Search

System reliability models typically use average equipment failure rates. Even if these models are calibrated based on historical reliability indices, all-like components within a calibrated region remain homogeneous. This paper presents a new method of customizing failure rates using equipment inspection data. This allows available inspection information to be reflected in system models, and allows for calibration based on interruption distributions rather than mean values. The paper begins by presenting a method to map equipment inspection data to a normalized condition score, and suggests a formula to convert this score into failure probability. The paper concludes by applying this methodology to a test system based on an actual distribution system, and shows that the incorporation of condition data leads to richer reliability models. Index Terms--Equipment failure rate modeling, inspection-based ranking, predictive reliability assessment.

Modern computerized distribution load forecasting methods, although accurate when applied to urban areas, give somewhat less satisfactory results when forecasting load growth in sparsely populated rural areas. This paper examines the differences between rural and urban load growth histories, identifying a major difference in the observed behavior of load growth. This difference is exploited in a new simulation forecasting algorithm. Tests show the new method is as accurate in forecasting rural load growth and as useful for analyzing DSM impacts than past methods, while requiring considerably lower computer resources and data than other simulation methods of comparable accuracy.