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Manchester, United Kingdom

Asiri Y.,Saudi Aramco | Vouk A.,Saudi Aramco | Renforth L.,HVPD Ltd | Clark D.,University of Manchester | Neuralware J.C.,NeuralWare
2011 Electrical Insulation Conference, EIC 2011 | Year: 2011

This paper discusses the general application of using Neural Networks (NN) to classify six different types of Partial Discharge (PD). Stator winding failures contribute about 30-40% of the total motor failures according to IEEE and EPRI. Ninety percent (90%) of electrical failures on High-Voltage (HV) equipment are related to insulation deterioration. Large datasets were collected for motors with PD defects as well as PD-free machines. The datasets of PD were pre-processed and prepared for use with a NN using statistical means. It was possible to utilise the advantages offered by multiple NN models to classify the PD defects with a maximum recognition rate of 94.5% achieved, whereas previous research work did not exceed a classification accuracy of 79%. © 2011 IEEE. Source

Giussani R.,HVPD Ltd | Renforth L.,HVPD Ltd | Seltzer-Grant M.,HVPD Ltd | Zachariades C.,University of Manchester
33rd Electrical Insulation Conference, EIC 2015 | Year: 2015

A new holistic technique for the combined electrical and mechanical condition monitoring (CM) of high voltage (HV) rotating machines has been developed. It is mainly aimed at the oil and gas sector where machine failure can potentially have catastrophic consequences as well as a huge financial cost. The technique combines partial discharge (PD) monitoring, motor current signature analysis (MCSA), power quality (PQ) monitoring and vibration monitoring. The sensors employed include a newly developed tri-band sensor for online partial discharge (OLPD) detection, PQ and MCSA measurements as well as accelerometers for vibration monitoring. The sensors and monitoring modules are complemented by a CM database for trending and benchmarking purposes. Case studies from oil and gas customers emphasize the importance of using a permanent, continuous, combined monitoring solution. © 2015 IEEE. Source

Renforth L.A.,HVPD Ltd | Armstrong R.,Tengizchevroil | Clark D.,University of Manchester | Goodfellow S.,HVPD Ltd | Hamer P.S.,High Voltage Partial Discharge Ltd.
IEEE Industry Applications Magazine | Year: 2014

In this article, a new technique is presented for the remote online partial discharge (OLPD) monitoring of the stator insulation condition of in-service, high-voltage (HV) rotating machines with a focus on the Ex/ATEX HV motors used in the petrochemical industry. The technique applies wideband, ferrite-based high-frequency current transformer (HFCT) sensors and high-resolution measurement technology. This remote PD monitoring technique has significant advantages when monitoring motors located in Ex/ATEX hazardous gas zones in oil and gas and petrochemical facilities. © 1975-2012 IEEE. Source

Renforth L.A.,HVPD Ltd | Hamer P.S.,Chevron | Clark D.,University of Manchester | Goodfellow S.,HVPD Ltd | Tower R.,Tengizchevroil
IEEE Transactions on Industry Applications | Year: 2015

This paper presents results from the continued application of a new technique for the remote online partial discharge (OLPD) testing and monitoring of in-service high-voltage (HV) explosive atmosphere (Ex)/atmosphere-explosive (ATEX) motors operating in hazardous gas zones. The technique employs high-current wideband high-frequency current transformer sensors located remotely from the motor under test, at the switchboard end of the HV feeder cable. Significant cost and operational benefits can be gained from this remote monitoring technique as the stator winding condition of these motors can be monitored without having to enter the hazardous gas (Ex/ATEX) zone. The work described in this paper is a continuation of that carried out by the same authors, as reported at the 2012 IEEE Petroleum and Chemical Industry Technical Conference, and includes the development of proposed condition guidelines for assessing the condition of the stator winding insulation systems of large populations of aged HV motors, based on an OLPD "league-table" database. Two case studies are presented wherein the HV stator insulation condition and reliability of in-service motors, operating in hazardous gas zones, and HV generators were assessed using both online and offline partial discharge (PD) testing and monitoring techniques. The case studies emphasize the importance of carrying out extended continuous OLPD monitoring of in-service rotating HV machines to detect underlying trends in PD activity over time. This paper concludes with how condition monitoring data can support reliability-centered maintenance and condition-based management regimes. © 1972-2012 IEEE. Source

Seltzer-Grant M.,HVPD Ltd | Renforth L.,HVPD Ltd | Djokic S.,University of Edinburgh | McKeever P.,Narec
IET Conference Publications | Year: 2013

The authors present a paper detailing their experiences (over the past 5 years) in the insulation condition monitoring of land-sea export cables and subsea array and platform interconnection cables in the UK offshore renewables and oil & gas industries. The paper focusses on the use of partial discharge (PD) testing to detect and locate pre-fault PD activity and also the use of time domain reflectometery (TDR) techniques to provide a 'fingerprint' of the cables and to help pinpoint cable faults after they have occurred. The paper will also discuss a 'holistic' monitoring approach, combining a number of condition monitoring technologies, to achieve necessary information to support improved CBM. For the HV networks, power quality monitoring should be integrated with the monitoring of partial discharge (PD), cable sheath current and weather data (i.e. wind, wave or tidal energy resources, power flows and occurrence of overvoltage/overcurrent conditions), with the further cross-correlation of all of these condition and state parameters, in order to allow for the early detection, faster identification and efficient resolving of faults, failures and other problems during the operation. The purpose of such an integrated monitoring platform is therefore, to provide an advanced 'early warning' against insulation faults to enable planning of preventive or corrective maintenance to be carried out and to avert unplanned outage. Source

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