Scott Strachan

The design of a multi-agent transformer condition monitoring system

Online diagnostics and online condition monitoring are important functions within the operation and maintenance of power transformers. This paper describes how a multi-agent system (MAS) for transformer condition monitoring has been designed to employ the data generated by the ultra high frequency (UHF) monitoring of partial discharge activity. It describes the rationale behind the use of multi-agent techniques, and the problems overcome through this technology. Every aspect of the MAS design is discussed. In addition, the design and performance of the intelligent interpretation techniques are detailed.

A community based approach for sustainable off-grid PV systems in developing countries

The energy requirements of developing countries underpin progress towards achieving the Millennium Development Goals (MDGs). Rural electrification is acknowledged as key to providing a source of reliable, affordable and sustainable energy. Many planners and decision makers agree that the off-grid PV system has the potential to become a valued and straightforward source of electricity for remote rural communities. However, there are several challenges that need to be addressed to realize the potential of PV as a sustainable solution. These challenges are illustrated and highlighted by many reported cases of poor sustainability/longevity of PV installation in sub-Saharan Africa. This paper reviews the sustainability of off-grid renewable energy deployments in sub-Saharan Africa along with recent attempts to define sustainable energy frameworks. The specific case of off-grid PV systems for improved educational and health facilities are considered and a community based approach for improved sustainability is proposed. Case studies illustrating practical implementation of this approach in Gambia and Malawi are described.

Partial discharge location in power cables using a double ended method based on time triggering with GPS

Partial discharge (PD) diagnostics is the most widely used tool to assess the insulation condition of insulated power cables which facilitates informed maintenance planning leading to extended service life of ageing assets. Time domain reflectometry (TDR) using a single ended or double ended approach is the most widely used method for locating PD sources. The success of the single ended method is dependent upon cable network design. However, by monitoring PDs at both ends of the cable, i.e. double-ended PD monitoring, higher accuracy of PD location can be achieved with a higher success rate. The double ended method is not widely used due to its complex system design, time synchronization and communication requirement between measurement units. This paper proposes a double ended PD location system which triggers on the predefined one pulse per second (1PPS) signal obtained from a global positioning systems (GPS) using novel time based triggering logic (TBTL) implemented in field programmable gate arrays (FPGA). This system ignores non-useable (not synchronized) data caused by flywheel 1PPS from GPS receiver due to any short-term loss of satellite signals which eventually reduces the PD location accuracy. Furthermore TBTL also ignores spurious triggering pulses radiated from noise sources within the substation. With the use of a communication link between two ends of the cable provided via mobile broadband together with TBTL, eliminated the acquisition of non-useable(not synchronized) data. Based on laboratory tests and on-site measurements PD location accuracy of less than ± 10 m can be achieved. The system design, laboratory tests and on-site measurements are discussed.

The use of power frequency current transformers as partial discharge sensors for underground cables

Partial discharge (PD) diagnostics can be used to diagnose insulation defects in medium voltage cables and thereby inform required maintenance planning to extend the lifespan of ageing assets. High frequency current transformers (HFCT) installed on the earth strap at the cable termination are widely used to detect the fast varying pulses resulting from PD. Due to constructional reasons earth strap accessibility in underground cables is often limited. Furthermore in three core cables, phase angle dependency of pulses resulting from PD sometimes fails to induce pulses in the earth screen. This paper investigates an alternative method of PD detection using conventional power frequency current transformers (PFCT) principally used for protection and measurement purposes. This investigation assesses the feasibility of this approach by conducting a magnetic study of the transformer core, frequency response analysis (FRA), and finally capturing and analyzing onsite PD measurements using conventional PFCTs and the developed PD data acquisition/interpretation system. Based on the laboratory and on-site measurement results, PFCTs can be considered for detecting PD. This approach does not require retrofitting additional PD sensors and can also be applied to older switchgear design without any outage. Hence this approach can be considered as a low cost and noninvasive method of PD detection in cables.

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 condition monitoring data (which represent modes 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 clarification of these respective plant items.

Incremental knowledge-based partial discharge diagnosis in oil-filled power transformers

The abstraction of meaningful diagnostic information from raw condition monitoring data in domains where diagnostic expertise and knowledge is limited presents a significant research challenge. This paper proposes a means of abstracting the salient features required to characterize partial discharge (PD) activity detected in oil-filled power transformers. This enables ultra high frequency (UHF) sensor data to be interpreted and translated into a meaningful diagnostic explanation of the observed PD activity. Plant data captured from UHF sensors forms the inputs to a knowledge-based data interpretation system, supporting on-line plant condition assessment and insulation defect diagnosis. The paper describes the functionality of a knowledge-based decision support system, providing engineers with a comprehensive diagnostic explanation of partial discharge activity detected in oil-filled power transformers. The diagnostic output can then be used to advise the engineer in (and potentially automate) the classification and location of partial discharge defect sources

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 11-kV distribution network data captured from Pole Mounted Auto-Reclosers as provided by a leading U.K. 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.

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.

Automatic analysis of Pole Mounted Auto-Recloser data for fault diagnosis and prognosis

Fault diagnosis is a key part of a control and protection engineers role to ensure the effective and stable performance of electrical power networks. One challenge is to support the analysis and application of expert judgement to the, often, large data sets generated. To assist engineers with this task and improve network reliability, this research focuses on analysing previous fault activity in order to obtain an early-warning report to assist fault diagnosis and fault prognosis. This paper details the design of an integrated system with a fault diagnosis algorithm utilising available Supervisory Control And Data Acquisition (SCADA) alarm data and 11kV distribution network data captured from Pole Mounted Auto-Reclosers (PMARs) (provided by a leading UK network operator). The developed system will be capable of diagnosing the nature of a circuits previous fault activity, underlying circuit activity and evolving fault activity and the risk of future fault activity. This will provide prognostic decision support for network operators and maintenance staff.

Remote monitoring of off-grid renewable energy Case studies in rural Malawi, Zambia, and Gambia

Increased understanding of off-grid renewable energy technology (RET) performance can assist in improving sustainability of such systems. The technologies for remote monitoring of RET deployments in developing countries are promising with various configurations and usages being tested. Recent applications of remote monitoring technologies in Malawi, Gambia, and Zambia are presented along with their respective strengths and weaknesses. The potential for remote monitoring applications to improve sustainability of off-grid RET is explored along with some theoretical directions of the technologies.