• A unified and normalized Component Rigid Reliability Factor (CRRF) is proposed to quantify maintenance side and failure mode rigidity of components across various AIS/GIS substation configurations to overcome the interpretability limitations of traditional importance measures. • A comprehensive sensitivity analysis including failure rate variation, influence of the base line rigidity factor and Monte Carlo verification are performed to accurately assess the component failure behaviour. • A CRRF driven reinforcement framework is formulated and applied to the IEEE Reliability Test System, integrating ageing adjusted failure rates and load dependent impact through a risk index. Traditional importance measures such as risk reduction worth (RRW), risk achievement worth (RAW), Fussell-Vesley (FV), Birnbaum measure (BM) are generally utilized for reliability assessment of electrical substations. However, these measures often portray numerically inconsistent i.e. poor discrimination among component reliability factors in air insulated (AIS)/ gas insulated (GIS) switchgear substations. Thus, an unified component rigid reliability factor (CRRF) with fusion of RRW, RAW, FV, BM based reliability assessment is proposed in this paper to characterize both maintenance side rigidity and failure mode rigidity of substation components. The efficacy of proposed CRRF is validated on four various substation configurations. Sensitivity analysis is also performed to prove that CRRF preserves component criticality pattern with good discrimination among other substation components. To demonstrate scalability, CRRF concept is embedded into IEEE RTS 24 bus system by adopting failure rates ageing and load dependence. Under an identical upgrade financial budget, the simulation results reveal that CRRF significantly reduces expected energy not supplied to 31% over RRW based prioritization. Therefore, the proposed CRRF offers a physically interpretable and operationally robust framework for reliability reinforcement of AIS/GIS substations in smart electric grids.
Varma et al. (Sun,) studied this question.