A. Nesbitt

Triangulation and 3D location estimation of RFI and Partial Discharge sources within a 400kV substation

The 3D location of RFI (Radio Frequency Interference) and PD (Partial Discharge) sources within a 400kV substation are presented in this paper. The technique involves the employment of four small broadband mobile antennas attached to a fast digitizing scope. The time differences of arrival within a group of four received digitized signals are used to triangulate the 3D position of radiating RF sources. Results presented demonstrate the capability of the technique. Two specific locations at heights of around 7m and 9m pinpointed regions associated with the surrounding joint areas of a recently refurbished bus bar. Deformed inner cable cores were discovered at one of these locations. After remedial attention, cleaning and reconnection of the bus bar the RFI problems disappeared. Other RFI source locations were pinpointed, and surprisingly, some of these were related to nearby lighting.

A novel approach to high voltage substation surveillance using radio frequency interference measurement

The deployment of radio frequency interference (RFI) measurement has gained increasing acceptance as a front line, non-invasive technique to assess the condition of individual high-voltage (HV) electrical equipment items as part of a substation surveillance program. However, successful detection and discrimination of low-repetition rate discharges that typically accompany electrical deterioration is constrained by the capabilities and limitations of the field spectrum/RFI analysers used and the electro-magnetic interference (EMI) measurement techniques supported. This paper presents a novel approach to RFI measurement and assessment that is more sensitive to the RFI emissions that typically accompany electrical deterioration and provides more effective discrimination of the discharge phenomena from the ambient frequency spectrum and noise. Case studies from substation surveillance are presented to demonstrate the effectiveness of the approach. The technique is effective with a wide range of HV equipment and is less reliant on expert knowledge for a practicing engineer to confidently characterise and trace electrical deterioration with a high degree of confidence.

Quality assessment of BSc engineering honours projects at Glasgow Caledonian University

This paper discusses the quality assessment of final-year BSc Engineering Honours projects within the School of Engineering, Science and Design at Glasgow Caledonian University. The issues of objectives and outcomes, assessment instruments and tools, marking criteria, methods of good practice, honours classifications, awarding creativity and innovation, and addressing plagiarism are all discussed.

Optimal sampling rate for wavelet-based denoising in PD measurement

Extracting PD pulses from excessively noisy environment has been a crucial issue in PD on-line measurement. DWT as a powerful mathematical tool has proven its effectiveness in PD measurement denoising. The paper presents a method on how to achieve an improved denoising effect at an optimal sampling rate in DWT. It reveals the relationship between the sampling rate and the frequency band in wavelet analysis and contrasts the distribution of a PD signal and white noise along the decomposition levels. A set of 3 PD pulses produced by a narrow band detector immersed in white noise are processed at different sampling rates to verify the effectiveness of the proposed method.

Trending of EMI data over years and overnight

Trending data is a time proven technique to better analyze conditions, verify repairs, identify stable conditions or detect deterioration that may be developing. Often this comparison of data takes place over a long period of several weeks, months or years. Some types of generator system related conditions also follow load or operating temperatures. In these cases trending over a short period is needed to better identify conditions. This paper presents case studies of long term and short term trending of a generator high voltage system. Trending large motors is also presented.

Analysis of HV cable faults based on correlated HFCT and IEC60270 measurements

This paper presents simultaneous measurements using three methods of partial discharge detection on 15kV high voltage cable sections with artificially created fault geometries, namely a semiconductor point and a large and small metal particle embedded on the insulation. These faults are simultaneously investigated using two 20MHz bandwidth HFCTs and an IEC60270 system. The peak value of one of the HFCT signals is measured to produce φ-q-n pattern plots and the other is used to generate an HFCT electromagnetic interference (EMI) frequency spectra. IEC60270 φ-q-n patterns are also simultaneously produced for comparison. Measurements are synchronised in time and trended allowing comparisons between the methods to be investigated for changes in characteristic behaviour. Correlations between primary statistical parameters of the φ-q-n patterns and identified signature HFCT EMI frequency band changes are highlighted in order to show the key distinctive characteristics for the faults considered. It is also shown that by understanding and correlating simultaneous HFCT EMI spectra and HFCT peak φ--q-n pattern measurements it may be possible to determine, with some confidence, the nature of the fault rather than relying on a direct contact IEC60270 measurement system.

