Development of an integrated low-power RF partial discharge detector

This paper presents the results from integrating a low-power partial discharge detector with a wireless sensor node designed for operating as part of an IEEE 802.15.4 sensor network, and applying an on-line classifier capable of classifying partial discharges in real-time. Such a system is of benefit to monitoring engineers as it provides a means to exploit the RF technique using a low-cost device while circumventing the need for any additional cabling associated with new condition monitoring systems. The detector uses a frequency-based technique to differentiate between multiple defects, and has been integrated with a SunSPOT wireless sensor node hosting an agent-based monitoring platform, which includes a data capture agent and rule induction agent trained using experimental data. The results of laboratory system verification are discussed, and the requirements for a fully robust and flexible system are outlined

UHF diagnostic monitoring techniques for power transformers

This paper initially gives an introduction to ultra-high frequency (UHF) partial discharge monitoring techniques and their application to gas insulated substations. Recent advances in the technique, covering its application to power transformers, are then discussed and illustrated by means of four site trials. Mounting and installation of the UHF sensors is described and measurements of electrical discharges inside transformers are presented in a range of formats, demonstrating the potential of the UHF method. A procedure for locating sources of electrical discharge is described and demonstrated by means of a practical example where a source of sparking on a tap changer lead was located to within 15 cm. Progress with the development of a prototype on-line monitoring and diagnostic system is reviewed and possible approaches to its utilization are discussed. New concepts for enhancing the capabilities of the UHF technique are presented, including the possibility of monitoring the internal mechanical integrity of plant. The research presented provides sufficient evidence to justify the installation of robust UHF sensors on transformer tanks to facilitate their monitoring if and when required during the service lifetime

A frequency-based RF partial discharge detector for low-power wireless sensing

Partial discharge (PD) monitoring has been the subject of significant research in recent years, which has given rise to a range of well-established PD detection and measurement techniques, such as acoustic and RF, on which condition monitoring systems for highvoltage equipment have been based. This paper presents a novel approach to partial discharge monitoring by using a low-cost, low-power RF detector. The detector employs a frequency-based technique that can distinguish between multiple partial discharge events and other impulsive noise sources within a substation, tracking defect severity over time and providing information pertaining to plant health. The detector is designed to operate as part of a wireless condition monitoring network, removing the need for additional wiring to be installed into substations whilst still gaining the benefits of the RF technique. This novel approach to PD detection not only provides a low-cost solution to on-line partial discharge monitoring, but also presents a means to deploy wide-scale RF monitoring without the associated costs of wide-band monitoring systems

Integrating an agent-based wireless sensor network within an existing multi-agent condition monitoring system

The use of wireless sensor networks for condition monitoring is gaining ground across all sectors of industry, and while their use for power engineering applications has yet been limited, they represent a viable platform for next-generation substation condition monitoring systems. For engineers to fully benefit from this new approach to condition monitoring, new sensor data must be incorporated into a single integrated system. This paper proposes the integration of an agent-based wireless sensor network with an existing agent-based condition monitoring system. It demonstrates that multi-agent systems can be extended down to the sensor level while considering the reduced energy availability of low-power embedded devices. A novel agent-based approach to data translation is presented, which is demonstrated through two case studies: a lab-based temperature and vibration monitoring system, and a proposal to integrate a wireless sensor network to an existing technology demonstrator deployed in a substation in the UK

Data management of on-line partial discharge monitoring using wireless sensor nodes integrated with a multi-agent system

On-line partial discharge monitoring has been the subject of significant research in previous years but little work has been carried out with regard to the management of on-site data. To date, on-line partial discharge monitoring within a substation has only been concerned with single plant items, so the data management problem has been minimal. As the age of plant equipment increases, so does the need for condition monitoring to ensure maximum lifespan. This paper presents an approach to the management of partial discharge data through the use of embedded monitoring techniques running on wireless sensor nodes. This method is illustrated by a case study on partial discharge monitoring data from an ageing HVDC reactor

