Budgeting Power in Data Centers

In my May 2014 Circuit Cellar article, “Data Centers in the Smart Grid” (Issue 286), I discussed the growing data center energy challenge and a novel potential solution that modulates data center power consumption based on the requests from the electricity provider. In the same article, I elaborated on how the data centers can provide “regulation service reserves” by tracking a dynamic power regulation signal broadcast by the independent service operator (ISO).

Demand-side provision of regulation service reserves is one of the ways of providing capacity reserves that are picking up traction in US energy markets. Frequency control reserves and operating reserves are other examples. These reserves are similar to each other in the sense that the demand-side, such as a data center, modulates its power consumption in reaction to local measurements and/or to signals broadcast by the ISO. The time-scale of modulation, however, differs depending on the reserves: modulation can be done in real time, every few seconds, or every few minutes.

In addition to the emerging mechanisms of providing capacity reserves in the grid, there are several other options for a data center to manage its electricity cost. For example, the data center operators can negotiate electricity pricing with the ISO such that the electricity cost is lower when the data center consumes power below a given peak value. In this scenario, the electricity cost is significantly higher if the center exceeds the given limit. “Peak shaving,” therefore, refers to actively controlling the peak power consumption using data center power-capping mechanisms. Other mechanisms of cost and capacity management include load shedding, referring to temporary load reduction in a data center, load shifting, which delays executing loads to a future time, and migration of a subset of loads to other facilities, if such an option is available.

All these aforementioned mechanisms require the data center to be able to dynamically cap its power within a tolerable error margin. Even in absence of advanced cost management strategies, a data center generally needs to operate under a predetermined maximum power consumption level as the electricity distribution infrastructure of the data center needs to be built accordingly.

This article appears in Circuit Cellar 292.

Most data centers today run a diverse set of workloads (applications) at a given time. Therefore, an interesting sub-problem of the power capping problem is how to distribute a given total power cap efficiently among the computational, cooling, and other components in a data center. For example, if there are two types of applications running in a data center, should one give equal power caps to the servers running each of these applications, or should one favor one of the applications?

Even when the loads have the same level of urgency or priority, designating equal power to different types of loads does not always lead to efficient operation. This is because the power-performance trade-offs of applications vary significantly. One application may meet user quality-of-service (QoS) expectations or service level agreements (SLAs) while consuming less power compared to another application.

Another reason that makes the budgeting problem interesting is the temperature and cooling related heterogeneity among the servers in a data center. Even when servers in a data center are all of the same kind (which is rarely the case), their physical location in the data center, the heat recirculation effects (which refer to some of the heat output of servers being recirculated back into the center and affecting the thermal dynamics), and the heat transfer among the servers create differences in temperatures and cooling efficiencies of servers. Thus, while budgeting, one may want to dedicate larger power caps to servers that are more cooling-efficient.

As the computational units in a data center need to operate at safe temperatures below manufacturer-provided limits, the budgeting policy in the data center needs to make sure a sufficient power budget is saved for the cooling elements. On the other hand, if there is over-cooling, then the overall efficiency drops because there is a smaller power budget left for computing.

I refer to the problem of how to efficiently allocate power to each server and to the cooling units as the “power budgeting” problem. The rest of the article elaborates on how this problem can be formulated and solved in a practical scenario.

Characterizing Loads

For distributing a total computational power budget in an application-aware manner, one needs to have an estimate of the relationship between server power and application performance. In my lab at Boston University, my students and I studied the relationship between application throughput and server power on a real-life system, and constructed empirical models that mimic this relationship.

Figure 1 demonstrates how the relationship between the instruction throughput and power consumption of a specific enterprise server changes depending on the application. Another interesting observation out of this figure is that, performance of some of the applications saturates beyond a certain power value. In other words, even when a larger power budget is given to such an application by letting it run with more threads (or in other cases, letting the processor operate at a higher speed), the application throughput does not improve further.

Figure 1: The plot demonstrates billion of instructions per second (BIPS) versus server power consumption as measured on an Oracle enterprise server including two SPARC T3 processors.

Figure 1: The plot demonstrates billion of instructions per second (BIPS) versus server power consumption as measured on an Oracle enterprise server including two SPARC T3 processors.

Estimating the slope of the throughput-power curve and the potential performance saturation point helps make better power budgeting decisions. In my lab, we constructed a model that estimates the throughput given server power and hardware performance counter measurements. In addition, we analyzed the potential performance bottlenecks resulting from a high number of memory accesses and/or the limited number of software threads in the application. We were able to predict the saturation point for each application via a regression-based equation constructed based on this analysis. Predicting the maximum server power using this empirical modeling approach gave a mean error of 11 W for our 400-to-700-W enterprise server.[1]

Such methods for power-performance estimations highlight the significance of telemetry-based empirical models for efficient characterization of future systems. The more detailed measurement capabilities newer computing systems can provide—such as the ability to measure power consumption of various sub-components of a server—the more accuracy one can achieve in constructing models to help with the data center management.

Temperature, Once Again

In several of my earlier articles this year, I emphasized the key role of temperature awareness for improving computing energy efficiency. This key role is a result of the high cost of cooling, the fact that server energy dynamics also rely on temperature substantially (i.e., consider the interactions among temperature, fan power and leakage power), and the impact of processor thermal management policies on performance.

