Analysis Of Outages Research Articles

Rigorous analysis of back-flashover outages caused by direct lightning strokes to overhead power lines

In estimating the lightning-caused outage rate, insulator voltage is at present computed by neglecting the induction effects of the electromagnetic fields of the lightning channel and the magnetic fields of the tower travelling current waves. An analytical method is proposed to take these effects into account. The induction effect of the lightning channel significantly increases the insulator voltage, whereas that of the tower travelling current waves decreases the insulator voltage. However, the overall effect is to increase the insulator-string voltage, giving an outage rate higher than that by currently used methods. The proposed method of estimating the outage rate by backflash should provide a better tool for the design of the lightning performance of overhead power lines.

Response bias in a conjoint analysis of power outages

A methodology is proposed for estimating `status quo effects' (resistance to change) and `loss aversion' (willingness to pay is less than willingness to accept) from consumer survey data. Conjoint data on Israeli households' attitudes to electricity outages reveal that both of these effects are pronounced and that they depend on the interviewees' characteristics. Ordered conditional logit estimation indicates that while the estimates are rank sensitive the suggested methodology continues to be applicable. Finally, the conjoint estimates are compared with those obtained by applying the method of contingent valuation.

Empirical Evidence of Reliability Growth InLarge-Scale Networks

An analysis of major telecommunications outages experienced by a nation-wide network is presented. The purpose of this analysis is to examine the utility of Nonhomogeneous Poisson Process (NHPP) models in characterizing large-scale network failure behavior. The analysis not only shows the suitability of this theory, but also demonstrates the reliability growth of these network services for the time period studied. Modeling network failures as a NHPP also allows the decomposition of the failure intensity into individual hazards, providing insights into failure causes. In addition, network reliability can be characterized in classical probabilistic terms. The usefulness and limitations of this technique are discussed.