Scalable Non-invasive Radiometric Wireless Sensor Network for Partial Discharge Monitoring in the Future Smart Grid

Lead Research Organisation: University of Huddersfield
Department Name: Sch of Computing and Engineering


Partial discharge (PD) refers to an electrical spark that does not completely bridge the space between the conductors causing it. It occurs in degraded electrical insulation and its occurrence is known to be characteristic of insulation defects in the high-voltage (HV) components (transformers, switchgear, cables, etc) of the electrical grid. Such PD results in the radiation of short pulses of electromagnetic energy extending over a wide band of radio frequencies. The detection and location of such radiated signals and, in particular, the careful tracking of changes in their intensity can thus be used to monitor the health of HV equipment. There is an immediate economic case for ubiquitous and continuous monitoring of PD intensity throughout the power system to realise an early warning system for equipment failure. This case rests on the fact that by monitoring radiated PD signals: (i) a compromised item of plant item can be de-rated or replaced to avoid catastrophic failure by rerouting network energy flows, (ii) routine maintenance can be replaced with condition- or risk-based maintenance, and (iii) de-rating or replacement of aging plant can be postponed until demonstrably necessary. One component of this project is to develop and deploy a network of free-standing, non-invasive, wireless sensors that will use these signals to cooperatively locate sources of PD and monitor their evolution. The resulting space-time map of changing PD intensity will provide system operators with a real-time picture of equipment health.

The radiometer network described above will map gross PD intensity and provide a simple, but robust, early warning of plant failure. The detailed character of a PD signal (its time waveform, frequency spectrum and statistical behaviour) carries more detailed information about the nature of the insulation defect producing it than PD intensity alone. This has been demonstrated in the context of invasive sensors requiring contact with plant items. The relationship between the nature of insulation defects and the character of the resulting PD signal received by free-standing sensors is, however, currently obscure. A second component of this project is to investigate this relationship by developing and deploying a smaller number of specially designed radio receivers capable of extracting a broad range of signal characteristics along with the radiometers (intensity sensors) described above. Over the course of the project, as insulation defects are diagnosed by the power system operator in the normal way (including forensic examination of failed items of plant), the nature of specific insulation defects will be correlated with the characteristics of the observed PD signal. A programme of opportunistic measurements of PD signals obtained by transporting a free-standing portable PD receiver to any substation identified as having a significant insulation defect or PD source will accelerate the collection of fault-specific PD data. Automatic signal processing and data analysis routines will be developed and used to identify those signal characteristics providing the best discrimination between insulation defect types. Spatially resolved, real-time, information about the health status of the grid, including information about the nature and severity of incipient faults, raises the possibility of the self-diagnosing (and ultimately self-healing) grid.

As an integrated part of the future 'smart grid', ubiquitous and continuous PD monitoring will allow routing of energy to be dynamically optimised to minimise any selected cost metric. Such a cost metric might, for example, include the monetary and environmental cost of transmission losses (including CO2 emissions), the cost of maintenance and/or replacement of plant, and the cost (economic and social) of supply interruptions. This project is thus an important component in the vision of the self-optimising grid.

Planned Impact

The wider beneficiaries of the proposed work will include:

(i) the electricity supply industry - In the short term (< 7 years) radiometer networks measuring PD intensity variation will allow the replacement of routine/planned maintenance with condition-based maintenance, the continued (confident) use of aging assets, the delayed replacement of aging assets, a reduced risk of catastrophic equipment failure, and fewer customer minutes lost (CMLs).

(ii) electricity consumers - In the short term consumers will benefit from greater supply reliability and lower (or less rapidly increasing) electricity prices due to the less urgent need to replace aging plant. In the longer term (>10 years), further savings will be made due to automation and the techno-economic optimisation of the transmission/distribution network made possible by a self-diagnosing, self-healing, self-optimising smart grid. This project will accelerate progress towards the realisation of the smart grid facilitating the earlier, and more complete, realisation of generic smart grid benefits (smart metering, smart loads etc) and the corresponding development of more sophisticated and flexible tariff structures.

(iii) WSN manufacturers and suppliers - The large market represented by the UK, European and worldwide electricity networks for the free-standing PD-WSN proposed will be of direct benefit to this commercial sector.

(iv) Policy makers, regulators, Government and society - In the long term the self-optimising grid (of which the self-diagnosing grid is a prerequisite) will allow complex socio-economic trade-offs between diverse cost metrics (grid efficiency/CO2 emissions, cost of plant replacement, maintenance cost of existing plant, probable number and duration of supply interruptions etc) to be facilitated. The weights chosen for the different elements in the formula describing an overall cost function will be a powerful tool for realising economic, social and political objectives.


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Iorkyase E (2019) Improving RF-Based Partial Discharge Localization via Machine Learning Ensemble Method in IEEE Transactions on Power Delivery

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Jaber A (2017) Calibration of free-space radiometric partial discharge measurements in IEEE Transactions on Dielectrics and Electrical Insulation

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Mohamed H (2019) Partial Discharge Detection and Localization: Using Software-Defined Radio in IEEE Industrial Electronics Magazine

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Upton D (2018) Low power radiometric partial discharge sensor using composite transistor-reset integrator in IEEE Transactions on Dielectrics and Electrical Insulation

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ZHANG Y (2017) A compact wideband printed antenna for free-space radiometricdetection of partial discharge in TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES

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Zhang Y (2015) Radiometric Wireless Sensor Network Monitoring of Partial Discharge Sources in Electrical Substations in International Journal of Distributed Sensor Networks

