Identifying problems in renewable energy networks

The Power Quality (PQ) of the electricity supply is becoming increasingly important owing to the large scale use of power electronic devices, coupled with the increasing number of loads which are sensitive to voltage disturbances, such as control systems and computers. As a result of the problems caused by the confluence of these two trends, there is an increasing need for PQ to be monitored in order to diagnose its nature and locate the source of the disturbance, allowing remedial measures to be taken. Whilst automated systems for diagnosis of PQ events have been developed, identifying the location of the source of a disturbance is a problem which has not been fully addressed to date; in this talk, a novel approach to identify the location of the source of a PQ disturbance is described, using a form of Artificial Neural Network known as a Self Organising Map. This approach provides a means of locating the source of power quality events, including transient disturbances.

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AI for better patient outcomes

Conference - Wed 18 October 2017 at 09:00

Cambridge

Artificial intelligence (AI), deep learning and machine learning all serve to interpret big data for an end goal. In healthcare, better patient outcomes are the great incentive.

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