Solving the budgeting problem efficiently, therefore, relies on having good estimates for how a given power allocation among the servers and cooling units would affect the temperature. The first step is estimating the CPU temperature for a given server power cap. In my lab, we modeled the CPU temperature as a function of the CPU junction-to-air thermal resistance, CPU power, and the inlet temperature to the server. CPU thermal resistance is determined by the hardware and packaging choices, and can be characterized empirically. For a given total server power, CPU power can be estimated using performance counter measurements in a similar way to estimating the performance given a server cap, as described above (see Figure 1). Our simple empirical temperature model was able to estimate temperature with a mean error of 2.9°C in our experiments on an Oracle enterprise server.[1]

Heat distribution characteristics of a data center depend strongly on the cooling technology used. For example, traditional data centers use a hot aisle-cold aisle configuration, where the cold air from the computer room air conditioners (CRAC) and the hot air coming out of the serves are separated by the rows of racks that contain the servers. The second step in thermal estimation, therefore, has to do with estimating the impact of servers to one another and the overall impact of the cooling system.

In a traditional hot-cold aisle setting, the inlet server temperatures can be estimated based on a heat distribution matrix, power consumption of all the servers, and the CRAC air temperature (which is the cold air input to the data center). Heat distribution matrix can be considered as a lumped model representing the impact of heat recirculation and the air flow properties together in a single N × N matrix, where N is the number of servers.[2]

Recently, using in-row coolers that leverage liquid cooling to improve efficiency of cooling is preferred in some (newer) data centers to improve cooling efficiency. In such settings, the heat recirculation effects are expected to be less significant as the most of the heat output of the servers is immediately removed from the data center.

In my lab, my students and I used low-cost data center temperature models to enable fast dynamic decisions.[1] Detailed thermal simulation of data centers is possible through computational fluid dynamics tools. Such tools, however, typically require prohibitively long simulation times.

Budgeting Optimization

What should the goal be during power budgeting? Maximizing overall throughput in the data center may seem like a reasonable goal. However, such a goal would favor allocating larger power caps to applications with higher throughput, and absolute throughput does not necessarily give an idea on whether the application QoS demand is met. For example, an application with a lower BIPS may have a stricter QoS target.

Consider this example for a better budgeting metric: the fair speed-up metric computes the harmonic mean of per-server speedup (i.e., per-server speedup is the ratio of measured BIPS to the maximum BIPS for an application). The purpose of this metric is to ensure none of the applications are starving while maximizing overall throughput.

It is also possible to impose constraints on the budgeting optimization such that a specific performance or throughput level is met for one or more of the applications. Ability to meet such constraints strongly relies on the ability to estimate the power-vs.-performance trends of the applications. Thus, empirical models I mentioned above are also essential for delivering more predictable performance to users.

Figure 2 demonstrates how the hill-climbing strategy my students and I designed for optimizing fair speed up evolves.  The algorithm starts setting the CRAC temperature to its last known optimal value, which is 20.6°C in this example. The CRAC power consumption corresponding to providing air input to the data center at 20.6°C can be computed using the relationship between CRAC temperature and the ratio of computing power to cooling power.[3] This relationship can often be derived from datasheets for the CRAC units and/or for the data center cooling infrastructure.

Figure 2: The budgeting algorithm starts from the last known optimal CRAC temperature value, and then iteratively aims to improve on the objective.

Figure 2: The budgeting algorithm starts from the last known optimal CRAC temperature value, and then iteratively aims to improve on the objective.

Once the cooling power is subtracted from the overall cap, the algorithm then allocates the remaining power among the servers with the objective of maximizing the fair speed up. Other constraints in the optimization formulation prevent any server to exceed manufacturer-given redline temperatures and ensure each server to receive a feasible power cap that falls between the server’s minimum and maximum power consumption levels.

The algorithm then iteratively searches for a better solution as demonstrated in steps 2 to 6 in Figure 2. Once the algorithm detects that the fair speed up is decreasing (e.g., fair speedup in step 6 is less than the speedup in step 5), it converges to the solution computed in the last step (e.g., converges to step 5 in the example). Note that setting cooler CRAC temperatures typically indicate a larger amount of cooling power, thus the fair speedup drops. However, as the CRAC temperature increases beyond a point, the performance of the hottest servers are degraded to maintain CPU temperatures below the redline; thus, a further increase in the CRAC temperature is not useful any longer (as in step 6).

This iterative algorithm took less than a second of running time using Matlab CVX[4] in our experiments for a small data center of 1,000 servers on an average desktop computer. This result indicates that the algorithm can be run in much shorter time with an optimized implementation, allowing for frequent real-time re-budgeting of power in a modern data center with a larger number of servers. Our algorithm improved fair speedup and BIPS per Watt by 10% to 20% compared to existing budgeting techniques.

Challenges

The initial methods and results I discussed above demonstrate promising energy efficiency improvements; however, there are many open problems for data center power budgeting.

First, the above discussion does not consider loads with some dependence to each other. For example, high-performance computing applications often have heavy communication among server nodes. This means that the budgeting method needs to account for the impact of inter-node communication for performance estimates as well as while making job allocation decisions in data centers.

Second, especially for data centers with a non-negligible amount of heat recirculation, thermally-aware job allocation significantly affects CPU temperature. Thus, job allocation should be optimized together with budgeting.

