Practical applications of data mining in plant monitoring and diagnostics

Using available expert knowledge in conjunction with a structured process of data mining, characteristics observed in captured condition monitoring data, representing characteristics of plant operation may be understood, explained and quantified. Knowledge and understanding of satisfactory and unsatisfactory plant condition can be gained and made explicit from the analysis of data observations and subsequently used to form the basis of condition assessment and diagnostic rules/models implemented in decision support systems supporting plant maintenance. This paper proposes a data mining method for the analysis of condition monitoring data, and demonstrates this method in its discovery of useful knowledge from trip coil data captured from a population of in-service distribution circuit breakers and empirical UHF data captured from laboratory experiments simulating partial discharge defects typically found in HV transformers. This discovered knowledge then forms the basis of two separate decision support systems for the condition assessment/defect clasification of these respective plant items

A sequential Bayesian approach to online power quality anomaly segmentation

Increased observability on power distribution networks can reveal signs of incipient faults which can develop into costly and unexpected plant failures. While low-cost sensing and communications infrastructure is facilitating this, it is also highlighting the complex nature of fault signals, a challenge which entails precisely extracting anomalous regions from continuous data streams before classifying the underlying fault signature. Doing this incorrectly will result in capture of uninformative data. Extraction processes can be confounded by operational noise on the network including harmonics produced by embedded generation. In this paper, an online model is proposed. Our Bayesian Changepoint Power Quality anomaly Segmentation allows automated segmentation of anomalies from continuous current waveforms, irrespective of noise. Demonstration of the effectiveness of the proposed technique is carried out with operational field data as well as a challenging simulated network, highlighting the ability to accommodate noise from typical network penetration levels of power electronic devices

Automated distribution network fault cause identification with advanced similarity metrics

Distribution network monitoring has the potential to improve service levels by reporting the origin of fault events and informing the nature of remedial action. To achieve this practically, intelligent systems to automatically recognize the cause of network faults could provide a data driven solution, however, these usually require a large amount of examples to learn from, making their implementation burdensome. Furthermore, the choice of input to such a system in order to make accurate classifications is not always clear. In response to this challenge, this paper contributes a means of using minimal amounts of historical fault data to infer fault cause from substation current data through a novel structural similarity metric applied to the associated power quality waveform. This approach is demonstrated along with disturbance context similarity assessment on an industrially relevant benchmark data set where it is shown to provide an improvement in classification accuracy over comparable techniques

Decision support for distribution automation : data analytics for automated fault diagnosis and prognosis

Distribution Automation (DA) is deployed to reduce outage times, isolate the faulted area, and rapidly restore customer supplies following network faults. Recent developments in Supervisory Control and Data Acquisition (SCADA) and intelligent DA equipment have sought to improve reliability and security of supply. The introduction of such ‘intelligent’ technologies on distribution networks, where investment in dedicated condition monitoring equipment remains difficult to justify, presents an opportunity to capture constant streams of operational data which can offer a useful insight into underlying circuit conditions if utilised and managed appropriately. The primary function of the NOJA Pole-Mounted Auto-Recloser (PMAR) is to isolate distribution circuits from detected faults, while attempting to minimise outages due to transient faults. However, in this process the PMAR also captures current and voltage measurements that can be analysed to inform any subsequent fault diagnosis, and potentially detect the early onset of circuit degradation, and monitor and predict its progression. This paper details the design and development of an automated decision support system for fault diagnosis and prognosis, which can detect and diagnose evolving faults by analysing PMAR data and corresponding SCADA alarm data. A knowledge based system has been developed, utilising data science and data mining techniques, to implement diagnostic and prognostic algorithms which automate the existing manual process of post fault diagnosis and anticipation, and circuit condition assessment

A North American Arctic Aerosol Climatology using Ground-based Sunphotometry

The Arctic is known as a key area for the detection of climate changes and atmospheric pollution on a global scale. In this paper we describe a new Canadian sunphotometer network called AEROCAN, whose primary mandate is to establish a climatology of atmospheric aerosols. This network is part of AERONET, the worldwide federated sunphotometer network managed by the NASA Goddard Space Flight Center. The potential of sunphotometer data from the AERONET/AEROCAN network for monitoring of Arctic aerosols is illustrated, using examples of the multiyear variation of aerosol optical properties and atmospheric precipitable water vapour content at some stations, and in particular at Bonanza Creek, Alaska since 1994. Despite its sparse spatial density, the network represents an important tool for monitoring the spatio-temporal variation of Arctic aerosols. It also represents an important source of independent aerosol data, which we feel should be further developed in northern areas to improve our understanding of how atmospheric aerosols influence global climate.L'Arctique est reconnu comme une région clé pour la détection des changements climatiques et de la pollution atmosphérique à l'échelle planétaire. Cet article présente un nouveau réseau canadien de photomètres solaires (AEROCAN) dont le mandat principal est d'établir une climatologie des aérosols atmosphériques. Ce réseau est intégré au réseau fédéré mondial de photomètres solaires AERONET géré par le Centre des vols spatiaux Goddard de la NASA. Le potentiel des données héliophotométriques générées par le réseau AERONET/AEROCAN pour la surveillance des aérosols dans l'Arctique est illustré à l'aide d'exemples de la variation pluriannuelle des paramètres optiques des aérosols et du contenu en vapeur d'eau atmosphérique précipitable à diverses stations, en particulier à Bonanza Creek (Alaska) depuis 1994. Malgré sa faible densité spatiale, le réseau représente un outil important pour la surveillance de la variation spatio-temporelle des aérosols arctiques. Il représente en outre une source majeure de données indépendantes sur les aérosols, données dont la provenance devrait, selon nous, englober les régions boréales afin que nous ayons une meilleure compréhension de l'influence des aérosols atmosphériques sur le climat de la planète

