Accounting for component condition and preventive retirement in power system reliability of supply analyses

Abstract

Deteriorated power system components have a higher probability of failure than new components. Still, reliability of supply analyses traditionally model all components of the same type with the same probability of failure, and thus neglects the effect of deteriorated components. This paper presents a methodology to integrate a condition dependent component probability of failure model into a power system reliability analysis. The component state is described by a semi-Markov process, and the paper shows how this, under reasonable assumptions, can be approximated by a Markov process. The Markov assumption simplifies the analysis and allows the model to include preventive retirement and be calibrated to statistical data. A case study using statistical data for Norwegian power transformers shows that, in the Norwegian power system, the proportion of failures that are due to poor condition is small, partly due to the common strategy of preventive retirement. However, if the condition of the transformers were worse, the impact of poor condition can be considerable. The methodology further enables identification of the transformers that contribute most to the risk to reliability of supply. The paper thus highlights the importance of accounting for component condition in strategic decisions such as long-term renewal planning.<br>

Integrating component condition in long-term power system reliability analysis

“…However, a drawback with the previously developed analytical approach is that it is applicable for an analysis horizon of around just one year into the future. This paper extends the work by Toftaker et al [11] to a longer time horizon by modelling how technical condition develops over time, both due to deterioration and maintenance or replacement. The extension is important to make the methodology applicable to decision making, for instance by assessing the long-term benefit of replacing an old transformer with a new one.…”

“…Methods for power system reliability analysis can be broadly divided into two groups: i) analytical methods and ii) Monte Carlo simulation methods [8,1,2]. In this paper we both propose an extension of the analytical method presented in Toftaker et al [11] and validate it using a MCS method. Therefore this section first introduces the theoretical background necessary for both approaches to evaluating the reliability of a power system, before specifying the analytical method in Section 2.1 and the specific MCS method used in Section 2.2.…”

“…The type of analytical, contingency enumeration method that we consider here is the minimal cut set method [1], where contributions to reliability of supply indices are calculated for each contingency j that corresponds to a minimal cut set for the power supply to delivery point k. More specifically, as Toftaker et al [11] we employ an implementation of the OPAL methodology [7,6]. OPAL extends the standard minimal cut set methodology by, among other things, accounting for multiple operating states.…”

“…The overall aim of this work is to account for the technical condition of power system components (such as transformers) in long-term power system reliability analyses to better inform power system development and asset management decisions. The work presented in this paper builds upon previous research integrating a transformer health model with an existing, analytical power system reliability analysis [11]. In this modelling framework, condition is modelled by a health index, and probability of failure is given by a lifetime distribution evaluated at the corresponding apparent age.…”

“…To validate the extended analytical methodology proposed in this paper, a Monte Carlo simulation (MCS) approach is employed, and Section 2 therefore also introduces the fundamentals of MCS approaches to power system reliability analysis. Section 3 first summarizes the component reliability model of Toftaker et al [11] before presenting the proposed extensions to longer time horizons. In Section 4, the analytical and MCS-based approach to accounting for time dependencies in component condition are applied to a case study.…”