Multi-Level Reliability Allocation of Phasor Measurement Unit Using Bayesian Networks. Issue 16 (21st October 2018)

• Record Type: Journal Article
• Authors: Ghosh, Soumita; Ghosh, Debomita; Kumar Mohanta, Dusmanta
• Is Part Of: Electric power components and systems. Volume 46:Issue 16/17(2018)
• Page Start: 1756
• Page End: 1768
• Publication Date: 2018-10-21
• Subjects: phasor measurement units (PMU) -- Bayesian networks (BN) -- multi-level system -- reliability allocation -- sensitivity analysis; Electric machinery -- Periodicals; 621.3104205
• DOI: 10.1080/15325008.2018.1528312
• Languages: English
• ISSNs: 1532-5008

Abstract

Abstract: The phasor measurement unit (PMU) provides synchrophasors at different locations in power grids. Therefore, its breakdown adversely affects grid monitoring and operation. In existing literature, PMU reliability has been performed using methods like Markov model, event trees, and reliability graphs. Although these methods have explored different aspects of component reliability and their combinations for overall PMU reliability evaluation, this paper puts forth additional information related to contribution of sub-modules and components toward overall PMU reliability through Bayesian networks (BNs). Moreover, the proposed methodology has ability to assess PMU status based on state of its components. Akin to a multi-level system, PMU consists of sub-modules and components. Hence, reliability improvement of PMU through reliability allocation to its sub-modules and components has been adopted. A framework has been developed to merge the information from different levels for modeling components, sub-modules, and PMU as a whole. This method also enables us to compute individual component sensitivity to failures. The efficacy of improved PMU reliability is validated based on significant improvement of observability reliability (OR), loss of data expectation (LODE), and loss of situational awareness (LOSA) has also been reported. Also, additionally a pragmatic and cost-effective design has been investigated so as to improve overall PMU reliability.