Methods to Estimate the Likelihood of Failure of Individual Asset in a Power System Business

An asset-intensive business’s success relies on the organization’s ability to manage risks. One of the critical risks is related to the in-service asset’s failure. Many organizations use asset health indices (AHI) or scoring and criticality matrices to estimate asset risks. The model selection depends on many factors, including the business requirements, cost and benefit consideration, the criticality of assets, people’s capability, and data availability. This paper provides different methods for estimating the likelihood of asset failure in a power system business consisting of generation, transmission, and distribution assets. The first is the deterministic method by developing an AHI model involving Failure Mode Effect Analysis (FMEA) and Failure Mode Effect and Criticality Analysis (FMECA). The output of AHI is a score representing the likelihood of a failure mode occurring. The second is the statistical lifetime analysis, which involves processing an asset population’s historical failure and survival data. The method assumes that the behavior of the asset population represents the individuals in it. The third is the simplest, i.e., by justifying the asset service time with a reference, such as a manufacturer’s technical lifetime guarantee. The paper also provides examples of implementing each method in a power system case study owned by PLN Indonesia.