Composite System Reliability Analysis Research Articles

Analyzing uncertainties in model response using the point estimate method: Applications from railway asset management

Predicting current and future states of rail infrastructure based on existing data and measurements is essential for optimal maintenance and operation of railway systems. Mathematical models are helpful tools for detecting failures and extrapolating current states into the future. This, however, inherently gives rise to uncertainties in the model response that must be analyzed carefully to avoid misleading results and conclusions. Commonly, Monte Carlo simulations are used for such analyses which often require a large number of sample points to be evaluated for convergence. Moreover, even if quite close to the exact distributions, the Monte Carlo approach necessarily provides approximate results only. In contrast to that, the present contribution reviews an alternative way of computing important statistical quantities of the model response. The so-called point estimate method, which can be shown to be exact under certain constraints, usually (i.e. depending on the number of input variables) works with only a few specific sample points. Thus, the point estimate method helps to reduce the computational load for model evaluation considerably in the case of complex models or large-scale applications. To demonstrate the point estimate method, five academic but typical examples of railway asset management are analyzed in more detail: (a) track degradation, (b) reliability analysis of composite systems, (c) terminal reliability in rail networks, (d) failure detection/identification using decision trees, and (e) track condition modeling incorporating maintenance. Advantages as well as limitations of the point estimate method in comparison with common Monte Carlo simulations are discussed.

Quantification of Storage Necessary to Firm Up Wind Generation

This paper proposes a method to quantitatively determine the sizes of energy storage systems that are intended to mitigate negative impacts of integrating wind energy into power systems. Although the integration of wind power has several advantages, it poses several technical challenges such as variability and uncertainty of wind speed and failures of wind turbine generators (WTGs), which may deteriorate the reliability of power systems. One of the most practical solutions to mitigate these drawbacks is the use of energy storage systems. The method proposed in this paper determines the sizes of the energy storage systems considering the effect of wind power uncertainty and variability, failures of WTGs, wind speed temporal resolution, and correlation with system load. Sizes of energy storage systems are determined based on composite system reliability analysis under operational and technical constraints using the ac power flow model. Monte Carlo simulation is used to emulate the behavior of the system. The proposed method is demonstrated on the IEEE reliability test system and the results are provided. The results show that the size of an energy storage system is dependent on wind farm characteristics, as well as the connectivity with the rest of the system.

Reliability Modeling of Dynamic Thermal Rating

In this paper, a Markov model for reliability analysis of transmission lines equipped with a dynamic thermal rating (DTR) system is proposed. Moreover, a fuzzy procedure is proposed to determine an annual equivalent fuzzy DTR number to represent hourly variations of the line DTR. To implement the fuzzy annual DTR in composite system reliability analysis, an interactive method resolution technique for solving fuzzy optimization problems is developed. This method is chosen to address the fuzzy constraints associated with the lines equipped with the DTR system. Numerical results are presented using the 24-bus IEEE RTS, and demonstrate the validity of the proposed approaches.

Integration of large-scale wind farm projects including system reliability analysis

This study intends to develop a comprehensive procedure for evaluating locational value of a wind farm project incorporating reduction in transmission system losses, load delivery point interruption cost and operating cost of generating units. The energy extracted from the wind farm in normal operation condition is considered to replace the energy from fossil-fueled conventional units. In addition, composite system reliability analysis in the presence of wind power is carried out to evaluate total costs associated with curtailed energy at different load points as well as generation of generating units in contingency conditions. System reliability analysis related to large-scale wind farms, along with operating cost and transmission losses analysis, can assist policy makers to prioritise wind farm projects based on the total benefits of wind power including reliability benefits and savings in fossil-fueled energy sources.

Incorporating station-related outages in composite system reliability analysis

A technique to incorporate station-related outages in composite system reliability analysis is presented. The technique is used to evaluate composite system performance as a function of station configuration. The basic technique used to evaluate station-related outages is explained in detail using a ring bus configuration. A small composite test system is used to examine the impact of ring bus, double-bus double-breaker, one and one-half breaker and one and one-third breaker configurations.