Partial discharge detection and location for HVDC polymeric cables

This poster is concerned with use of partial discharge monitoring to provide information about the condition of the insulation of electrical cables used for HVDC transmission systems. Electrical cables are among the most fundamental components of any electrical grid, from large subsea international interconnectors, to the ‘last mile’ providing consumers with their electrical supply. The size, cost and current carrying capability are the main considerations when designing and selecting a cable, and in this regard the insulation of these cables is as fundamental as the conductor. Partial discharge (PD) measurement is becoming increasingly vital in monitoring the condition of cable insulation, providing valuable information about the health of the insulation, and predicting when insulation is likely to fail. The majority of this PD monitoring is performed on cable operating under AC conditions, however, with the increasing use of high voltage DC links, for subsea, or long land-based connections provides motivation for the increased use of PD monitoring on cables operating under HVDC. However, despite the increased intensity of research into PD in HVDC cables, there are significant knowledge gaps, preventing the practical application of PD monitoring techniques to HVDC cables. This poster describes the initial stages of a project to partially address these gaps in knowledge, by seeking to obtain results from PD measurements on cables of different insulation types under both AC and DC conditions. From this, recommendations on the use of PD monitoring for HVDC cables, with emphasis on insulation type, are will be provided, as well as recommendations for future research at both an academic and industrial level. The poster will detail the results of the initial literature review, as well as the design for the planned experimentation, and test rig

Pre-determination of partial discharge inception voltage in power cables using electrode gaps in air under AC voltage

The breakdown of insulation in cables while in service can cause considerable damage to equipment and the accessories to which they are connected. PD in cables arises due to the overstressing of cable insulation resulting from electric field enhancement caused by imperfections in cable core and screen. The nature and magnitude of PD activity depends upon the type of defect, aging, environmental factors, applied voltage and cable loading. Reduction in system voltage can potentially reduce PD, which will correspondingly extend the service life of the cable. Currently, industry voltage statutory requirements permit ±6% tolerance setting on nominal voltage on distribution networks. This ±6% voltage reduction on may have little or an adverse effect on PD magnitude depending on the nature of defect present in the cable. Hence there is a clear requirement to pre-determine the PD inception voltage in cables through laboratory experiments to understand the significance of voltage reduction. This means to verify the effect of voltage reduction on extinguishing or minimizing PD activity in cables. In this paper, range of voltages at which PD incepts termed as partial discharge inception voltage(PDIV) is measured using a test cell containing different types of electrode configuration having different spacing. PDIV measured using the test cell is verified by conducting partial discharge testing in paper insulated lead covered (PILC) and cross-linked poly ethylene(XLPE) cables. It has been found that PDIV measured using the test cell and cable are in good agreement

Methods of characterisation of DC partial discharge in polymeric cable insulation

Partial discharge monitoring is frequently used in AC cable systems, and there exists a strong desire for the same for DC cables within the electrical power industry, given their recent increased use. However, DC PD is a less well understood phenomenon. This paper provides analysis of three methods of partial discharge characterisation: pulse duration and amplitude analysis, frequency-domain spectra analysis, and partial discharge inception voltage analysis in artificially-created voids in polymeric cable insulation samples (polyethylene and polypropylene) under both AC and positive and negative DC excitations. From these a ‘finger-print’ of the defects can be determined based on the distribution of energy within the discharge frequency spectrum, and the inception voltage

Evolution of electricity distribution control room data streams - UK case study

The move towards 'Net Zero', the balance of greenhouse gas emissions produced and removed from the atmosphere, is increasing the complexity of managing distribution networks, with more deployments of low carbon technologies, new markets for flexibility services, and greater monitoring and visibility of the network itself. This has driven the UK's Distribution Network Operators who manage the network, to evolve towards becoming Distribution System Operators, taking on a role more akin to the traditionally more complex Transmission System Operator. In parallel with this development, the data streams utilised by the distribution network control room have also evolved. This paper sets out this evolution by detailing the existing data streams within a typical distribution control room, and how these data streams will need to evolve, as well what new sources of data a future control room will need to accommodate, and finally by identifying novel ways that that value can be created from this data through the use of new 'smart' tools

A novel wavelet selection scheme for partial discharge signal denoising

Over the past two decades, wavelet-based techniques have been widely used to extract partial discharge (PD) signals from noisy signals. To effectively select the correct technique to minimize the effect of noise on PD detection, three aspects are considered: wavelet selection, decomposition scale, and noise or threshold estimation. For wavelet selection, popular techniques, including correlation-based wavelet selection scheme (CBWSS) and energy-based wavelet selection scheme (EBWSS), are applied to select an appropriate wavelet basis function. These two schemes, however, have their limitations. CBWSS is not as effective as expected when the signal to noise ratio (SNR) is very low. EBWSS selects the optimal wavelet that can maximize the energy ratio of the PD signal in approximation coefficients through wavelet decomposition. It is not strictly true for damped oscillating PD signals, particularly when the decomposition scale increases. As such, a novel wavelet selection scheme, wavelet entropy-based wavelet selection scheme ( WEBWSS), is proposed to provide an alternative to CBWSS and EBWSS for PD denoising. PD signals are simulated and also obtained through laboratory experiments to demonstrate that this new method has better performance in the removal of noise, particularly when SNR is low

