Condition monitoring of power transformer as part of power plant maintenance process

Power transformer is one of the most critical components for electrical network in power plants. This means that dependability has a big role. At the moment end users allocate resources to power transformer maintenance. Resources for on-line condition monitoring on the other hand are not very significant. Reason for this is that transformers are reliable and long life components. However, failure costs might be very significant and online monitoring is justified from that point of view. This thesis focuses on power transformer online condition monitoring. The goal is to find cost-effective and integrated solution which provides good-enough transformer monitoring. The subject has been studied quite a lot which tells about increasing interest towards the subject and might indicate possible markets for transformer monitoring services. In the beginning research will focus on describing maintenance and condition monitoring related terms. Also goals are defined for different stakeholders applying the Delphi method. The middle part of the work focus on power transformer structure, fault statistics, condition monitoring methods and measurement devices. Also possibilities of condition monitoring are covered. Research results are divided into two different categories. First part of the results will be related to requirements defined for power transformer condition monitoring. Results include requirements for three different ranges of transformer monitoring. Second part of the results contains a specification for pilot project to test power transformer condition monitoring methods and devices.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Digital condition monitoring for smart transformers

A digital transformer is the centrepiece of a smart grid that gives agility to the business model of the power sector. It enables self-measurement, monitoring, analysis, and two-way communication of its condition using various electronic devices in real time. However, big data issues, high cost of sensors, rapidly changing digital technologies, and a lack of standardisation protocol restricts the emergence of a truly digital transformer. This paper describes that a multidimensional approach towards storage, analysis, and safety of condition monitoring data is the key to an integrated platform for complete automation of such purposes

Digital condition monitoring for smart transformers

A digital transformer is the centrepiece of a smart grid that gives agility to the business model of the power sector. It enables self-measurement, monitoring, analysis, and two-way communication of its condition using various electronic devices in real time. However, big data issues, high cost of sensors, rapidly changing digital technologies, and a lack of standardisation protocol restricts the emergence of a truly digital transformer. This paper describes that a multidimensional approach towards storage, analysis, and safety of condition monitoring data is the key to an integrated platform for complete automation of such purposes

A Markovian model for power transformer maintenance

The condition of the insulation paper is one of the key determinants of the lifetime of a power transformer. The winding insulation paper may deteriorate aggressively and result in the unexpected failure of power transformers, especially under the presence of high moisture, oxygen, and metal contaminants. Such types of scenarios can be prevented if the deterioration is detected on time. Various types of condition monitoring techniques have been developed to detect transformer condition such as dissolved gas analysis and frequency response analysis. They are non-intrusive and provide early warning of accelerated deterioration both chemically and mechanically. However, the accuracy of those techniques is imperfect, which means periodic inspection is still indispensable. In this paper, we discuss the value of continuous condition monitoring for power transformers and present a way to estimate this value. Towards this, a continuous-time Markov decision model is presented to optimize periodic inspections, so that the cost is minimized and the availability is maximized. We also analyze the performance based on the information from both discrete inspection and continuous condition monitoring. The result shows the dissolved gas analysis can improve the availability and operation cost, while frequency response analysis can only improve the availability of power transformers.EPSRC, Innovate U

On-line condition monitoring of transition assets

There are a number of medium voltage (MV) power distribution cable networks worldwide that are constructed predominantly of mass impregnated paper cables - London being one of these. Paper insulated lead covered (PILC) cables were extensively laid in the 50s and 60s before the introduction of cheaper polymeric alternatives that were sufficiently reliable. The current operational state of these networks has shown a gradual increase in failure rates of the previously reliable paper cables that are drawing to the end of their expected design life. Utilities are faced with the prospect of the impending failure of large sections of their prized asset and are keen to develop tools to better understand the health of their hardware. The analysis of partial discharge (PD) signals produced by the cables has been identified as a economically viable option to provide continuous condition monitoring of PILC cable circuits. Clearly, a comprehensive understanding of how PD activity relates to the various failure mechanisms exhibited by cable circuits in the field is required. A recently published technique for PD source discrimination was coupled with an understanding of the experiment and applied to the experiment data to isolate the signals specific to each degradation mechanism [1]. This technique has been applied to both rotation machines and transformer systems with promising results. PD signal discrimination is seen as the first step towards an autonomous condition monitoring futur

Intelligent Condition Assessment of Power Transformer Based on Data Mining Techniques

