## 6 Conclusions  
This study introduced a novel methodology for assessing the technical condition of HV switchyard devices--specifically, CBs, Ds, and ITs--in HV substations. The existing models in the literature for determining the technical condition or health index often do not account for the direct outcomes of condition-based maintenance processes, and their application to large populations of devices is typically difficult to automate. To address these limitations, this study proposed an enhanced model that integrates both multiplicative and additive criteria for calculating a device's technical condition. The multiplicative criteria reflect the outcomes of maintenance activities on HV switchyard devices directly, using the outcomes of maintenance processes in the form of detected deficiencies and internal defects (DIDs) as the primary input. A comprehensive failure mode analysis was developed for CBs, Ds, and ITs, allowing each DID to be assigned a corresponding RDID severity level. The additive criteria, which form the second core component of the model, are designed to account for operational history, previous maintenance actions, and external influences over the device's service life. Together, these criteria yield a scalar index that quantifies the overall technical condition of each device. The proposed methodology is supported by an information system that consolidates all the relevant maintenance, operational, and environmental data into a unified database. A generalized data model was developed to enable the extraction and transformation of these data into partial estimates of the additive and multiplicative criteria. This allows the assessment of the technical condition throughout the entire operational life of a device, and it enables the generation of continuous condition curves (\(c\)-curves). The multiplicative criteria respond to recent maintenance actions by tracking DID levels, while the additive criteria capture longer-term degradation due to usage and external stressors between the maintenance intervals. The multiplicative model serves as the foundational tool for developing new platforms for maintenance task planning, including the adjustment of intervals between the selected tasks. Maintenance planning managers can perform ad hoc analyses using subsets of c-curves for specific device types, filtered by various attributes, such as the location, voltage level, manufacturer, maintenance task, or combinations thereof. Figure 9: (a) Longitudinal technical state plot of all the combined instrumental transformers assessed using multiplicative criteria; (b) longitudinal technical state plot of a specific combined instrumental transformer assessed using additive criteria. The model was validated using a dataset representing a large population of CBs, Ds, and ITs. The proposed solution was implemented in TSO ELES as Technology Readiness Level 6 (full prototype implementation in the relevant environment). For the system to actually be used in TSO processes, changing the internal guidelines for maintenance planning for CBs, Ds, and ITs needs to be finalized. A comparative analysis between the newly proposed model (which includes both multiplicative and additive criteria) and a conventional additive-criteria-only model demonstrated the superiority of the new approach. The results of the performed comparative analysis clearly show that the new multiplicative criteria-enhanced model reflects real-time maintenance impacts better. Specifically, it demonstrates condition degradation corresponding to the severity of the detected DIDs and improvements following remediation. In contrast, the existing additive-criteria-only model, which is more aligned with traditional health index approaches in the literature, does not capture the effects of recent maintenance activities adequately, resulting in less responsive and less interpretable \(c\)-curves. This research study lays the foundation for the advanced asset management of HV switchyard devices by combining failure mode analysis with dynamic health assessment.