Risk Based Maintenance in Electricity Network Organisations

Presently, maintenance management of assets in infrastructure utilities such as electricity, gas and water are widely undergoing changes towards new working environments. These are mainly driven against the background of stringent regulatory regimes, an ageing asset base, increased customer demands and constrained financial resources. Therefore, it becomes imperative for infrastructure utilities to strive towards more effective and efficient operation and maintenance approaches. In this light, new asset management approaches such as risk management are gaining more interest worldwide. In this research the focus has been on the framework for introducing modern maintenance management in electricity network organisations. It covers the development and practical application of enabling factors for maintenance management such as organisation capabilities, maturity models, structured and comprehensive frameworks for assessing risk based maintenance policies and decision-making tools and technologies. Thus, an integral, multi-disciplinary, approach towards maintenance management needs to be established, which is a challenge that has been taken up in this thesis for electricity network organisations.Electrical Power EngineeringElectrical Engineering, Mathematics and Computer Scienc

Application of Statistical Life Data Analysis for Cable Joints in MV Distribution Networks: An Asset Management Approach

The greater pressure from both customers and regulators to maintain and enhance service reliability, while at the same time controlling costs, has caused many utility distribution businesses to adopt Asset Management (AM) as their framework. Therefore, AM is widely being applied in asset intensive industries around the world. Generally, AM consists of data driven decision-making processes with the goal of deriving the most value from utility assets within the available budget. Asset intensive industries rely on asset data, information and asset knowledge as key enablers in undertaking both strategic AM activities and operational activities. Good asset information (timely, reliable and accurate data) enables better decisions to be made such as determining the optimal asset maintenance or renewal frequency for an asset. Consequently, in the past years utilities have progressively created databases to record asset or business data such as failure, maintenance, operation and cost. However, in many cases, the available data required to track asset population reliability are not sufficiently rich to provide a basis for straightforward decision-making processes. However, the determination of asset population reliability requires collection of data and systematic and scientific evaluation of data on equipment failures. In this MSc thesis project, a systematic approach for analyzing asset life data (data describing equipment lifetime) in presence of incomplete data by means of statistical analytical methods is introduced and applied in a practical case for medium voltage cable joint populations. The analysis in this thesis mainly focus on a statistically based approach which uses data available from the past to predict short term reliability of this specific group of assets.High Voltage Technology & Asset ManagementElectrical Power EngineeringElectrical Engineering, Mathematics and Computer Scienc

Upcoming Role of Condition Monitoring in Risk-Based Asset Management for the Power Sector

The electrical power sector is stimulated to evolve under the pressures of the energy transition, the deregulation of electricity markets and the introduction of intelligent grids. In general, engineers believe that technologies such as monitoring, control and diagnostic devices, can realize this evolvement smoothly. Unfortunately, the contributions of these emerging technologies to business strategies remain difficult to quantify in straightforward metrics. Consequently, decisions to invest on these technologies are still taken in an ad hoc manner. This is far from the risk-based approach commonly recommended for asset management (AM). The paper introduces risk-based management as a guiding principle for maintenance management. Then, the triple-level AM model (strategic, tactical and operational) as the foundation to define risk-based AM is described. Afterwards, two categories of risks, one triggered by technical stimuli and the other by non-technical stimuli are introduced. It is shown that the main challenge of managing risks with technical stimuli is to have the ability to understand the technical cause of failures, which is located at the operational level within the triple-level AM model. One method to quantitatively understand the technical cause of failures is by means of condition diagnostic and monitoring technologies. Therefore, the aim of this paper is to clarify the potential contribution of condition diagnostic and monitoring technologies to risk-based decision making for the power sector. This paper shows that, in practice, the implementation of condition diagnostic and monitoring technologies is mainly driven by purely technical asset based considerations without evaluating the contribution to, for instance, risks. This paper provides a list of aspects in which condition diagnostic and monitoring may contribute to risk evaluation with technical stimuli. The listed aspects (which are: (1) asset specific condition data, (2) timely condition data and (3) predictive condition data) can be regarded as input for the probability of failure and as influencing input for the consequence of failure, hence benefiting quantitative risk studies and AM activities (such as condition assessment/maintenance or replacement). Finally, these benefits can be evaluated afterwards in a risk-based AM planning stage, so that asset managers can justify investments on necessary technical improvements of condition monitoring systems.Electrical Sustainable EnergyElectrical Engineering, Mathematics and Computer Scienc