Read the original entry from IPSO CHALLENGE 2013′s Grand Prize winner, Redwire Consulting, LLC.
Title: Energy Harvesting Wireless Internet Current Transformer for End-Use Power Metering
Abstract: Auditing the end-use for electricity consumption is the first step towards implementing effective conservation methods. Without ongoing monitoring of which loads are consuming the most energy and which conservation methods produce the largest gains, effective measures cannot be implemented.
Current end-use monitoring systems are difficult to use and install. They rely on local storage rather that directly uploading data through the Internet to cloud storage and analysis. Installation requires special skill and electrical knowledge.
Our development of a current transformer that directly connects to the internet and is powered by the primary current of the load it is measuring makes end-use monitoring easier to use and install than ever before.
A typical end-use monitoring system uses a local storage system to collect data from up to 64 current transformers that have been wired into a buildings sub-panel. These need to be installed by hand, usually requiring each conductor to be disconnected, fed through the current transformer, and reconnected to the mains circuit. Each current transformer then has to be connected to a local logger, which leaves circuit breaker panels a mess.
Our current transformer incorporates a split-core in a snap-lock enclosure, which allows it to clip over conductors. Each current transformer contains a low-power microcontroller with built-in 802.15.4 radio and an energy harvester. The energy harvester removes the need for an external power source, which would require wiring, and the built in radio allows the CT to upload each sample directly to a cloud server.
The result is a compact current transformer that installs in seconds and requires no local storage, external power supply, or external wiring whatsoever.
IP Usage: IP enables us to efficiently transmit power metering data directly to the cloud. End-use metering generates copious amount of data in short periods of time. This data is typically stored locally on a physical storage device, which is manually retrieved and entered into a networked storage system. Not only is manual labor required, but storing the data is slow and power hungry in itself.
IP provides a common language between the current transformer and a growing number of other devices that are IP enabled. By doing so, we can focus development on application challenges and user-experience rather than re-inventing software that enables integration. This provides a new degree of market flexibility and makes our products easier to use.
Efficient data transmission directly from an end node to cloud storage is what enables us to harvest energy from a tiny power source, like a current transformer, and still have enough energy to transmit every few seconds.
Application: The energy harvesting wireless internet current transformer is intended for end-use power metering. End-use power metering is comprised of remote power monitoring, operational power diagnostics, power failure diagnostics, local power demand response, and load shedding. The ability to connect directly to the internet provides unparalleled flexibility in local power data analysis, enabling the functionality listed above.
In the case of remote power monitoring, an end-user would have the ability to audit energy consumption in their buildings or facility and identify costly branch circuits. This is an essential step for effective analysis and reduction of energy consumption as reduction measures are often costly to perform. Once reduction measures have been performed, remote power monitoring can be used to verify that these measures were effective and can justify their gains.
Another key application for end-use current monitoring is for remote diagnostics and failure detection. Monitoring the current in a load is a simple indication that a load is operating. By combining this with expected use conditions (such as thermostatic calls), simple failures can be detected.
In addition to monitoring if a load is present when it should be, the history of the load current can be analyzed to determine the state-of-health for the load. It is then possible to implement preventative maintenance schedules which reduces overall repair costs (by maintaining load efficiency) and can avoid costly “crisis” repairs due to unexpected breakdowns.
Lastly, branch current data could be used to detect overloading on critical circuits. In the case of an overload, M2M communication could facilitate load shedding to help mitigate the problem.
Description: Energy Harvesting
We’ve designed a current transformer energy harvester to accomplish sampling and transmitting without additional power sources. When powered, the secondary of a current transformer looks like a high impedance power source. In order to efficiently harvest energy from a current transformer, you need a high impedance AC energy harvester. The harvester uses a synchronous rectifier to charge up a high voltage storage capacitor. The storage cap powered a high-efficiency buck converter which had a regulated output voltage of 2.5V. This high-voltage storage to low-voltage output configuration allows us to utilize the square voltage law for energy stored in a capacitor and miniaturize the storage capacitor to a reasonable size. Since the energy stored in a capacitor is proportional to the square of its voltage, we can determine when the capacitor is charged enough to sample and transmit by reading the storage capacitor’s voltage with an ADC during the sleep cycle.
Once woken up, the microcontroller needs to sample one 60Hz cycle of the current waveform. In order to get a clean reading, the microcontroller switches in a heavy load to burden the current transformer. At this point, the energy harvester can no longer be powered, and the microcontroller begins running off of stored energy in the storage cap. The effects of the synchronous rectifier are no longer visible, and the resulting waveform is smooth. The low-resistance burden causes the output of the current transformer to drop to a region where the voltage amplitude is more linearly related to the current in the primary. Because the CT’s voltage amplitude may be low and may dip below ground, we need a difference amp to measure the voltage across the CT, then perhaps amplify and offset the resulting waveform to mid-rail (1.125V). The waveform can then swing +-1.125V, allowing the ADC to sample with reasonable resolution. The ADC then reads 256 samples over a cycle and proceeds to transmit those samples over IP on its 802.15.4 radio.
Our microcontroller is an ARM7 core with an integrated 802.15.4 radio from Freescale Semiconductor (MC13224v). We run the Contiki Operating System which provides a full IPv6 stack and the 6LoWPAN IPv6 to 802.15.4 adaption layer. We are using the RPL routing protocol and the current transformers join as leaf nodes. The current transformers provide a RESTful interface using the CoAP protocol. The data is set via POST to the end server after optionally performing DNS name resolution.