This article looks at how to build a data concentrator for the smart grid that uses the incoming data to reduce reactive losses and handle diagnostics and fault detection. It looks at wireless technologies such as the CC1120 and how they interface with a controller optimized for smart energy designs such as the Sitara AM335x line of processors.
Smart meters are being deployed rapidly to meet the mandate that this technology reaches eighty percent of households by 2020 across Europe, and one of the key techniques to meet this target is to use a concentrator. The technical and economic challenge of every installed meter directly communicating with utility servers makes a one-on-one solution very difficult to implement, so having a concentrator is a key way to support a large installed base of automated metering infrastructure (AMI).
With automated meter reading (AMR) measurement, the communication of meter data to the central billing station will be seamless. In addition to collecting energy usage information for billing, utility providers can use the AMI to analyze faults in the metering network and find solutions remotely without having to send out an engineer.
Figure 1: The smart utility network.
The data concentrator is a critical node in the AMI, connecting several utility meters to a central utility server, simplifying the communication links of the energy service provider. These are used at several points in the infrastructure to securely aggregate data from a manageable number of meters and relay the information to the centralized utility servers.
The connection between the meters and the concentrator can be handled in several ways, but wireless is emerging as a cost effective and efficient way of linking multiple meters to a central aggregator. The frequency of the data fed back to the central servers ranges from an hourly feedback meter to real-time meters with a built-in two-way communication structure. These systems have the capability of recording and transmitting instantaneous information, providing more information on the load of the various end points that are actively consuming energy.
Figure 2: Building an Automated Metering Infrastructure (AMI) with wireless concentrators.
At the heart of any smart meter is the basic energy-measurement function. It is critical that utilities and consumers can rely on the accuracy, security, and reliability of this metering capability. Texas Instruments’
energy-measurement products are designed to meet all of the requirements for ANSI C12.20 and IEC 62053 accuracy for Class 0.2 and Class 0.5 meters, across the entire temperature range and a full 2000:1 dynamic input range.
The requirements for a separate metering host or applications processor vary by market and product. This is where the evolving Smart Grid requirements across the world significantly impact meter architectures. In some products, an inexpensive 16-bit MCU with 128 KB of Flash is suitable as a host, while other products can require a 32-bit MCU with 1 MB of Flash for more advanced metering functions or multiple communications stacks to support wireless links. The most advanced of today’s e-meters may use an embedded microprocessor that operates a high-level operating system such as Linux with multiple megabytes of memory on the board.
AMI networks require robust communications between the individual meters and the data concentrators, which aggregate meter data in a neighborhood area before sending that information to the utility’s central office through a backhaul link. AMI networks are either RF (mesh or star topology) or Powerline Communications (PLC). The choice between RF or PLC networks is usually driven by grid topology and geographical environment as these factors have enorm