Partial discharge pattern classification of angled point-oil-pressboard degradation

This paper compares single network (SNN) and ensemble neural network (ENN) capabilities to recognize and distinguish surface discharges between two point-interface-pressboard arrangements with point angles of 100 and 450. The training fingerprints for both the SNN and ENN comprise statistical parameters from the measurement of the surface discharge patterns captured over a period of 15 hours. The results shows that there is minimal statistical variability for surface discharges from a 450 point-interface-pressboard angles in comparison to that of 100, which shows different behavior over a similar degradation period. In comparison to the widely applied SNN, the ENN also consistently provides improved recognition of PD patterns while the SNN actually shows improved discrimination potential between the two point-oil-pressboard degradation angle geometries.

Recognition of partial discharges using an Ensemble of Neural Networks

This paper introduces an improved method for classifying Partial Discharge (PD) patterns using Ensemble Neural Network (ENN) learning. The method is based on training several Neural Network (NN) models and combining their predictions. In this paper it is applied to the recognition of PD from artificially created poly-ethylene-terephthalate (PET) voids and in particular the ability of the ENN to categorise statistical Φ-q-n patterns for two different void sizes over 50 and 250 power cycles. The training data for the ENN comprises statistical parameters obtained from voids of 0.6mm and 1mm diameter. Measurements were made on three separately manufactured void samples for both these diameters. Similarities between the different PD measurements and different cycle captures is investigated using both a Single Neural Network (SNN) and the ENN. For each set of 3 void samples, each NN was trained and tested from the data of one PD void defect. Each NN was then tested using data from two other void geometries in order to determine the recognition abilities of both the ENN and SNN. The results show that the ENN always produces higher recognition efficiency for unseen data when compared to the SNN. It is also shown that ENN produces similar recognition predictions for PD patterns captured using either 50 or 250 power cycles while the SNN shows more sensitivity to the number of power cycles captured.

Change in pattern of void-type PD with ageing

The measurement and assessment of partial discharge (PD) activity is used both for condition assessment of high voltage plant (by operators) and for quality control (by manufacturers). Except for the online circumstances, the measurement usually lasts for a short period of time while higher than working stress is applied. The resulting pattern of discharge data, when compared with a knowledge base of PD patterns of different types of defects is a crucial factor in diagnosing the condition of the insulation system in the plant.

Discrepancies of PD patterns from a fixed metal object on oil-paper insulation

Partial discharge (PD) has long been thought to be one of the most important indications of the condition of high power plant, not only as quality assurance by manufacturers but also by plant operators during the service. Both types of measurement take place as a short period of activity during which, higher than working stress is applied. In the test regimes, the widely used phi-q-n pattern of discharge activity, when compared with existing PD characteristic databases from different defect forms, is crucial to determining the fault type and severity and in diagnosing the condition of the insulation system. The availability and validity such a database is essential and there have been unremitting efforts in both research and industrial applications. In this paper the characteristics of phi-q-n patterns for metal particles entrapped in oil-paper are extensively investigated using an IEC60270 PD measurement system. The test pieces are formed by wrapping a brass conductor with insulation paper, at the final layer of paper a spherical steel ball is included to simulate the trapped particle. The complete construct is immersed in oil so that a natural defect in a transformer is vividly simulated. Different values for the key factors are applied to reveal the resulted changes of phi-q-n pattern of discharge activity. Conventional criteria are calculated to characterize the different patterns. Oil samples for DGA are also collected, before and after each stressing period