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

Providing decision support for the condition-based maintenance of circuit breakers through data mining of trip coil current signatures

The focus of this paper centers on the condition assessment of 11kV-33kV distribution circuit breakers from the analysis of their trip coil current signatures captured using an innovative condition monitoring technology developed by others. Using available expert knowledge in conjunction with a structured process of data mining, thresholds associated with features representing each stage of a circuit breaker's operation may be defined and used to characterize varying states of circuit breaker condition. Knowledge and understanding of satisfactory and unsatisfactory breaker condition can be gained and made explicit from the analysis of captured trip signature data and subsequently used to form the basis of condition assessment and diagnostic rules implemented in a decision support system, used to inform condition-based decisions affecting circuit breaker 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 SP Power System's in-service circuit breakers. This knowledge then forms the basis of a decision support system for the condition assessment of these circuit breakers during routine trip testing

Intelligent monitoring of the health and performance of distribution automation

With a move to 'smarter' distribution networks through an increase in distribution automation and active network management, the volume of monitoring data available to engineers also increases. It can be onerous to interpret such data to produce meaningful information about the health and performance of automation and control equipment. Moreover, indicators of incipient failure may have to be tracked over several hours or days. This paper discusses some of the data analysis challenges inherent in assessing the health and performance of distribution automation based on available monitoring data. A rule-based expert system approach is proposed to provide decision support for engineers regarding the condition of these components. Implementation of such a system using a complex event processing system shell, to remove the manual task of tracking alarms over a number of days, is discussed

Electronic Device Incorporating Memristor Made From Metallic Nanowire

An electronic device includes a first electrode, a second electrode and a nanowire connected between the first and second electrodes to allow electric current flow. The nanowire is made from a conductive material exhibiting a variable resistance due to electromigration. The nanowire is repeatably switchable between two states. A voltage clamp operates through feedback control to maintain the voltage across the nanowire and prevent thermal runaway

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

Exercising UNESCO Competencies In Students Through Research-Based Education For Sustainable Development

Today’s complex global challenges call upon a different pedagogical approach to Higher Education (HE) that is fit for the purpose of preparing our students – to paraphrase the words of Sir Jonathan Porritt - not only for the world of work, but the work of the world. Indeed, we can and should be preparing students for both, as it is through their professional lives and activities that they will arguably be able to have the most positive impact on these global challenges. Consequently, re-focusing teaching on ways of thinking, being and practicing, the so-called ‘head, heart and hands’ framework, should be done in a way that actively stretches students beyond the comfort of their disciplinary boundaries, knowledge and skill sets. This paper will present the University of Strathclyde’s practice and experience of establishing their award winning Vertically Integrated Projects for Sustainable Development (VIP4SD) programme, as an exemplar of how to embed ResearchBased Education for Sustainable Development in undergraduate curricula. This paper will show how VIP4SD provides students with the time and space in their curriculum to develop demonstrable levels of domain expertise and exercise key UNESCO sustainability (and ergo employability) competences. We then discuss how we have sought to evidence this by supporting students to recognise and articulate their competency development, achieved through the experiential and transformational learning provided by the VIP4SD programme

Integrable generalizations of Schrodinger maps and Heisenberg spin models from Hamiltonian flows of curves and surfaces

A moving frame formulation of non-stretching geometric curve flows in Euclidean space is used to derive a 1+1 dimensional hierarchy of integrable SO(3)-invariant vector models containing the Heisenberg ferromagnetic spin model as well as a model given by a spin-vector version of the mKdV equation. These models describe a geometric realization of the NLS hierarchy of soliton equations whose bi-Hamiltonian structure is shown to be encoded in the Frenet equations of the moving frame. This derivation yields an explicit bi-Hamiltonian structure, recursion operator, and constants of motion for each model in the hierarchy. A generalization of these results to geometric surface flows is presented, where the surfaces are non-stretching in one direction while stretching in all transverse directions. Through the Frenet equations of a moving frame, such surface flows are shown to encode a hierarchy of 2+1 dimensional integrable SO(3)-invariant vector models, along with their bi-Hamiltonian structure, recursion operator, and constants of motion, describing a geometric realization of 2+1 dimensional bi-Hamiltonian NLS and mKdV soliton equations. Based on the well-known equivalence between the Heisenberg model and the Schrodinger map equation in 1+1 dimensions, a geometrical formulation of these hierarchies of 1+1 and 2+1 vector models is given in terms of dynamical maps into the 2-sphere. In particular, this formulation yields a new integrable generalization of the Schrodinger map equation in 2+1 dimensions as well as a mKdV analog of this map equation corresponding to the mKdV spin model in 1+1 and 2+1 dimensions.Comment: Published version with typos corrected. Significantly expanded version of a talk given by the first author at the 2008 BIRS workshop on "Geometric Flows in Mathematics and Physics

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

Vertically integrated projects for sustainable development : achieving transformational action by embedding research-based ESD in curricula

The University of Strathclyde’s flagship Vertically Integrated Projects for Sustainable Development (VIP4SD) program is embedded in the formal and informal curriculum and encourages students to work in partnership with experienced researchers and academics, and with their peers from different disciplines and across all year groups to create student-centered, SDG-focused research projects. The program is designed to develop the core competencies of Education for Sustainable Development (ESD) through an immersive ‘real-world’ educational experience that aims to provide a “transformative learning environment” that enable students to engage in “transformative action” through ESD , and so not only “prepare our students for the world of work, but to tackle the work of the world” . It does this by embedding ESD in curricula through the use of Research (or Inquiry)-Based Education (RBE or IBE). The paper will explore Strathclyde’s experience, and the challenges it has encountered, in taking the program from pilot to mainstream and how this has inspired a whole institution approach to embedding ESD more generally in Strathclyde curricul

Embedding research-based education for sustainable development and climate education in HE curricula

The University of Strathclyde are embedding Research-Based Education (RBE) for Sustainable Development into undergraduate curricula using an innovative pedagogy called Vertically Integrated Projects (VIP) and aligning this with United Nations Sustainable Development Goal (UN SDG)-focused research. The development of student-centred Climate Education workshops is being used to ensure students are offered joined-up Climate Education activity that can support Strathclyde's broader Education for Sustainable Development agenda