Test Tools Help Identify and Eliminate Interference In Smart Grids

With demand for energy consumption projected to greatly increase, the utility industry is looking for ways to improve the efficiency, predictability and reliability of their networks.

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By Mark Heimbach

With demand for energy consumption projected to greatly increase, the utility industry is looking for ways to improve the efficiency, predictability and reliability of their networks. Smart grids are being developed as a means to service those requirements as well as to lay the foundation for future growth.

A large portion of the intelligence being incorporated into the smart grid is wireless, such as radios, sensors and concentrators. A challenge for utilities is the development of these two-way communication systems that can support a large range of applications and requirements. Figure 1 is an illustration of a smart grid ecosystem.

Currently, the spectrum of these wireless networks includes both licensed and unlicensed devices. In the U.S., the Federal Communications Commission (FCC) permits the operation of unlicensed devices in many bands. Important frequency bands for unlicensed devices include 902-928 MHz, 2.4 GHz, 3.65 GHz and 5 GHz. According to the Department of Energy (DOE), many wireless smart meters use the 900 MHz band.

Licensed networks, being owned and regulated, have the advantage of less interference compared to their unlicensed counterparts. Licensed frequencies, however, can be expensive. In addition, sources such as amateur radio stations or repeaters and consumer products might cause interference in licensed bands. Interference can also invade licensed bands though intermodulation distortion.

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Figure 1: A smart grid consists of several basic networks:
• Home area network (HAN)—Communications between a building’s interior and a smart meter,
• Field area network (FAN)—Communications between the user’s smart meter and a concentrator (or aggregator), and
• Wide area network (WAN)—A high-bandwidth backhaul communications link between the concentrator and the utility.

Unlicensed frequencies, while saving bandwidth costs, are accessed by a variety of different users. This can create many opportunities for interference, both in- and out-of-band. Nevertheless, given the potential cost savings, many utilities have chosen to use the unlicensed 900 MHz spectrum for smart grid deployment. Smart grids share this band with various other technologies, including 900 MHz cordless phones, amateur television and Integrated Digital Enhanced Network (iDEN). While still problematic, interference issues can be managed in a way that minimizes disruption to the network.

Cellular operators are also entering the smart grid space. Various advantages to deploying wireless services for smart grid over traditional consumer voice and data services include:

• M2M communication only (no voice, only data);
• No device subsidies needed to drive demand;
• Fixed communications devices;
• Minimal data requirements compared to traditional cellular service;
• No billing overhead for end users; and
• No customer care structure and overhead, no customer churn.

Wireless Networks in Smart Grids

The DOE also mentions that many communications and network technologies can be used for supporting the smart grid. These types of communications include cellular, satellite, microwave and WiMax, as well as short-range technologies such as WiFi, ZigBee and proprietary solutions.

These technologies are integrated into two wireless networks: access and backhaul. Access networks are typically found throughout the smart grid, connecting the end user to the backhaul networks. These networks are usually low power and consist of devices such as smart thermostats, local repeaters and smart meters. Located on the customer premises, smart meters are combined to form the automated metering infrastructure (AMI).

Backhaul networks are high capacity, bringing the data from the access networks to the utility control centers. Wireless backhaul includes WiMax, microwave systems and cellular networks.

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Figure 2: Map of indoor coverage measurements

Signal Coverage

As utilities deploy the smart grid using various wireless technologies, the need to assess signal coverage and interference sources will grow exponentially. Similar to cell phone carriers, utilities need to be able to analyze communication problems quickly and ensure their wireless network continues to run under a variety of conditions.

Utilities often operate in service areas containing a varying degree of population densities and topography. Signal propagation can vary considerably depending on geographic characteristics such as hills, buildings, valleys and trees. It can also vary according to time of day and changes in the environment. In addition, the network throughput capacity can be limited when large numbers of users attempt access in a certain time period.