In data centers, there are elements other than the servers that consume significant amounts of power such as storage units. In addition there are a heterogeneous set of servers. Thus, a challenge lies in budgeting the power to a heterogeneous computing, storage, and networking elements.

Finally, the discussion above focuses on budgeting a total power cap among servers that are actively running applications. One can, however, also adjust the number of servers actively serving the incoming loads (by putting some servers into sleep mode/turning them off) and also consolidate the loads if desired. Consolidation often decreases performance predictability. The server provisioning problem needs to be solved in concert with the budgeting problem, taking the additional overheads into account. I believe all these challenges make the budgeting problem an interesting research problem for future data centers.

 

Ayse CoskunAyse K. Coskun (acoskun@bu.edu) is an assistant professor in the Electrical and Computer Engineering Department at Boston University. She received MS and PhD degrees in Computer Science and Engineering from the University of California, San Diego. Coskun’s research interests include temperature and energy management, 3-D stack architectures, computer architecture, and embedded systems. She worked at Sun Microsystems (now Oracle) in San Diego, CA, prior to her current position at BU. Coskun serves as an associate editor of the IEEE Embedded Systems Letters.

 

 
[1] O. Tuncer, K. Vaidyanathan, K. Gross, and A. K. Coskun, “CoolBudget: Data Center Power Budgeting with Workload and Cooling Asymmetry Awareness,” in Proceedings of IEEE International Conference on Computer Design (ICCD), October 2014.
[2] Q. Tang, T. Mukherjee, S. K. S. Gupta, and P. Cayton, “Sensor-Based fast Thermal Evaluation Model for Energy Efficient High-Performance Datacenters,” in ICISIP-06, October 2006.
[3] J. Moore, J. Chase, P. Ranganathan, and R. Sharma, “Making Scheduling ‘Cool’: Temperature-Aware Workload Placement in Data Centers,” in USENIX ATC-05, 2005.
[4] CVX Research, “CVX: Matlab Software for Disciplined Convex Programming,” Version 2.1, September 2014, http://cvxr.com/cvx/.

Small High-Current Power Modules

 

Exar Corp. recently announced the 10-A XR79110 and 15-A XR79115 single-output, synchronous step-down power modules. The modules will be available in mid-November in RoHS-compliant, green/halogen-free, QFN packages.

In a product release, Exar noted that “both devices provide easy to use, fully integrated power converters including MOSFETs, inductors, and internal input and output capacitors.”

The modules come in compact 10 x 10 x 4 mm and 12 x 12 x 4 mm footprints, respectively. The XR79110 and XR79115 offer versatility to convert from common input voltages such as 5, 12, and 19 V.

Both modules feature Exar’s emulated current-mode COT control scheme. The COT control loop enables operation with ceramic output capacitors and eliminates loop compensation components. According to Exar documentation, tthe output voltage can be set from 0.6 to 18 V and with exceptional full range 0.1% line regulation and 1% output accuracy over full temperature range.

The XR79110 and XR79115 are priced at $8.95 and $10.95, respectively, in 1,000-piece quantities.

Source: Exar Corp.

High-Bandwidth Oscilloscope Probe

Keysight Technologies recently announced a new high-bandwidth, low-noise oscilloscope probe, the N7020A, for making power integrity measurements to characterize DC power rails. The probe’s specs include:

  • low noise
  • large ± 24-V offset range
  • 50 kΩ DC input impedance
  • 2-GHz bandwidth for analyzing fast transients on their DC power-rails KeysightN7020A

According to Keysight’s product release, “The single-ended N7020A power-rail probe has a 1:1 attenuation ratio to maximize the signal-to-noise ratio of the power rail being observed by the oscilloscope. Comparable oscilloscope power integrity measurement solutions have up to 16× more noise than the Keysight solution. With its lower noise, the Keysight N7020A power-rail probe provides a more accurate view of the actual ripple and noise riding on DC power rails.”

 

The new N7020A power-rail probe starts at $2,650.

Source: Keysight Technologies 

Client Profile: Invenscience LC

Invenscience2340 South Heritage Drive, Suite I
Nibley UT, 84321

CONTACT: Collin Lewis, sales@invenscience.com
invenscience.com

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  • Gamer joystick controllers
  • Conventional joystick controllers
  • Equalizer-style slider controllers
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All of these controllers provide power and the radio control (RC) PWM signal necessary to make servos move without any programming effort.

EXCLUSIVE OFFER: Use the promo code “CC2014” to receive a 10% discount on all purchases through March 31, 2014.

Circuit Cellar prides itself on presenting readers with information about innovative companies, organizations, products, and services relating to embedded technologies. This space is where Circuit Cellar enables clients to present readers useful information, special deals, and more.

Testing Power Supplies (EE Tip #112)

How can you determine the stability of your lab or bench-top supply? You can get a good impression of the stability of a power supply under various conditions by loading the output dynamically. This can be implemented using just a handful of components.

Power supply testing

Power supply testing

Apart from obvious factors such as output voltage and current, noise, hum and output resistance, it is also important that a power supply has a good regulation under varying load conditions. A standard test for this uses a resistor array across the output that can be switched between two values. Manufacturers typically use resistor values that correspond to 10% and 90% of the rated power output of the supply.