In recent years, the trade-off between quality and cost of power system components has become a matter of interest for many utilities. The widespread use of costly electricity networks either in residential or industrial areas has encouraged service providers to find a proper strategy that will minimize the overall life-cycle cost while keeping components in good working condition. The power transformer, which represents approximately 60% of the overall cost of the network, is ranked as one of the most important and expensive components. However, the transformer's sudden failure puts the system in a serious or critical condition which in most cases causes catastrophic loss to both utilities and customers. Significant attention has been given to monitoring and diagnostic techniques that observe any abnormal behaviour, assess the transformer's condition, and therefore minimize the probability of unplanned outage. Yet, applying many various monitoring tests is not always applicable due to the following factors: some tests require the unit to be taken out from service for testing, insufficient availability of man power, and significant cost of applying all the tests. Thus, there is a vital demand for an intelligent method of minimizing the number of monitoring tests without losing much information about the transformer's actual condition. In this research, data mining techniques have been employed to evaluate the transformer's state through intelligent selection criteria that determines the optimal number of monitoring tests in cost-effectiveness. Feature selection technique based on ranker search method has been used to rank the monitoring tests (features) in a priority sequence from their individual evaluation, and to select the most inductive tests that provide the most information about the unit's condition. When the measured data from monitoring tests is collected and prepared, a diagnostic technique is applied to assess the condition of the transformer. In this regard, Support Vector Machine (SVM) has been utilized to perform this task due to its robust classification accuracy. SVM is first applied to the full number of tests, and then the number of monitoring tests is reduced by one after each classification process using the feature selection algorithm. The selected number of monitoring tests has shown the best possible accuracy the classifier can reach over the whole number of tests. Radial Basis Function (RBF) classifier has been used in the classification process for results comparison purposes. This proposed work contributes towards finding an intelligent method of evaluating the transformer state as well as minimizing the number of tests without losing much information about the unit's actual condition. Therefore, this method facilitates deciding a wise course of action regarding the transformer: either maintain, repair, or replace

Learning models of plant behavior for anomaly detection and condition monitoring

Providing engineers and asset managers with a too] which can diagnose faults within transformers can greatly assist decision making on such issues as maintenance, performance and safety. However, the onus has always been on personnel to accurately decide how serious a problem is and how urgently maintenance is required. In dealing with the large volumes of data involved, it is possible that faults may not be noticed until serious damage has occurred. This paper proposes the integration of a newly developed anomaly detection technique with an existing diagnosis system. By learning a Hidden Markov Model of healthy transformer behavior, unexpected operation, such as when a fault develops, can be flagged for attention. Faults can then be diagnosed using the existing system and maintenance scheduled as required, all at a much earlier stage than would previously have been possible

A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies

CBM (Condition Based Maintenance) solutions are increasingly present in industrial systems due to two main circumstances: rapid evolution, without precedents, in the capture and analysis of data and significant cost reduction of supporting technologies. CBM programs in industrial systems can become extremely complex, especially when considering the effective introduction of new capabilities provided by PHM (Prognostics and Health Management) and E-maintenance disciplines. In this scenario, any CBM solution involves the management of numerous technical aspects, that the maintenance manager needs to understand, in order to be implemented properly and effectively, according to the company’s strategy. This paper provides a comprehensive representation of the key components of a generic CBM solution, this is presented using a framework or supporting structure for an effective management of the CBM programs. The concept “symptom of failure”, its corresponding analysis techniques (introduced by ISO 13379-1 and linked with RCM/FMEA analysis), and other international standard for CBM open-software application development (for instance, ISO 13374 and OSA-CBM), are used in the paper for the development of the framework. An original template has been developed, adopting the formal structure of RCM analysis templates, to integrate the information of the PHM techniques used to capture the failure mode behaviour and to manage maintenance. Finally, a case study describes the framework using the referred template.Gobierno de Andalucía P11-TEP-7303 M

Technical and vocational skills (TVS): a means of preventing violence among youth in Nigeria

Technical and vocational skills are an important tool for reducing violence among youth, especially in Nigeria, who face security challenges due to different kinds of violence. This paper focusses on the policies and programmes intended to provide youth with skills that can help them improve their life instead of engaging in violence. The paper also studies youth participation in violence. The study shows that youth in Nigeria participate in violence because of unemployment and economic pressure. These youth are mostly from poor families and are mostly used by others to achieve their own unlawful ambition. The data were collected from various secondary sources such as textbooks, journals and conference papers that were carefully reviewed. The results obtained from the literature revealed that youth are not committed, sensitised and mobilised to taking advantage of the opportunities available to them. The results also revealed that almost all the programmes meant to provide youths with skills have failed. Poverty alleviation programmes established to create jobs, self-employment and self-reliance have been unsuccessful. Therefore, alternatives must be provided to help the younger generations. Based on the literature reviewed, the paper discusses related issues and outcomes and ends with recommendations to improve the situation