Component outage data analysis methods. Volume 1: Overview and special applications

Methods for improving the quality of outage statistics for power equipment are addressed. Statistical techniques were evaluated and applied to utility outage data to illustrate their use. Fitting distributions is discussed for interval estimation and verifying models of equipment failure and repair. Precision is gained by pooling data across similar experience/equipment categories; methods are demonstrated for testing data samples to be pooled. Similarly, use of regression analysis to uncover relationships and integrate information from dissimilar samples is illustrated. Errors in modeling equipment performance, e.g., neglecting dependences, are discussed and model verification by simulation is shown to be a powerful tool. Emphasis is given to infrequent events and small data samples; Bayesian analysis and model simplification (reducing the number of parameters estimated) are examined as remedies to data sparsity.