The switching frequency between the values is normally several tens of hertz (e.g. 40 Hz). The behavior of the output can then be inspected with an oscilloscope, from which you can deduce how stable the power supply is. At the rising edge of the square wave you will usually find an overshoot, which is caused by the way the regulator functions, the inductance of the internal and external wiring and any output filter.

This dynamic behavior is normally tested at a single frequency, but the designers in the Elektor Lab have tested numerous lab supplies over the years and it seemed interesting to check what happens at higher switching frequencies. The only items required for this are an ordinary signal generator with a square wave output and the circuit shown in Figure 1.Fig1-pwrsupply

You can then take measurements up to several megahertz, which should give you a really good insight for which applications the power supply is suitable. More often than not you will come across a resonance frequency at which the supply no longer remains stable and it’s interesting to note at which frequency that occurs.

The circuit really is very simple. The power MOSFET used in the circuit is a type that is rated at 80 V/75 A and has an on-resistance of only 10 mΩ (VGS = 10 V).

The output of the supply is continuously loaded by R2, which has a value such that 1/10th of the maximum output current flows through it (R2 = Vmax/0.1/max). The value of R1 is chosen such that 8/10th of the maximum current flows through it (R1 = Vmax/0.8/max). Together this makes 0.9/max when the MOSFET conducts. You should round the calculated values to the nearest E12 value and make sure that the resistors are able to dissipate the heat generated (using forced cooling, if required).

At larger output currents the MOSFET should also be provided with a small heatsink. The gate of the FET is connected to ground via two 100-Ω resistors, providing a neat 50-Ω impedance to the output of the signal generator. The output voltage of the signal generator should be set to a level between 5 V and 10 V, and you’re ready to test. Start with a low switching frequency and slowly increase it, whilst keeping an eye on the square wave on the oscilloscope. And then keep increasing the frequency… Who knows what surprises you may come across? Bear in mind though that the editorial team can’t be held responsible for any damage that may occur to the tested power supply. Use this circuit at your own risk!

— Harry Baggen and Ton Giesberts (Elektor, February 210)

High-Voltage Gate Driver IC

Allegro A4900 Gate Driver IC

Allegro A4900 Gate Driver IC

The A4900 is a high-voltage brushless DC (BLDC) MOSFET gate driver IC. It is designed for high-voltage motor control for hybrid, electric vehicle, and 48-V automotive battery systems (e.g., electronic power steering, A/C compressors, fans, pumps, and blowers).

The A4900’s six gate drives can drive a range of N-channel insulated-gate bipolar transistors (IGBTs) or power MOSFET switches. The gate drives are configured as three high-voltage high-side drives and three low-side drives. The high-side drives are isolated up to 600 V to enable operation with high-bridge (motor) supply voltages. The high-side drives use a bootstrap capacitor to provide the supply gate drive voltage required for N-channel FETs. A TTL logic-level input compatible with 3.3- or 5-V logic systems can be used to control each FET.

A single-supply input provides the gate drive supply and the bootstrap capacitor charge source. An internal regulator from the single supply provides the logic circuit’s lower internal voltage. The A4900’s internal monitors ensure that the high- and low-side external FET’s gate source voltage is above 9 V when active.

The control inputs to the A4900 offer a flexible solution for many motor control applications. Each driver can be driven with an independent PWM signal, which enables implementation of all motor excitation methods including trapezoidal and sinusoidal drive. The IC’s integrated diagnostics detect undervoltage, overtemperature, and power bridge faults that can be configured to protect the power switches under most short-circuit conditions. Detailed diagnostics are available as a serial data word.

The A4900 is supplied in a 44-lead QSOP package and costs $3.23 in 1,000-unit quantities.

Allegro MicroSystems, LLC
www.allegromicro.com

Solar Cells Explained (EE Tip #104)

All solar cells are made from at least two different materials, often in the form of two thin, adjacent layers. One of the materials must act as an electron donor under illumination, while the other material must act as an electron acceptor. If there is some sort of electron barrier between the two materials, the result is an electrical potential. If each of these materials is now provided with an electrode made from an electrically conductive material and the two electrodes are connected to an external load, the electrons will follow this path.

Source: Jens Nickels, Elektor, 070798-I, 6/2009

Source: Jens Nickels, Elektor, 070798-I, 6/2009

The most commonly used solar cells are made from thin wafers of polycrystalline silicon (polycrystalline cells have a typical “frosty” appearance after sawing and polishing). The silicon is very pure, but it contains an extremely small amount of boron as a dopant (an intentionally introduced impurity), and it has a thin surface layer doped with phosphorus. This creates a PN junction in the cell, exactly the same as in a diode. When the cell is exposed to light, electrons are released and holes (positive charge carriers) are generated. The holes can recombine with the electrons. The charge carriers are kept apart by the electrical field of the PN junction, which partially prevents the direct recombination of electrons and holes.

The electrical potential between the electrodes on the top and bottom of the cell is approximately 0.6 V. The maximum current (short-circuit current) is proportional to the surface area of the cell, the impinging light energy, and the efficiency. Higher voltages and currents are obtained by connecting cells in series to form strings and connecting these strings of cells in parallel to form modules.

The maximum efficiency achieved by polycrystalline cells is 17%, while monocrystalline cells can achieve up to 22%, although the overall efficiency is lower if the total module area is taken into account. On a sunny day in central Europe, the available solar energy is approximately 1000 W/m2, and around 150 W/m2 of this can be converted into electrical energy with currently available solar cells.

