Integrating a Novel Particle Filtering and Model Predictive Health Management for Optimising Power Transformers Lifespan

ABSTRACT Power transformers are vital components in electric grids; however, methods to optimise their loading to extend lifespan while accounting for insulation degradation remain underdeveloped. This research paper introduces a novel integrated data‐driven framework that combines particle filtering and model predictive health (PF‐MPH) model for the predictive health management of power transformers. Initially, the particle filter probabilistically estimates power transformers' remaining life ( R L ) using direct winding hotspot temperatureχ H${\chi }_}$ measurements. The obtained R L will then be used to calculate the degree of polymerisation (DP) level and assess the current insulation condition. After that, a comparative analysis between direct and model‐basedχ H${\chi }_}$ measurement methods is performed to highlight the superior accuracy of direct measurements for predictive health management. Then, the MPH optimisation algorithm, which uses the R L and DP forecasts from the PF method, derives an optimal trajectory over the transformer's R L that balances the costs of increased loading against the benefits gained from prolonged insulation longevity. The findings show that the proposed PF‐MPH model has successfully reduced theχ H${\chi }_}$ by 2.46% over the predicted 19 years. This approach is expected to enable grid operators to optimise transformer loading schedules to extend the R L of these critical assets in a cost‐effective manner.