Projects: Projects for Investigator
Reference Number NIA_UKPN0037
Title SYNAPS Fault Detection, Classification & Location Solution
Status Completed
Energy Categories Other Power and Storage Technologies(Electricity transmission and distribution) 100%;
Research Types Applied Research and Development 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 20%;
ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 80%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Project Contact
No email address given
UK Power Networks
Award Type Network Innovation Allowance
Funding Source Ofgem
Start Date 01 June 2018
End Date 01 December 2019
Duration ENA months
Total Grant Value £679,854
Industrial Sectors Power
Region London
Programme Network Innovation Allowance
Investigators Principal Investigator Project Contact , UK Power Networks (100.000%)
  Other Investigator Project Contact , UK Power Networks (100.000%)
  Industrial Collaborator Project Contact , UK Power Networks (0.000%)
Web Site
Objectives The SYNAPS (synchronous analysis and protection system) solution will be deployed in substations and feeder link-boxes or feeder pillars. It applies innovative algorithms to power waveforms in order to detect and classify fault events.SYNAPS uses state-of-the-art advanced statistical signal processing and machine learning algorithms to identify unique features of LV feeder cable faults (including early transient so called pecking faults). A high sample rate detector is then employed to identify faults, when a manifesting fault is detected the sensor records the fault waveform and transmits to the server software for further processing. The server software classifies fault type and location (target accuracy 3m) utilising Powerline Technologies (PLT) proprietary algorithms.Existing solutions use voltage/current analysis that give an approximate location of the fault, which is then pinpointed with gas sniffers and thermal cameras. These techniques are mainly used for permanent faults and the equipment; when used en masse is expensive.SYNAPS could enable DNOs to make significant reductions in the cost of LV network operation, replacing expensive and manpower intensive operations with automated procedures. The detection and location of faults at an early stage, before they become permanent, will facilitate proactive planned maintenance, rather than expensive reactive emergency action. Faults can be detected, classified and located before a fuse failure, giving the DNO the opportunity to repair the fault before or immediately after the first fuse failure. The project will seek to validate and trial the SYNAPS LV fault detection, classification and location solution. The project will cover technology validation and demonstration on an operational network taking the solution from TRL4 to TRL6.Stage 1: Technology validation (TRL5 Technology Demonstration)Stage 2: Prototype design for testing on the LV network. SYNAPS demonstration in a working environment (TRL6 Technology Demonstration) Stage 1 Objectives SYNAPS Technology Validation at Power Networks Demonstration Centre (PNDC) (TRL5)Demonstrate a two-unit SYNAPS systemTest SYNAPS technology at PNDCValidate technology and prove it can detect, classify and locate faults to required level of performanceCollect and analyse data to facilitate improvements in algorithmsCollect and analyse data to facilitate future improvement of LV network simulation model Preparation for Stage 2Stage 2 Objectives Demonstration of SYNAPS operation in a working environment on UKPN network (TRL6) Enhance SYNAPS algorithms and demonstration system based on PNDC reportUnderstand network interface/connection requirementsTwo pairs of sensors will be delivered to the participating DNOs (one set for UK Power Networks & one set for SSEN) for operation on actual LV network Design concept and specification for link box/substation sensor (including transducer)Document the cable calibration procedureEnhancement of LV network simulation model to include cable calibrationDemonstration of prototype SYNAPS sensors on feeder(s) chosen jointly with UK Power Networks and SSEN (detection, classification and location of faults on feeders with known issues) installation for duration of demonstration only and supported by personnel from the manufacturer and the DNOs. -Incudes detailed site surveys to establish optimal locations for prototype SYNAPS system -Deployment of prototype SYNAPS system on problematic feeders with known faults -SYNAPS will be used in conjunction with traditional fault location instruments, such as TDR and cable sniffers, in order to validate location results when faults become permanent
Abstract Many faults on the low voltage (LV) network are caused by gradual degradation of underground feeder cables. As the cables age/insulation layers gradually break down allowing the ingress of moisture, which starts to cause momentary short circuits between the conductors. This causes an arc which often vaporises the water and clears the fault. These faults are known as transient faults, and they are invisible to the DNOs and customers. As the cables degrade further the arc current may be sufficient to cause a fuse to blow, causing a power cut. If the fuse is replaced, the fault will appear to have cleared. However, the underlying fault will remain, meaning that the fuse will blow again, giving rise to an intermittent fault. Eventually fuse replacement will not clear the fault and the fault becomes permanent.Current DNO practice is mostly reactive, with faults only becoming visible when they are reported by customers. Standard practice is then to replace the fuse, if the fault is not cleared then technologies such as Time Domain Reflectometry (TDR) are employed to locate the fault. These faults account for a significant proportion of LV network costs and Customer Minutes Lost (CML). To technically and economically improve the performance of the LV network, there is a requirement to move from reactive to proactive management of LV faults.
Publications (none)
Final Report (none)
Added to Database 14/12/22