A low cost master and slave distributed intelligent meter for non-intrusive load classification and anomaly warning

Quek, Y. T., Woo, Wai Lok and Logenthiran, Thillainathan (2018) A low cost master and slave distributed intelligent meter for non-intrusive load classification and anomaly warning. In: I2MTC - 2018 IEEE International Instrumentation and Measurement Technology Conference, 14th - 17th May 2018, Houston, Texas.

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In the recent years, there is a growing need for remote monitoring of electrical systems to assist users in making informed decision in managing their electrical systems. On top of the energy consumption and power usage, it is very useful to provide users fault detections, anomaly warnings and classification of load for predictive maintenance. This paper describes a low-cost solution that consists of multiple distributed slave meters with a single master computer. The distributed slave meter acquires the current waveform from the investigated cable of interest with non-intrusive current transducer. The low-cost hardware and small computer conditioned the data and extract a 2-feature data that will be send to the master computer remotely over Wi-Fi network. The master computer identifies each slave meters by their IP addresses and will perform logging, high level processing for classification, fault detection and anomaly warning. This solution can be implemented as a distributed monitoring network over a number of circuits in a building or vicinity, with the option of expanding into an Internet of Things (IoT) implementation. It can also be used as an ad hoc standalone investigation of a suspicious branch circuit for faults or anomalies. This solution has been tested with lighting, thermal and motor loads in an electrical circuit.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Statistical Process Control, anomalies warning, intelligent meter, load classification, non-intrusive load monitoring
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:
Depositing User: Paul Burns
Date Deposited: 25 Mar 2019 12:41
Last Modified: 10 Oct 2019 19:18
URI: http://nrl.northumbria.ac.uk/id/eprint/38529

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