A data analytic approach to automatic fault diagnosis and prognosis for distribution automation

Distribution Automation (DA) is deployed to reduce outages and to rapidly reconnect customers following network faults. Recent developments in DA equipment have enabled the logging of load and fault event data, referred to as ‘pick-up activity’. This pick-up activity provides a picture of the underlying circuit activity occurring between successive DA operations over a period of time and has the potential to be accessed remotely for off-line or on-line analysis. The application of data analytics and automated analysis of this data supports reactive fault management and post fault investigation into anomalous network behavior. It also supports predictive capabilities that identify when potential network faults are evolving and offers the opportunity to take action in advance in order to mitigate any outages. This paper details the design of a novel decision support system to achieve fault diagnosis and prognosis for DA schemes. It combines detailed data from a specific DA device with rule-based, data mining and clustering techniques to deliver the diagnostic and prognostic functions. These are applied to 11kV distribution network data captured from Pole Mounted Auto-Reclosers (PMARs) as provided by a leading UK network operator. This novel automated analysis system diagnoses the nature of a circuit’s previous fault activity, identifies underlying anomalous circuit activity, and highlights indications of problematic events gradually evolving into a full scale circuit fault. The novel contributions include the tackling of ‘semi-permanent faults’ and the re-usable methodology and approach for applying data analytics to any DA device data sets in order to provide diagnostic decisions and mitigate potential fault scenarios

A Planetary Companion to gamma Cephei A

We report on the detection of a planetary companion in orbit around the primary star of the binary system $\gamma$ Cephei. High precision radial velocity measurements using 4 independent data sets spanning the time interval 1981--2002 reveal long-lived residual radial velocity variations superimposed on the binary orbit that are coherent in phase and amplitude with a period or 2.48 years (906 days) and a semi-amplitude of 27.5 m s$^{-1}$. We performed a careful analysis of our Ca II H & K S-index measurements, spectral line bisectors, and {\it Hipparcos} photometry. We found no significant variations in these quantities with the 906-d period. We also re-analyzed the Ca II $\lambda$8662 {\AA} measurements of Walker et al. (1992) which showed possible periodic variations with the ``planet'' period when first published. This analysis shows that periodic Ca II equivalent width variations were only present during 1986.5 -- 1992 and absent during 1981--1986.5. Furthermore, a refined period for the Ca II $\lambda$8662 {\AA} variations is 2.14 yrs, significantly less than residual radial velocity period. The most likely explanation of the residual radial velocity variations is a planetary mass companion with $M$ sin $i$ = 1.7 $M_{Jupiter}$ and an orbital semi-major axis of $a_2$ $=$ 2.13 AU. This supports the planet hypothesis for the residual radial velocity variations for $\gamma$ Cep first suggested by Walker et al. (1992). With an estimated binary orbital period of 57 years $\gamma$ Cep is the shortest period binary system in which an extrasolar planet has been found. This system may provide insights into the relationship between planetary and binary star formation.Comment: 19 pages, 15 figures, accepted in Ap. J. Includes additional data and improved orbital solutio

Multi vector energy demand modelling for predicting low-carbon heat loads

The push to decarbonize heating through the adoption of low-carbon heating alternatives, such as electrical heat pumps, will alter network load magnitudes and shapes at the LV power distribution network level. Due to the lack of monitoring at the distribution level it is of interest to develop methods to infer LV network conditions in the absence of complete data. Limited present uptake of domestic heat pumps in the UK limits available data to use for localized predictions sensitive to household specific time of use and magnitude variability. This work demonstrates a methodology for inferring potential future electrical heat load from existing household electrical and gas demand data, facilitating the prediction of future electrical heat load from limited data. Recurring load profiles from gas and electrical data from the Energy Demand Research Project are identified and clustered using a k-means clustering approach. The relation of these recurring load profiles with respect to each other is mapped using a through the construction of a Markov model with transition probabilities trained from household electrical and gas demand data. The use of this approach to infer future electrical heat load is then demonstrated in a simple case study

Extracting distribution network fault semantic labels from free text incident tickets

Increased monitoring of distribution networks and power system assets present utilities with new opportunities to predict and forestall system failures. Although automated pattern recognition methodologies have given other industries significant advantage, power system operators face additional challenges before these can be realized. The effort of apportioning ground truth to fault data creates a knowledge bottleneck that can make utilizing automatic classification techniques impossible. Surrogate approaches using operational process outputs such as maintenance tickets as labels can be challenging owing to the causal ambiguity of these written records. To approach a solution, this paper demonstrates utilizing natural language processing techniques to disambiguate the free text in maintenance tickets for onward use in supervised learning of fault prediction and classification techniques. A demonstration of this approach on an established power quality fault data set is provided for illustration

Automated fault analysis and diagnosis using high-frequency and maintenance data from distribution networks

Fault analysis based on high-resolution data acquisition is growing in use as it offers a more complete picture of faults which provides an opportunity to deal with failures more effectively. However, with increased volume of data collected, it becomes impossible for engineers to interpret every fault instance. A machine learning approach to classification should be the solution to this, but it is time-consuming to manually label faults for training and validation making data-driven approaches impossible to transfer into practical implementation. A solution to this is to unify fault analysis with maintenance report analysis to automate the generation of training labels. This paper outlines how a fully automatic fault detection and diagnostic approach based around power quality waveform analysis can be used to improve situational awareness on distribution networks. The methodology is illustrated using operational case study data and realistic simulations to demonstrate the diagnostic functionality as well as the practical benefit. In particular, classification accuracy is shown to approach that of expert labelled fault data