Source: Jens Nickels, Elektor, 070798-I, 6/2009

Source: Jens Nickels, Elektor, 070798-I, 6/2009

Cells made from selenium, gallium arsenide, or other compounds can achieve even higher efficiency, but they are more expensive and are only used in special applications, such as space travel. There are also other approaches that are aimed primarily at reducing costs instead of increasing efficiency. The objective of such approaches is to considerably reduce the amount of pure silicon that has to be used or eliminate its use entirely. One example is thin-film solar cells made from amorphous silicon, which have an efficiency of 8 to 10% and a good price/performance ratio. The silicon can be applied to a glass sheet or plastic film in the form of a thin layer. This thin-film technology is quite suitable for the production of robust, flexible modules, such as the examples described in this article.

Battery Charging

From an electrical viewpoint, an ideal solar cell consists of a pure current source in parallel with a diode (the outlined components in the accompanying schematic diagram). When the solar cell is illuminated, the typical U/I characteristic of the diode shifts downward (see the drawing, which also shows the opencircuit voltage UOC and the short-circuit current ISC). The panel supplies maximum power when the load corresponds to the points marked “MPP” (maximum power point) in the drawing. The power rating of a cell or panel specified by the manufacturer usually refers to operation at the MPP with a light intensity of 100,000 lux and a temperature of 25°C. The power decreases by approximately 0.2 to 0.5 %/°C as the temperature increases.

A battery can be charged directly from a panel without any problems if the open-circuit voltage of the panel is higher than the nominal voltage of the battery. No voltage divider is necessary, even if the battery voltage is only 3 V and the nominal voltage of the solar panel is 12 V. This is because a solar cell always acts as a current source instead of a voltage source.

If the battery is connected directly to the solar panel, a small leakage current will flow through the solar panel when it is not illuminated. The can be prevented by adding a blocking diode to the circuit (see the schematic). Many portable solar modules have a built-in blocking diode (check the manufacturer’s specifications).

This simple arrangement is adequate if the maximum current from the solar panel is less than the maximum allowable overcharging current of the battery. NiMH cells can be overcharged for up to 100 hours if the charging current (in A) is less than one-tenth of their rated capacity in Ah. This means that a panel with a rated current of 2 A can be connected directly to a 20-Ah battery without any problems. However, under these conditions the battery must be fully discharged by a load from time to time.

Practical Matters

When positioning a solar panel, you should ensure that no part of the panel is in the shade, as otherwise the voltage will decrease markedly, with a good chance that no current will flow into the connected battery.

Most modules have integrated bypass diodes connected in reverse parallel with the solar cells. These diodes prevent reverse polarization of any cells that are not exposed to sunlight, so the current from the other cells flows through the diodes, which can cause overheating and damage to the cells. To reduce costs, it is common practice to fit only one diode to a group of cells instead of providing a separate diode for each cell.

—Jens Nickels, Elektor, 070798-I, 6/2009

Simple Guitar Transmitter (EE Tip #102)

You need a guitar amplifier to play an electric guitar. The guitar must be connected with a cable to the amplifier, which you might consider an inconvenience. Most guitar amplifiers operate off the AC power line. An electric guitar fitted with a small transmitter offers several advantages. You can make the guitar audible via an FM tuner/amplifier, for example. Both the connecting cable and amplifier are then unnecessary. With a portable FM broadcast radio or, if desired, a boombox, you can play in the street or in subway.

Source: Elektor 3/2009

Source: Elektor 3/2009

stations (like Billy Bragg). In that case, everything is battery-powered and independent of a fixed power point. (You might need a permit, though.)

Designing a transmitter to do this is not necessary. A variety of low-cost transmitters are available. The range of these devices is often not more than around 30′, but that’s likely plenty for most applications. Consider a König FMtrans20 transmitter. After fitting the batteries and turning it on, you can detect a carrier signal on the radio. Four channels are available, so it should always be possible to find an unused part of the FM band. A short cable with a 3.5-mm stereo audio jack protrudes from the enclosure. This is the audio input. The required signal level for sufficient modulation is about 500 mVPP.

If a guitar is connected directly, the radio’s volume level will have to be high to get sufficient sound. In fact, it will have to be so high that the noise from the modulator will be quite annoying. Thus, a preamplifier for the guitar signal is essential.

To build this preamplifier into the transmitter, you first have to open the enclosure. The two audio channels are combined. This is therefore a single channel (mono) transmitter. Because the audio preamplifier can be turned on and off at the same time as the transmitter, you also can use the transmitter’s on-board power supply for power. In our case, that was about 2.2 V. This voltage is available at the positive terminal of an electrolytic capacitor. Note that 2.2 V is not enough to power an op-amp. But with a single transistor the gain is already big enough and the guitar signal is sufficiently modulated. The final implementation of the modification involves soldering the preamplifier circuit along an edge of the PCB so that everything still fits inside the enclosure. The stereo cable is replaced with a 11.8″ microphone cable, fitted with a guitar plug (mono jack). The screen braid of the cable acts as an antenna as well as a ground connection for the guitar signal. The coil couples the low-frequency signal to ground, while it isolates the high-frequency antenna signal. While playing, the cable with the transmitter just dangles below the guitar, without being a nuisance. If you prefer, you can also secure the transmitter to the guitar with a bit of double-sided tape.

