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SYNAPS Fault Detection, Classification & Location Solution

Rona Mitchell

About UK Power Networks

Our vision is formed by our employees, consumers and wider stakeholders:

The safest

The best employer

The most reliable

The most satisfied customers

The most innovative

The most socially responsible

The lowest cost

Agenda

• Project Partners

• Project Overview

• Network Context

• Project Detail

• Q&A

Project Partner: Powerline Technologies

• Background/expertise in communications and smart grid

• 51% owned by Fundamentals Ltd

• European business partner for Hubbell Aclara overhead line sensors

• Focus on distribution automation, fault detection & monitoring

• Previous Innovate UK projects

• Expertise in AI and machine/deep learning

Other Partners

• DNO funding partner

• Support in governance, collaboration

• Test network

SYNAPS Project

• Fault identification, classification and location

• Duration: 06/2018 to 09/2019

• Budget: £680k

• Project Organisation:

• Phase 1: PNDC testing

• Phase 2: Live network trial

Problem

Cable Faults

Number of LV Faults

Cable Faults

Cost of LV Faults

What is driving change?

• UK transition to a low carbon economy• 180,000 plug-in EVs

• 8.9 GW DG

• 1 GW battery storage

• Improving technology

• Political and regulatory climate

Solution - What

• Detect unusual waveforms

• Classify type of fault

• Predict location (~3m)

Solution - How

• Machine learning algorithm

• 100 kHz voltage sampling

• Abnormal readings sent for review

• Software classifies fault type and location

But how do you test it?

Cable damage from fault types

Type 1 Fault: Arcing, transient

0.50-5kA, 5-60ms duration

Type 2 Fault: Permanent

2-10kA peak, 100-500ms

duration

Results

Machine Learning

SYNAPS Phase 2 – In flight

• Test device on a live network

• Write procedure for use

• Risk assessment for new hardware

• Identify test circuits

• Compare results with traditional fault location technologies

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Questions?

Rona.Mitchell@ukpowernetworks.co.uk