Electrical interruptions, or “nuisance trips,” occur when a detector installed behind the wall trips an outlet's electrical circuit when it senses something that could be an arc-fault — a potentially dangerous spark in the electric line. The problem with today's arc-fault detectors is that they often err on the side of being overly sensitive, shutting off an outlet's power in response to electrical signals that are actually harmless.
A smart power outlet was developed that can analyze electrical current usage from a single outlet or multiple outlets, and can distinguish between benign arcs — harmless electrical spikes such as those caused by common household appliances — and dangerous arcs, such as sparking that results from faulty wiring and could lead to a fire. The device can also be trained to identify what might be plugged into a particular outlet, such as a fan versus a desktop computer.
The design comprises custom hardware that processes electrical current data in real time, and software that analyzes the data via a neural network — a set of machine learning algorithms that are inspired by the workings of the human brain. In this case, the machine-learning algorithm is programmed to determine whether a signal is harmful or not by comparing a captured signal to others that previously were used to train the system. The more data the network is exposed to, the more accurately it can learn characteristic “fingerprints” used to differentiate good from bad, or even to distinguish one appliance from another.
The smart power outlet is able to connect to other devices wirelessly as part of the Internet of Things. A pervasive network could be developed in which customers can install not only a smart power outlet in their homes, but also an app on their phone through which they can analyze and share data on their electrical usage.
The hardware setup consists of a Raspberry Pi Model 3 microcomputer — a low-cost, power-efficient processor that records incoming electrical current data — and an inductive current clamp that fixes around an outlet's wire without actually touching it, sensing the passing current as a changing magnetic field. Between the current clamp and the microcomputer, a USB sound card was connected, which was used to read the incoming current data.
After training the network, the hardware and software were run on new data from the same four devices, and found it was able to discern among the four types of devices (for example, a fan versus a computer) with 95.61 percent accuracy. In identifying good from bad signals, the system achieved 99.95 percent accuracy — slightly higher than existing AFCIs. The system was also able to react quickly and trip a circuit in less than 250 milliseconds, matching the performance of contemporary, certified arc detectors.
A neural network could be run over the Internet where other users can connect to it and report on their electrical usage, providing additional data to the network that helps it to learn new definitions and associate new electrical patterns with new appliances and devices. These new definitions would then get shared wirelessly to users’ outlets, improving their performance and reducing the risk of nuisance trips without compromising safety.