—Gert Baars, “Simple Guitar Transmitter,” Elektor,  080533-1, 3/2009.

Two-Channel CW Laser Diode Driver with an MCU Interface

The iC-HT laser diode driver enables microcontroller-based activation of laser diodes in Continuous Wave mode. With this device, laser diodes can be driven by the optical output power (using APC), the laser diode current (using ACC), or a full controller-based power control unit.

The maximum laser diode current per channel is 750 mA. Both channels can be switched in parallel for high laser diode currents of up to 1.5 A. A current limit can also be configured for each channel.

Internal operating points and voltages can be output through ADCs. The integrated temperature sensor enables the system temperature to be monitored and can also be used to analyze control circuit feedback. Logarithmic DACs enable optimum power regulation across a large dynamic range. Therefore, a variety of laser diodes can be used.

The relevant configuration is stored in two equivalent memory areas. Internal current limits, a supply-voltage monitor, channel-specific interrupt-switching inputs, and a watchdog safeguard the laser diodes’ operation through iC-HT.

The device can be also operated by pin configuration in place of the SPI or I2C interface, where external resistors define the APC performance targets. An external supply voltage can be controlled through current output device configuration overlay (DCO) to reduce the system power dissipation (e.g., in battery-operated devices or systems).

The iC-HT operates on 2.8 to 8 V and can drive both blue and green laser diodes. The diode driver has a –40°C-to-125°C operating temperature range and is housed in a 5-mm × 5-mm, 28-pin QFN package.

The iC-HT costs $13.20 in 1,000-unit quantities.

iC-Haus GmbH
www.ichaus.com

Accurate Measurement Power Analyzer

The PA4000 power analyzer provides accurate power measurements. It offers one to four input modules, built-in test modes, and standard PC interfaces.

The analyzer features innovative Spiral Shunt technology that enables you to lock onto complex signals. The Spiral Shunt design ensures stable, linear response over a range of input current levels, ambient temperatures, crest factors, and other variables. The spiral construction minimizes stray inductance (for optimum high-frequency performance) and provides high overload capability and improved thermal stability.

The PA4000’s additional features include 0.04% basic voltage and current accuracy, dual internal current shunts for optimal resolution, frequency detection algorithms for noisy waveform tracking, application-specific test modes to simplify setup. The analyzer  easily exports data to a USB flash drive or PC software. Harmonic analysis and communications ports are included as standard features.

Contact Tektronix for pricing.

Tektronix, Inc.
www.tek.com

Design a Low-Power System in 2013

A few months ago, we listed the top design projects from the Renesas RL78 Green Energy Challenge. Today, we’re excited to announce that Circuit Cellar‘s upcoming 25th anniversary issue will include a mini-challenge featuring the RL78. In the issue, you’ll learn about a new opportunity to register for an RL78/G14 demonstration kit that you can use to build a low-power design.

Renesas RL78

The RL78/G14 demonstration kit (RDK) is a handy evaluation tool for the RL78/G14 microcontrollers. Several powerful compilers and sample projects will be offered either free-of-charge (e.g., the GNU compiler) or with a code-size-limited compiler evaluation license (e.g., IAR Systems).  Also featured will be user-friendly GUIs, including the Eclipse-based e2studio.

RL78G14 RDK KIT

  • 32-MHz RL78/G14 MCU board with integrated debugger and huge peripheral, including Wi-Fi, E Ink display, matrix LCD, audio ports, IR ports, motor control port, FET and isolated triac interfaces
  •  256-KB On-chip flash
  • USB Debugger cable
  • Four factory demos showcasing local and cloud connectivity through Wi-Fi

The CC25 anniversary issue is now available.

Electrostatic Cleaning Robot Project

How do you clean a clean-energy generating system? With a microcontroller (and a few other parts, of course). An excellent example is US designer Scott Potter’s award-winning, Renesas RL78 microcontroller-based Electrostatic Cleaning Robot system that cleans heliostats (i.e., solar-tracking mirrors) used in solar energy-harvesting systems. Renesas and Circuit Cellar magazine announced this week at DevCon 2012 in Garden Grove, CA, that Potter’s design won First Prize in the RL78 Green Energy Challenge.

This image depicts two Electrostatic Cleaning Robots set up on two heliostats. (Source: S. Potter)

The nearby image depicts two Electrostatic Cleaning Robots set up vertically in order to clean the two heliostats in a horizontal left-to-right (and vice versa) fashion.

The Electrostatic Cleaning Robot in place to clean

Potter’s design can quickly clean heliostats in Concentrating Solar Power (CSP) plants. The heliostats must be clean in order to maximize steam production, which generates power.

The robot cleaner prototype

Built around an RL78 microcontroller, the Electrostatic Cleaning Robot provides a reliable cleaning solution that’s powered entirely by photovoltaic cells. The robot traverses the surface of the mirror and uses a high-voltage AC electric field to sweep away dust and debris.

Parts and circuitry inside the robot cleaner

Object oriented C++ software, developed with the IAR Embedded Workbench and the RL78 Demonstration Kit, controls the device.