Fault location and diagnosis in a medium voltage EPR power cable

This paper presents a case study on fault location, characterization and diagnosis in a length of shielded 11 kV medium voltage ethylene-propylene rubber (EPR) power cable. The defect was identified on-site as a low resistance fault occurring between the sheath and the core. A 43 m section was removed for further analysis. The fault resistance was characterized and the location of the defect pinpointed to within a few cm using a combination of time-difference-of-arrival location and infra-red imaging. A combination of X-ray computed tomography, scanning electron microscopy and energy dispersive X-ray spectroscopy were then applied to characterize any abnormalities in the dielectric surrounding the breakdown region. A significant number of high density contaminants were found to be embedded in the dielectric layer, having an average diameter of the order of 100 um, a maximum diameter of 310 um and an average density of 1 particle per 2.28 mm3 . Scanning electron microscopy and energy-dispersive X-ray spectroscopy were used to determine the geometry and elemental composition of some initial contaminant samples. It was concluded that contamination of the EPR layer, combined with an observed eccentricity of the cable’s core and sheath resulting in a reduced insulation gap, may have led to an electric field concentration in the region of the defect sufficient to initiate breakdown. Preventative strategies are discussed for similar families of cables, including more stringent dielectric testing requirements at the manufacturing stage and PD monitoring to detect incipient failure

Embedded intelligence for electrical network operation and control

Integrating multiple types of intelligent, mulitagent data analysis within a smart grid can pave the way for flexible, extensible, and robust solutions to power network management

Rough set theory applied to pattern recognition of partial discharge in noise affected cable data

This paper presents an effective, Rough Set (RS) based, pattern recognition method for rejecting interference signals and recognising Partial Discharge (PD) signals from different sources. Firstly, RS theory is presented in terms of Information System, Lower and Upper Approximation, Signal Discretisation, Attribute Reduction and a flowchart of the RS based pattern recognition method. Secondly, PD testing of five types of artificial defect in ethylene-propylene rubber (EPR) cable is carried out and data pre-processing and feature extraction are employed to separate PD and interference signals. Thirdly, the RS based PD signal recognition method is applied to 4000 samples and is proven to have 99% accuracy. Fourthly, the RS based PD recognition method is applied to signals from five different sources and an accuracy of more than 93% is attained when a combination of signal discretisation and attribute reduction methods are applied. Finally, Back-propagation Neural Network (BPNN) and Support Vector Machine (SVM) methods are studied and compared with the developed method. The proposed RS method is proven to have higher accuracy than SVM and BPNN and can be applied for on-line PD monitoring of cable systems after training with valid sample data

Electrical stress monitoring of distribution transformers using smart grid techniques

Electrical stresses that distribution transformers rated 16 kVA up to 2 MVA are subjected to can often cause premature transformer failures. In this study, research related to the development of cost effective bushing embedded sensors that can measure the electrical stresses on the MV side of distribution transformers has been conducted. An embedded screen in a specially designed 24 kV bushing was used for both power frequency and transient voltage measurements. Observed results showed that the screen-based bushing capacitive voltage divider offered results that are consistent with those of a commercial capacitive voltage divider for power frequency voltages as low as 1 kV up to 24 kV. Impulse voltage measurements were consistent with those of a wideband resistive divider for voltages lower than 60 kV. Voltages higher than 60 kV revealed non-linear behaviour which increases as the 150 kV BIL rating of a 22 kV transformer is reached. A nonlinear resistor added to ATPdraw simulations was able to compensate for the observed nonlinearity. PD tests conducted on the prototype bushing showed that the designed prototype had surface discharges which are affected by the positioning of the bushing screen. A Rogowski coil embedded in the same bushing was used for the measurement of both power frequency and transient currents. Measured coil parameters used in ATPdraw simulations produced results that were consistent with the output of the Rogowski coils when measuring 8/20 s current impulses. Numerical integration of the Rogowski coil output voltages was successfully used in the recovery of both power frequency and measured impulse currents. The Rogowski coil sensitivity is affected by both coil dimensions and terminating resistance. The designed prototype bushing opens up opportunities for performing stress monitoring on the MV side of distribution transformers