. 

Antenna Solutions

Naturally, the choice of antenna would depend heavily upon the wireless protocol leveraged. More often than not, large omnidirectional antenna structures are used for SMGWs to support Line-of-Sight (LoS) and Non-LoS (NLoS) connectivity. Smart meters, however, can often utilize lower gain and structurally smaller antennas. The integration of a small form factor antenna, however, is not a major concern considering the static nature of a smart meter. Internal antennas are often used for smart meters, while external antennas can be mounted onto the cabinet for a NLoS connection. It should be noted, the sub-GHz frequencies are typically preferred for wireless smart meter applications as building penetration loss (BPL) decreases considerably with frequency—the BPL stands at 15 dB for the 2.4 GHz ISM-band, 12 dB for the 800/900 MHz bands, and goes down to 7.5 dB for the 169 MHz band found in Wireless M-bus^[3].  

While omnidirectional antennas are almost exclusively used in “mesh” communications found in Z-wave, Zigbee, and Bluetooth. The “star,” or point to multi-point (PtMP), topologies found in many of the protocols listed in Table 1 involve communications from multiple devices to a singular gateway where either omnidirectional (e.g., high-gain dipole) or directional antenna structures can be used, depending upon the layout of the nodes.  Point-to-point communications will leverage directional antennas for their focused radiation pattern to maximize the link budget (Equation 1) and minimize potential avenues for interference. Figure 4 depicts the respective applications and antenna types that can be used for PtP and PtMP applications.

Equation 1

Where P_RX is the received power, P_TX is the radiated power,  G_TX/RX is the gain of the transmitter and receiver antennas respectively, L_TX/RX are the losses experienced at the transmitter/receiver (e.g., installation losses, insertion loss of interconnect), L_FS is the free space path loss, and L_M are miscellaneous losses such as the proper fading margin (M_f) accounting for signal fluctuations such as shadowing as well as the building penetration loss (BPL). The type of antenna used for each smart meter application depends upon the power output of the transceiver, type of coax used, size of the antenna structure, distance over which transmission must occur, required throughput and terrain/obstacles the signal must propagate through. 

 Conclusion

Smart meters are critical to the smart grid infrastructure in order to readily track and assess the power consumption of residences, buildings, offices, and large industrial facilities. In the short term, this provides an accurate assessment of energy consumption as opposed to the previous method of monthly estimates with manual readings of electromechanical meters. The near real-time consumption information allows for direct feedback for additional energy-saving methods such as load balancing and shifting based upon energy demands. This, however, is not possible without the wired/wireless protocols and technology that enable smart metering. 

References 

1. Römer, B., Reichhart, P., Kranz, J., and Picot, A. (2012). “The role of smart metering and decentralized electricity storage for smart grids: The importance of positive externalities. “Energy Policy, 50, 486-495. doi:10.1016/j.enpol.2012.07.047

2. S. Ziegler, R. C. Woodward, H. H. Iu and L. J. Borle, “Current Sensing Techniques: A Review,” in IEEE Sensors Journal, vol. 9, no. 4, pp. 354-376, April 2009, doi: 10.1109/JSEN.2009.2013914.

3. M. Barbiroli, F. Fuschini, G. Tartarini and G. E. Corazza, “Smart Metering Wireless Networks at 169 MHz,” in IEEE Access, vol. 5, pp. 8357-8368, 2017, doi: 10.1109/ACCESS.2017.2694853.

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