IAR Embedded Workbench IDE

The RL78 microcontroller uses the following for system control:

• 20 Digital I/Os used as system control lines

• 1 ADC monitors solar cell voltage

• 1 Interval timer provides controller time tick

• Timer array unit: 4 timers capture the width of sensor pulses

• Watchdog timer for system reliability

• Low voltage detection for reliable operation in intermittent solar conditions

• RTC used in diagnostic logs

• 1 UART used for diagnostics

• Flash memory for storing diagnostic logs

The complete project (description, schematics, diagrams, and code) is now available on the Challenge website.

 

DIY Green Energy Design Projects

Ready to start a low-power or energy-monitoring microcontroller-based design project? You’re in luck. We’re featuring eight award-winning, green energy-related designs that will help get your creative juices flowing.

The projects listed below placed at the top of Renesas’s RL78 Green Energy Challenge.

Electrostatic Cleaning Robot: Solar tracking mirrors, called heliostats, are an integral part of Concentrating Solar Power (CSP) plants. They must be kept clean to help maximize the production of steam, which generates power. Using an RL78, the innovative Electrostatic Cleaning Robot provides a reliable cleaning solution that’s powered entirely by photovoltaic cells. The robot traverses the surface of the mirror and uses a high voltage AC electric field to sweep away dust and debris.

Parts and circuitry inside the robot cleaner

Cloud Electrofusion Machine: Using approximately 400 times less energy than commercial electrofusion machines, the Cloud Electrofusion Machine is designed for welding 0.5″ to 2″ polyethylene fittings. The RL78-controlled machine is designed to read a barcode on the fitting which determines fusion parameters and traceability. Along with the barcode data, the system logs GPS location to an SD card, if present, and transmits the data for each fusion to a cloud database for tracking purposes and quality control.

Inside the electrofusion machine (Source: M. Hamilton)

The Sun Chaser: A GPS Reference Station: The Sun Chaser is a well-designed, solar-based energy harvesting system that automatically recalculates the direction of a solar panel to ensure it is always facing the sun. Mounted on a rotating disc, the solar panel’s orientation is calculated using the registered GPS position. With an external compass, the internal accelerometer, a DC motor and stepper motor, you can determine the solar panel’s exact position. The system uses the Renesas RDKRL78G13 evaluation board running the Micrium µC/OS-III real-time kernel.

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Water Heater by Solar Concentration: This solar water heater is powered by the RL78 evaluation board and designed to deflect concentrated amounts of sunlight onto a water pipe for continual heating. The deflector, armed with a counterweight for easy tilting, automatically adjusts the angle of reflection for maximum solar energy using the lowest power consumption possible.

RL78-based solar water heater (Source: P. Berquin)

Air Quality Mapper: Want to make sure the air along your daily walking path is clean? The Air Quality Mapper is a portable device designed to track levels of CO2 and CO gasses for constructing “Smog Maps” to determine the healthiest routes. Constructed with an RDKRL78G13, the Mapper receives location data from its GPS module, takes readings of the CO2 and CO concentrations along a specific route and stores the data in an SD card. Using a PC, you can parse the SD card data, plot it, and upload it automatically to an online MySQL database that presents the data in a Google map.

Air quality mapper design (Source: R. Alvarez Torrico)

Wireless Remote Solar-Powered “Meteo Sensor”: You can easily measure meteorological parameters with the “Meteo Sensor.” The RL78 MCU-based design takes cyclical measurements of temperature, humidity, atmospheric pressure, and supply voltage, and shares them using digital radio transceivers. Receivers are configured for listening of incoming data on the same radio channel. It simplifies the way weather data is gathered and eases construction of local measurement networks while being optimized for low energy usage and long battery life.

The design takes cyclical measurements of temperature, humidity, atmospheric pressure, and supply voltage, and shares them using digital radio transceivers. (Source: G. Kaczmarek)

Portable Power Quality Meter: Monitoring electrical usage is becoming increasingly popular in modern homes. The Portable Power Quality Meter uses an RL78 MCU to read power factor, total harmonic distortion, line frequency, voltage, and electrical consumption information and stores the data for analysis.

The portable power quality meter uses an RL78 MCU to read power factor, total harmonic distortion, line frequency, voltage, and electrical consumption information and stores the data for analysis. (Source: A. Barbosa)

High-Altitude Low-Cost Experimental Glider (HALO): The “HALO” experimental glider project consists of three main parts. A weather balloon is the carrier section. A glider (the payload of the balloon) is the return section. A ground base section is used for communication and display telemetry data (not part of the contest project). Using the REFLEX flight simulator for testing, the glider has its own micro-GPS receiver, sensors and low-power MCU unit. It can take off, climb to pre-programmed altitude and return to a given coordinate.

High-altitude low-cost experimental glider (Source: J. Altenburg)

AC Tester Schematic Update

An error was found in one of the AC tester schematics that ran in Kevin Gorga’s June 2012 article, “AC Tester” (Circuit Cellar 263). As a reader indicated, T2 is disconnected in the published version of the schematic. An edited schematic follows.

Edited version of Figure 2 in K. Gorga’s June 2012 article, “AC Tester” (Source: Paul Alciatore)

The correction is now available on Circuit Cellar‘s Errata, Corrections, & Updates page.