Description A wireless sensor network for the monitoring and location of partial discharge in electricity substations based on incoherent detectors is extremely challenging but has been successfully realised. Node sensitivity has been the critical challenge. The use of logarithmic detectors has been found to be essential to simultaneously satisfy both dynamic range and sensitivity constraints. Analogue signal processing has been found necessary to remove coherent interference to the PD signals being detected by the sensor nodes. Integration of the detected partial discharge signal has been used to collapse the bandwidth of the signal allowing a lower sampling rate than would otherwise be required, thereby reducing ADC power drain and increasing sensor node battery-life. The technology that has been developed has been proven, using both indoor and outdoor trials, to locate partial discharge accurately. A paradigm shifting advance has been the demonstration that useful estimates of absolute partial discharge intensity (i.e. apparent charge in pC) can be measured radiometrically. Prior to this advance only relative partial discharge intensities were thought to be measurable wirelessly and the prediction of imminent insulation failure was based solely on the time derivative of discharge intensity.
Exploitation Route The wireless sensor network is capable of continuous partial discharge monitoring and location in electricity substations and will allow condition-based maintenance of plant. It will extend the life of aging plant without undue risk of catastrophic plant failure. Whilst the techniques and technology developed have been primarily focused on electricity supply infrastructure they will also find application in the aerospace, defence and marine industries since partial discharge monitoring using wireless sensor networks could be applied in any high voltage environment. The discovery that a useful estimate of absolute partial discharge intensity can be made by a sensor network of the type that has been developed will enhance the utility of these networks very significantly.
Sectors Aerospace, Defence and Marine,Energy

Description The Partial Discharge Wireless Sensor Network (PD WSN) technology developed under this grant has been deployed by TATA Steel in their Port Talbot plant for evaluation purposes since November 2017. The success of these evaluation trials is reported in an article 'Great Vision on Show in New Sensor Tests' appearing in TATA Steel's fortnightly journal 'Delivering Our Future' issue 261, 25 January 2018. Negotiations are currently underway for a research contract between the University of Huddersfield and TATA Steel to industrially harden the PD WSN so it can be deployed permanently across Port Talbot plant and, perhaps, rolled out over other TATA plants. Drallim Industries, a company providing commercial condition monitoring equipment, has approached the University of Huddersfield with a view to commercializing the PD WSN for application across a variety of industrial sectors operating HV equipment.
First Year Of Impact 2018
Sector Energy
Impact Types Economic

Description Drallim Industries 
Organisation Drallim Industries Ltd
Country United Kingdom 
Sector Private 
PI Contribution Drallim Industries has approached the University of Huddersfield with a view to collaborating in commercialisation of the technology developed under this grant.
Collaborator Contribution Drallim Industries has approached the University of Huddersfield with a view to collaborating in commercialisation of the technology developed under this grant.
Impact Outcomes are restricted to negotiations which are ongoing.
Start Year 2017
Description TATA Steel 
Organisation TATA Steel
Country India 
Sector Private 
PI Contribution TATA Steel have hosted extensive industrial trialing of the Partial Discharge Wireless Sensor Network (PD WSN). The trials revealed partial discharge activity in one of steel works substations. The detected activity ceased immediately after planned maintenance of the HV switch-gear. This suggests strongly that the PD activity was caused by surface contamination of the switch-gear contacts which may lead to planned maintenance being replaced, in part or in full, with condition-based maintenance. Such a change would bring a concomitant economic benefit.
Collaborator Contribution The trialing of a novel technology by an industrial partner in a realistic industrial environment is crucial to the building of credibility and industry confidence in the technology. The extensive trials in the Port Talbot steel works is central to the journey from university lab to commercial product.
Impact The field trial at port Talbot will result in further academic publications and has already resulted in an article in the internal TATA Steel journal. This publicity has led to a direct approach to from Drallim Industries wishing to investigate the possibility of collaborating with University of Huddersfield in developing a fully commercial product for sale across a wide variety of HV-related industry sectors.
Start Year 2017
Description UFCG Partial Discharge 
Organisation Federal University of Campina Grande
Country Brazil 
Sector Academic/University 
PI Contribution The University of Huddersfield team has coordinated the project, conceptualised a solutution, designed, developed and tested hardware and software in the realisation of this solution.
Collaborator Contribution The Univsrsidade Federal de Campina Grande (UFCG) team has developed the human interface for the sensor network. This involved contributing time and specialist development facilities in the Human Machine Interface Research Laboratory at UFCG as follows: (i) two undergraduate students have progressed part of the Human Interface for the PD WSN in the context of their final year projects, (ii) a doctoral student (recently appointed as a lecturer in another Brazilian institution) has taken part in the development of the sensor network supervisory system. UFCG have thus conributed a supervisory system which intefaces the PD sensor network to a database and itegrates the database with the sensor network and a human interface to the sensor network allowing real-time data visualisation and system state monitoring. In addition to the value of UG and research student contributions, Professor Vieira's personal contribution includes approximately 500 hours nominally broken down as follows: four 2-week visits to the UK (distributed over four years), (ii) five hours per month over four years whilst not in the UK UFCG have also contributed the use of their specialist Human Interface evelauation facilities.
Impact Software for integration in to the PD WSN and multiple research publications.
Start Year 2013
Description University of Strathclyde Partial Discharge 
Organisation University of Strathclyde
Department Department of Pure and Applied Chemistry
Country United Kingdom 
Sector Academic/University 
PI Contribution The team at the University of Huddersfield have led and coordinated the research, conceptualised and specified the system, developed, tested and deployed the sensor hardware and leading field trials.
Collaborator Contribution The team at the University of Strathclyde has developed real-time location algorithms to establish the source of partial discharge, provided access to measurement and field trial facilities, and loaned specialist laboratory test equipment.
Impact Multiple publications
Start Year 2013