In accessing the level of signal coverage for a given transmitter, the measured data volume needed makes manual coverage mapping difficult. Unless numerous measurements are made, areas of low-signal coverage can be easily overlooked. In addition, measurement levels must be accurately defined for the geographic coverage area so that sufficient margin can be built into the system. Signal levels, which may be marginal in optimal conditions, might prove to be unintelligible because of environmental changes such as rain or high humidity.

To reduce time spent verifying signal coverage, analysis criteria can be used to quickly define signal levels as good, fair or poor. This provides a high-level overview of signal quality, allowing the user to quickly identify trouble spots where more investigation or appropriate action might be needed—such as re-positioning transmitters or setting up repeaters to boost signal strength in poor coverage areas.

The use of a global positioning system (GPS) enhances the effectiveness of outdoor coverage mapping by providing fixed position coordinates for each measurement taken. Maps can be saved in Keyhole Markup Language (KML) format and analyzed later. KML files can be displayed in third-party programs such as Google Earth, where measurement data is overlayed onto geographical maps.

Coverage Areas

To plan and optimize wireless smart grid networks, utilities must identify their transmission coverage in a given geographical area. For indoor signal analysis in areas such as airports, train stations or office environments, coverage mapping allows the technician to easily identify coverage quality.

Figure 2 shows a map of indoor coverage measurements. This map shows various measurements, color-coded according to their relative signal strength. Precise signal level data is saved with each measurement point.

Outdoor coverage mapping enables the utility provider to optimize the network by identifying signal coverage in an outdoor environment. With a GPS, areas of relative signal strength can be automatically measured and precisely correlated with location. The analyzer can take automated measurements as a function of distance or time.

A map provides a high-level overview of signal coverage, using various colors indicating relative signal strength. Moving the mouse indicator over any particular data point automatically brings up a balloon window showing precise measurement levels, along with the settings at which those measurements were taken.

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Figure 3: Snapshot of a signal ID feature

Interference

Various tools are available to identify and locate sources of interference. The initial requirement is often to identify the frequency or frequencies of the interfering source. Using a handheld spectrum analyzer with a wide-band sweep, potential interferers can be identified by observing their frequencies and relative signal strength. Spectrum displays are often the best tool in determining the frequency of the interferer, while signal strength meters can assist with measuring the power level.

Once an interfering signal is measured, it can be helpful to have further information on the signal type being measured. An automated signal ID feature can display the signal’s center frequency and such parameters as the signal’s bandwidth; type, such as Global System for Mobile Communications (GSM), wireless local area network (WLAN), code division multiple access (CDMA), and so forth; number of carriers; and signal-to-noise (SNR) ratio. A snapshot of a signal ID feature is shown in Figure 3. This breakdown of the signal’s characteristics is done automatically by matching real-time measurements with previously stored identity profiles in the instrument.

Locating the Source of Interference

Once the interferer is discovered and identified, actions can be taken to locate the signal source. In many cases, the person who found the interference relied on paper maps and drawings to triangulate various measurements to locate the direction of interference and its approximate source location. With an interference analysis capability, this process has been completely automated. Using a directional antenna, the direction from which the interferer is transmitted can be found and automatically logged into the analyzer. Terrain maps are shown in the spectrum analyzer, along with directional information for each measurement taken.

The addition of a GPS provides latitudinal and longitudinal information for each measurement, showing the position of the interferer in precise GPS coordinates. This enables the user to perform fast in-field analysis without the need for post-processing at a later time.

Conclusion

As smart grid systems are deployed in an increasingly crowded wireless environment, it is imperative that utilities have the tools necessary to plan their systems, rapidly diagnose problems and maintain the security of their wireless network. Handheld analyzers are now designed to meet these smart grid measurement needs using innovative and user-friendly tools. These tools are crucial for network planning, optimization and maintenance of network infrastructure, saving the utility time and money.


About the author: Mark Heimbach is a product manager at Anritsu. He holds a Master of Science in Electrical Engineering from the University of Wisconsin-Madison and a Masters of Business Administration from the University of Dallas.

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