DIY, Microcontroller-Based Battery Monitor for RC Aircraft

I’ve had good cause to be reading and perusing a few old Circuit Cellar articles every day for the past several weeks. We’re preparing the upcoming 25th anniversary issue of Circuit Cellar, and part of the process is reviewing the company’s archives back to the first issue. As I read through Circuit Cellar 143 (2002) the other day I thought, why wait until the end of the year to expose our readers to such intriguing articles? Since joining Elektor International Media in 2009, thousands of engineers and students across the globe have become familiar with our magazine, and most of them are unfamiliar with the early articles. It was in those articles that engineers set the foundation for the development of today’s embedded technologies.

Over the next few months, I will highlight some past articles here on CircuitCellar.com as well as in our print magazine. I encourage long-time readers to revisit these articles and projects and reflect on their past and present use values. Newer readers should not regard them as simply historical documents detailing outdated technologies. Not only did the technologies covered lead to the high-level engineering you do today, many of those technologies are still in use.

The article below is about Thomas Black’s “BatMon” battery monitor for RC applications (Circuit Cellar 143, 2002). I am leading with it simply because it was one of the first I worked on.

For years, hobbyists have relied on voltmeters and guesswork to monitor the storage capacity of battery packs for RC models. Black’s precise high-tech battery monitor is small enough to be mounted in the cockpit of an RC model helicopter. Black writes:

I hate to see folks suffer with old-fashioned remedies. After three decades of such anguish, I decided that enough is enough. So what am I talking about? Well, my focus for today’s pain relief is related to monitoring the battery packs used in RC models. The cure comes as BatMon, the sophisticated battery monitoring accessory shown in Photo 1.

Photo 1: The BatMon is small enough to fit in most RC models. The three cables plug into the model’s RC system. A bright LED remotely warns the pilot of battery trouble. The single character display reports the remaining capacity of the battery.

Today, electric model hobbyists use the digital watt-meter devices, but they are designed to monitor the heavy currents consumed by electric motors. I wanted finer resolution so I could use it with my RC receiver and servos. With that in mind, a couple of years ago, I convinced my firm that we should tackle this challenge…My solution evolved into the BatMon, a standalone device that can mount in each model aircraft (see Figure 1).

Figure 1: Installation in an RC model is as simple as plugging in three cables. Multiple point measurements allow the system to detect battery-related trouble. Voltage detection at the RC receiver even helps detect stalled servos and electrical issues.

This is not your typical larger-than-life Gotham City solution. It’s only 1.3″ × 2.8″ and weighs one ounce. But the BatMon does have the typical dual persona expected of a super hero. For user simplicity, it reports battery capacity as a zero to nine (0% to 90%) level value. This is my favorite mode because it works just like a car’s gas gauge. However, for those of you who must see hard numbers, it also reports the actual remaining capacity—up to 2500 mAH—with 5% accuracy. In addition, it reports problems associated with battery pack failures, bad on/off switches, and defective servos. A super-bright LED indicator flashes if any trouble is detected. Even in moderate sunlight this visual indicator can be seen from a couple hundred feet away, which is perfect for fly-by checks. The BatMon is compatible with all of the popular battery sizes. Pack capacities from 100 mAH to 2500 mAH can be used. They can be either four-cell or five-cell of either NiCD or NiMH chemistries. The battery parameters are programmed by using a push button and simple menu interface. The battery gauging IC that I used is from Dallas Semiconductor (now Maxim). There are other firms that have similar parts (Unitrode, TI, etc.), but the Dallas DS2438 Smart Battery Monitor was a perfect choice for my RC application (see Figure 2).

Figure 2: A battery fuel gauging IC and a microcontroller are combined to accurately measure the current consumption of an RC system. The singlecharacter LCD is used to display battery data and status messages.

This eight-pin coulomb counting chip contains an A/D-based current accumulator, A/D voltage convertor, and a slew of other features that are needed to get the job done. The famous Dallas one-wire I/O method provides an efficient interface to a PIC16C63 microcontroller…In the BatMon, the one-wire bus begins at pin 6 (port RA4) of the PIC16C63 microcontroller and terminates at the DS2438’s DQ I/O line (pin 8). Using bit-banging I/O, the PIC can read and write the necessary registers. The timing is critical, but the PIC is capable of handling the chore…The BatMon is not a good candidate for perfboard construction. A big issue is that RC models present a harsh operating environment. Vibration and less than pleasant landings demand that you use rugged electronic assembly techniques. My vote is that you design a circuit board for it. It is not a complicated circuit, so with the help of a freeware PCB program you should be on your way…The connections to the battery pack and receiver are made with standard RC hobby servo connectors. They are available at most RC hobby shops. You will need a 22-AWG, two-conductor female cable for the battery (J1), a 22-AWG, two-conductor male for the RC switch (J2), and a three-conductor (any AWG) for the Aux In (J3) connector…The finished unit is mounted in the model’s cockpit using double-sided tape or held with rubber bands (see Photo 2).

Photo 2: Here's how the battery monitor looks installed in the RC model helicopter’s cockpit. You can use the BatMon on RC airplanes, cars, and boats too. Or, you could adapt the design for battery monitoring applications that aren’t RC-related.

Thomas Black designs and supports high-tech devices for the consumer and industrial markets. He is currently involved in telecom test products. During his free time, he can be found flying his RC models. Sometimes he attempts to improve his models by creating odd electronic designs, most of which are greeted by puzzled amusement from his flying pals.

The complete article is now available.