Exploiting multi-agent system technology within an autonomous regional active network management system

This paper describes the proposed application of multi-agent system (MAS) technology within AuRA-NMS, an autonomous regional network management system currently being developed in the UK through a partnership between several UK universities, distribution network operators (DNO) and a major equipment manufacturer. The paper begins by describing the challenges facing utilities and why those challenges have led the utilities, a major manufacturer and the UK government to invest in the development of a flexible and extensible active network management system. The requirements the utilities have for a network automation system they wish to deploy on their distribution networks are discussed in detail. With those requirements in mind the rationale behind the use of multi-agent systems (MAS) within AuRA-NMS is presented and the inherent research and design challenges highlighted including: the issues associated with robustness of distributed MAS platforms; the arbitration of different control functions; and the relationship between the ontological requirements of Foundation for Intelligent Physical Agent (FIPA) compliant multi-agent systems, legacy protocols and standards such as IEC 61850 and the common information model (CIM)

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

Knowledge and model based reasoning for power system protection performance analysis

Technological advances within the field of power systems has led to engineers, at all levels, being confronted with an ever increasing amount of data to be analysed. This coincides with greater pressure on engineers to work more efficiently and cost effectively, due to the increasingly competitive nature of the electricity supply industry. As a result, there is now the requirement for intelligent systems to interpret the available data and provide information which is relevant, manageable and readily assimilated by engineers. This thesis concerns the application of intelligent systems to the data interpretation tasks of protection engineers. An on-line decision support system is discussed which integrates two expert system paradigms in order to perform power system protection performance analysis. Knowledge based system techniques are used to interpret the data from supervisory, control and data acquisition systems, whereas a model based diagnosis approach to the comprehensive validation of protection performance, using the more detailed data which is available from fault records or equivalent, is assessed. Such a decision support system removes the requirement for time consuming manual analysis of data. An assessment of power system protection performance is provided in an on-line fashion, quickly alerting the engineers to failures or problems within the protection system. This improves efficiency and maximises the benefit of having an abundance of data available.Technological advances within the field of power systems has led to engineers, at all levels, being confronted with an ever increasing amount of data to be analysed. This coincides with greater pressure on engineers to work more efficiently and cost effectively, due to the increasingly competitive nature of the electricity supply industry. As a result, there is now the requirement for intelligent systems to interpret the available data and provide information which is relevant, manageable and readily assimilated by engineers. This thesis concerns the application of intelligent systems to the data interpretation tasks of protection engineers. An on-line decision support system is discussed which integrates two expert system paradigms in order to perform power system protection performance analysis. Knowledge based system techniques are used to interpret the data from supervisory, control and data acquisition systems, whereas a model based diagnosis approach to the comprehensive validation of protection performance, using the more detailed data which is available from fault records or equivalent, is assessed. Such a decision support system removes the requirement for time consuming manual analysis of data. An assessment of power system protection performance is provided in an on-line fashion, quickly alerting the engineers to failures or problems within the protection system. This improves efficiency and maximises the benefit of having an abundance of data available

Online conditional anomaly detection in multivariate data for transformer monitoring

Retrofitting condition monitoring systems to aging plant can be problematic, since the particular signature of normal behavior will vary from unit to unit. This paper describes a technique for anomaly detection within the context of the conditions experienced by an in-service transformer, such as loading, seasonal weather, and network configuration. The aim is to model the aged but normal behavior for a given transformer, while reducing the potential for anomalies to be erroneously detected. The paper describes how this technique has been applied to two transmission transformers in the U.K. A case study of 12 months of data is given, with detailed analysis of anomalies detected during that time

Machine learning model for event-based prognostics in gas circulator condition monitoring

Gas circulator (GC) units are an important rotating asset used in the Advanced Gas-cooled Reactor (AGR) design, facilitating the flow of CO2 gas through the reactor core. The ongoing maintenance and examination of these machines is important for operators in order to maintain safe and economic generation. GCs experience a dynamic duty cycle with periods of non-steady state behavior at regular refuelling intervals, posing a unique analysis problem for reliability engineers. In line with the increased data volumes and sophistication of available the technologies, the investigation of predictive and prognostic measurements has become a central interest in rotating asset condition monitoring. However, many of the state-of-the-art approaches finding success deal with the extrapolation of stationary time series feeds, with little to no consideration of more-complex but expected events in the data. In this paper we demonstrate a novel modelling approach for examining refuelling behaviors in GCs, with a focus on estimating their health state from vibration data. A machine learning model was constructed using the operational history of a unit experiencing an eventual inspection-based failure. This new approach to examining GC condition is shown to correspond well with explicit remaining useful life (RUL) measurements of the case study, improving on the existing rudimentary extrapolation methods often employed in rotating machinery health monitoring

Prognostics and health management oriented data analytics suite for transformer health monitoring

Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety, and economic effects. With the advance of machine learning techniques, it is possible to enhance traditional transformer health monitoring techniques with data-driven and expert-based prognostics and health management (PHM) applications. Accordingly, this paper reviews the experience of the authors in the implementation of machine learning methods for transformer condition monitoring

Prognostics and health management oriented data analytics suite for transformer health monitoring

Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety, and economic effects. With the advance of machine learning techniques, it is possible to enhance traditional transformer health monitoring techniques with data-driven and expert-based prognostics and health management (PHM) applications. Accordingly, this paper reviews the experience of the authors in the implementation of machine learning methods for transformer condition monitoring

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

Weather related fault prediction in minimally monitored distribution networks

Power distribution networks are increasingly challenged by ageing plant, environmental extremes and previously unforeseen operational factors. The combination of high loading and weather conditions is responsible for large numbers of recurring faults in legacy plants which have an impact on service quality. Owing to their scale and dispersed nature, it is prohibitively expensive to intensively monitor distribution networks to capture the electrical context these disruptions occur in, making it difficult to forestall recurring faults. In this paper, localised weather data are shown to support fault prediction on distribution networks. Operational data are temporally aligned with meteorological observations to identify recurring fault causes with the potentially complex relation between them learned from historical fault records. Five years of data from a UK Distribution Network Operator is used to demonstrate the approach at both HV and LV distribution network levels with results showing the ability to predict the occurrence of a weather related fault at a given substation considering only meteorological observations. Unifying a diverse range of previously identified fault relations in a single ensemble model and accompanying the predicted network conditions with an uncertainty measure would allow a network operator to manage their network more effectively in the long term and take evasive action for imminent events over shorter timescales