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

The role of intelligent systems in delivering the smart grid

The development of "smart" or "intelligent" energy networks has been proposed by both EPRI's IntelliGrid initiative and the European SmartGrids Technology Platform as a key step in meeting our future energy needs. A central challenge in delivering the energy networks of the future is the judicious selection and development of an appropriate set of technologies and techniques which will form "a toolbox of proven technical solutions". This paper considers functionality required to deliver key parts of the Smart Grid vision of future energy networks. The role of intelligent systems in providing these networks with the requisite decision-making functionality is discussed. In addition to that functionality, the paper considers the role of intelligent systems, in particular multi-agent systems, in providing flexible and extensible architectures for deploying intelligence within the Smart Grid. Beyond exploiting intelligent systems as architectural elements of the Smart Grid, with the purpose of meeting a set of engineering requirements, the role of intelligent systems as a tool for understanding what those requirements are in the first instance, is also briefly discussed

Big data techniques for wind turbine condition monitoring

The continual development of sensor and storage technology has led to a dramatic increase in volumes of data being captured for condition monitoring and machine health assessment. Beyond wind energy, many sectors are dealing with the same issue, and these large, complex data sets have been termed ‘Big Data’. Big Data may be defined as having three dimensions: volume, velocity, and variety. This paper discusses the application of Big Data practices for use in wind turbine condition monitoring, with reference to a deployed system capturing 2 TB of data per month

Circuit breaker prognostics using SF6 data

Control decisions within future energy networks may take account of the health and condition of network assets, pushing condition monitoring within the smart grid remit. In order to support maintenance decisions, this paper proposes a circuit breaker prognostic system, which ranks circuit breakers in order of maintenance priority. By monitoring the SF6 density within a breaker, the system not only predicts the number of days to a critical level, but also incorporates uncertainty by giving upper and lower bounds on the prediction. This prognostic model, which performs linear regression, will be described in this paper, along with case studies demonstrating ranking breakers based on maintenance priority and prognosis of a leaking breaker. Providing an asset manager with this type of information could allow improved management of his/her assets, potentially deferring maintenance to a time when an outage is already scheduled

An agent-based implementation of hidden Markov models for gas turbine condition monitoring

This paper considers the use of a multi-agent system (MAS) incorporating hidden Markov models (HMMs) for the condition monitoring of gas turbine (GT) engines. Hidden Markov models utilizing a Gaussian probability distribution are proposed as an anomaly detection tool for gas turbines components. The use of this technique is shown to allow the modeling of the dynamics of GTs despite a lack of high frequency data. This allows the early detection of developing faults and avoids costly outages due to asset failure. These models are implemented as part of a MAS, using a proposed extension of an established power system ontology, for fault detection of gas turbines. The multi-agent system is shown to be applicable through a case study and comparison to an existing system utilizing historic data from a combined-cycle gas turbine plant provided by an industrial partner

Designing Wind Turbine Condition Monitoring Systems Suitable for Harsh Environments

Research into wind turbine condition monitoring is continually receiving greater attention due to the potential benefits from condition monitoring systems (CMS). These benefits can only be realised with high reliability of the condition monitoring system itself. This paper discusses how CMS reliability can be increased, by introducing four types of robustness and how to design the CMS to meet these requirements. The paper uses a case study CMS installation to illustrate the design requirements, and lessons learned from the installation process

Using a high fidelity CCGT simulator for building prognostic systems

Pressure to reduce maintenance costs in power utilities has resulted in growing interest in prognostic monitoring systems. Accurate prediction of the occurrence of faults and failures would result not only in improved system maintenance schedules but also in improved availability and system efficiency. The desire for such a system has driven research into the emerging field of prognostics for complex systems. At the same time there is a general move towards implementing high fidelity simulators of complex systems especially within the power generation field, with the nuclear power industry taking the lead. Whilst the simulators mainly function in a training capacity, the high fidelity of the simulations can also allow representative data to be gathered. Using simulators in this way enables systems and components to be damaged, run to failure and reset all without cost or danger to personnel as well as allowing fault scenarios to be run faster than real time. Consequently, this allows failure data to be gathered which is normally otherwise unavailable or limited, enabling analysis and research of fault progression in critical and high value systems. This paper presents a case study of utilising a high fidelity industrial Combined Cycle Gas Turbine (CCGT) simulator to generate fault data, and shows how this can be employed to build a prognostic system. Advantages and disadvantages of this approach are discussed

Identifying prognostic indicators for electrical treeing in solid insulation through pulse sequence analysis

Predictive maintenance attempts to evaluate the condition of equipment and predict the future trend of the equipment's aging, in order to reduce costs when compared to the two traditional approaches: corrective and preventive maintenance. This prediction requires an accurate prognostic model of aging. In solid insulation, the ultimate goal of prognostics is to predict the advent of failure, i.e., insulation breakdown, in terms of remaining useful life (RUL). One fault is electrical treeing, which is progressive thus leading to potentially catastrophic failure. Research has shown that diagnosis of faults can be achieved based on partial discharge (PD) monitoring [1], i.e., phase-resolved and pulse sequence analysis (PSA). This work will explore the extension of this concept towards predicting evolution of the defect: moving beyond diagnostics towards prognostics. To do this, there is a need for further investigation of prognostic features within PD characteristics leading up to breakdown. In this work, a needle-plane test arrangement was set up using a hypodermic needle and pre-formed silicone rubber as test samples. The visual observations and tree growth measurements were made using a digital microscope. PD data was captured using a radio frequency (RF) sensor and analysed using PSA. The main idea of the PSA approach is the strong relationship between two consecutive pulses caused by PD activities, which can give an understanding of the local degradation processes [1]. As for electrical treeing, a breakdown indicator in PSA is the appearance of heavily clustered data points that lie diagonally in scatter plots of the differential ratio of voltage and time of consecutive charges (Un = Δun/Δtn) [2,3]. Figure 1 shows an example of a plot that changed to a diagonal line after 14 hours of aging time. This paper investigates the formation of the diagonal line based on the distribution of the plot from the start of electrical treeing until breakdown occurs. Finally, statistical features of the PSA plot are given and will be used for lifetime prediction of insulation samples in future work

Identifying Prognostic Indicators for Electrical Treeing in Solid Insulation through PD Analysis

This paper presents early results from an experimental study of electrical treeing on commercially available pre-formed silicone samples. A needle-plane test arrangement was set up using hypodermic needles. Partial discharge (PD) data was captured using both the IEC 60270 electrical method and radio frequency (RF) sensors, and visual observations are made using a digital microscope. Features of the PD plot that corresponded to electrical tree growth were assessed, evaluating the similarities and differences of both PD measurement techniques. Three univariate phase distributions were extracted from the partial discharge phase-resolved (PRPD) plot and the first four statistical moments were determined. The implications for automated lifetime prediction of insulation samples due to electrical tree development are discussed

Identifying harmonic attributes from online partial discharge data

Partial discharge (PD) monitoring is a key method of tracking fault progression and degradation of insulation systems. Recent research discovered that the harmonic regime experienced by the plant also affects the PD pattern, questioning the conclusions about equipment health drawn from PD data. This paper presents the design and creation of an online system for harmonic circumstance monitoring of distribution cables, using only PD data. Based on machine learning techniques, the system can assess the prevalence of the 5th and 7th harmonic orders over the monitoring period. This information is key for asset managers to draw correct conclusions about the remaining life of polymeric cable insulation, and prevent overestimation of the degradation trend