Incorporating forecasting and peer-to-peer negotiation frameworks into a distributed model predictive control approach for meshed electric networks

The continuous integration of renewable energy sources into a power network has caused a paradigm shift in energy generation and distribution. The intermittent nature of renewable sources affects the prices at which energy can be sold or purchased. In addition, the network is subject to operational constraints, voltage limits at each node, rated capacities for the power electronic devices, current bounds for distribution lines; these constraints coupled with intermittent renewable injections may pose a threat to system stability and performance. We propose a distributed predictive controller to handle operational constraints while minimising generation costs, and an agent based market negotiation framework to obtain suitable pricing policies, agreed among participating agents, that explicitly considers availability of energy storage in its formulation. The controller handles the problem of coupled constraints using information exchanges with its neighbours to guarantee their satisfaction. We study the effect of different forecast accuracy have on the overall performance and market behaviours. We provide a convergence analysis for both the negotiation iterations, and its interaction with the predictive controller. Lastly, We assess the impact of the information availability with the aid of testing scenarios

Incorporating forecasting and peer-to-peer negotiation frameworks into a distributed model-predictive control approach for meshed electric networks

The continuous integration of renewable energy sources into power networks is causing a paradigm shift in energy generation and distribution with regard to trading and control. The intermittent nature of renewable sources affects the pricing of energy sold or purchased. The networks are subject to operational constraints, voltage limits at each node, rated capacities for the power electronic devices, and current bounds for distribution lines. These economic and technical constraints, coupled with intermittent renewable injection, may pose a threat to system stability and performance. In this article, we propose a novel holistic approach to energy trading composed of a distributed predictive control framework to handle physical interactions, i.e., voltage constraints and power dispatch, together with a negotiation framework to determine pricing policies for energy transactions. We study the effect of forecasting generation and consumption on the overall network's performance and market behaviors. We provide a rigorous convergence analysis for both the negotiation framework and the distributed control. Finally, we assess the impact of forecasting in the proposed system with the aid of testing scenarios

WHIRLY1 functions in the nucleus to regulate barley leaf development and associated metabolite profiles

The WHIRLY (WHY) DNA/RNA binding proteins fulfil multiple but poorly characterised functions in leaf development. Here, we show that WHY1 transcript levels were highest in the bases of 7-day old barley leaves. Immunogold labelling revealed that the WHY1 protein was more abundant in the nuclei than the proplastids of the leaf bases. To identify transcripts associated with leaf development we conducted hierarchical clustering of differentially abundant transcripts along the developmental gradient of wild-type leaves. Similarly, metabolite profiling was employed to identify metabolites exhibiting a developmental gradient. A comparative analysis of transcripts and metabolites in barley lines (W1–1 and W1–7) lacking WHY1, which show delayed greening compared with the wild type revealed that the transcript profile of leaf development was largely unchanged in W1–1 and W1–7 leaves. However, there were differences in levels of several transcripts encoding transcription factors associated with chloroplast development. These include a barley homologue of the Arabidopsis GATA transcription factor that regulates stomatal development, greening and chloroplast development, NAC1; two transcripts with similarity to Arabidopsis GLK1 and two transcripts encoding ARF transcriptions factors with functions in leaf morphogenesis and development. Chloroplast proteins were less abundant in the W1–1 and W1–7 leaves than the wild type. The levels of tricarboxylic acid cycle metabolites and GABA were significantly lower in WHY1 knockdown leaves than the wild type. This study provides evidence that WHY1 is localised in the nuclei of leaf bases, contributing the regulation of nuclear-encoded transcripts that regulate chloroplast development

A novel wavelet selection scheme for partial discharge signal detection under low SNR condition

Wavelet-based techniques have been widely used to extract partial discharge (PD) signals from noisy signals. Generally, the procedure consists of 3 steps: wavelet selection, decomposition scale determination, and noise estimation. Wavelet selection is the first and most important step for its successful application in PD denoising. However, despite many variants of techniques deployed, the success rate is not generally good especially when the signal to noise ratio is unity or less. This paper discusses a novel technique that addresses this issue. The technique is inspired by the concept of Shannon entropy and the associated information cost functions (ICF) in information theory. It is adaptive to the detected PD signals. The paper demonstrates that the proposed technique is effective when applied to PD signals obtained through laboratory experiments and on-site measurements. When this technique is applied to cable diagnostics, it should have the potential to extend the range of PD detection from cables